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| Author | SHA1 | Date | |
|---|---|---|---|
 thomwolf
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thomwolf
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thomwolf
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9d56c4abe2 | ||
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thomwolf
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466ed99196 | ||
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thomwolf
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ba910fd8a0 | ||
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thomwolf
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thomwolf
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3eb54d6ded |
@@ -1,319 +0,0 @@
|
||||
version: 2.1
|
||||
orbs:
|
||||
gcp-gke: circleci/gcp-gke@1.0.4
|
||||
go: circleci/go@1.3.0
|
||||
|
||||
# TPU REFERENCES
|
||||
references:
|
||||
checkout_ml_testing: &checkout_ml_testing
|
||||
run:
|
||||
name: Checkout ml-testing-accelerators
|
||||
command: |
|
||||
git clone https://github.com/GoogleCloudPlatform/ml-testing-accelerators.git
|
||||
cd ml-testing-accelerators
|
||||
git fetch origin 5e88ac24f631c27045e62f0e8d5dfcf34e425e25:stable
|
||||
git checkout stable
|
||||
build_push_docker: &build_push_docker
|
||||
run:
|
||||
name: Configure Docker
|
||||
command: |
|
||||
gcloud --quiet auth configure-docker
|
||||
cd docker/transformers-pytorch-tpu
|
||||
if [ -z "$CIRCLE_PR_NUMBER" ]; then docker build --tag "$GCR_IMAGE_PATH:$CIRCLE_WORKFLOW_JOB_ID" -f Dockerfile --build-arg "TEST_IMAGE=1" . ; else docker build --tag "$GCR_IMAGE_PATH:$CIRCLE_WORKFLOW_JOB_ID" -f Dockerfile --build-arg "TEST_IMAGE=1" --build-arg "GITHUB_REF=pull/$CIRCLE_PR_NUMBER/head" . ; fi
|
||||
docker push "$GCR_IMAGE_PATH:$CIRCLE_WORKFLOW_JOB_ID"
|
||||
deploy_cluster: &deploy_cluster
|
||||
run:
|
||||
name: Deploy the job on the kubernetes cluster
|
||||
command: |
|
||||
go get github.com/google/go-jsonnet/cmd/jsonnet && \
|
||||
export PATH=$PATH:$HOME/go/bin && \
|
||||
kubectl create -f docker/transformers-pytorch-tpu/dataset.yaml || true && \
|
||||
job_name=$(jsonnet -J ml-testing-accelerators/ docker/transformers-pytorch-tpu/bert-base-cased.jsonnet --ext-str image=$GCR_IMAGE_PATH --ext-str image-tag=$CIRCLE_WORKFLOW_JOB_ID | kubectl create -f -) && \
|
||||
job_name=${job_name#job.batch/} && \
|
||||
job_name=${job_name% created} && \
|
||||
echo "Waiting on kubernetes job: $job_name" && \
|
||||
i=0 && \
|
||||
# 30 checks spaced 30s apart = 900s total.
|
||||
max_checks=30 && \
|
||||
status_code=2 && \
|
||||
# Check on the job periodically. Set the status code depending on what
|
||||
# happened to the job in Kubernetes. If we try max_checks times and
|
||||
# still the job hasn't finished, give up and return the starting
|
||||
# non-zero status code.
|
||||
while [ $i -lt $max_checks ]; do ((i++)); if kubectl get jobs $job_name -o jsonpath='Failed:{.status.failed}' | grep "Failed:1"; then status_code=1 && break; elif kubectl get jobs $job_name -o jsonpath='Succeeded:{.status.succeeded}' | grep "Succeeded:1" ; then status_code=0 && break; else echo "Job not finished yet"; fi; sleep 30; done && \
|
||||
echo "Done waiting. Job status code: $status_code" && \
|
||||
pod_name=$(kubectl get po -l controller-uid=`kubectl get job $job_name -o "jsonpath={.metadata.labels.controller-uid}"` | awk 'match($0,!/NAME/) {print $1}') && \
|
||||
echo "GKE pod name: $pod_name" && \
|
||||
kubectl logs -f $pod_name --container=train
|
||||
echo "Done with log retrieval attempt." && \
|
||||
gcloud container images delete "$GCR_IMAGE_PATH:$CIRCLE_WORKFLOW_JOB_ID" --force-delete-tags && \
|
||||
exit $status_code
|
||||
delete_gke_jobs: &delete_gke_jobs
|
||||
run:
|
||||
name: Delete GKE Jobs
|
||||
command: |
|
||||
# Match jobs whose age matches patterns like '1h' or '1d', i.e. any job
|
||||
# that has been around longer than 1hr. First print all columns for
|
||||
# matches, then execute the delete.
|
||||
kubectl get job | awk 'match($4,/[0-9]+[dh]/) {print $0}'
|
||||
kubectl delete job $(kubectl get job | awk 'match($4,/[0-9]+[dh]/) {print $1}')
|
||||
|
||||
|
||||
|
||||
|
||||
jobs:
|
||||
run_tests_torch_and_tf:
|
||||
working_directory: ~/transformers
|
||||
docker:
|
||||
- image: circleci/python:3.6
|
||||
environment:
|
||||
OMP_NUM_THREADS: 1
|
||||
resource_class: xlarge
|
||||
parallelism: 1
|
||||
steps:
|
||||
- checkout
|
||||
- restore_cache:
|
||||
keys:
|
||||
- v0.3-torch_and_tf-{{ checksum "setup.py" }}
|
||||
- v0.3-{{ checksum "setup.py" }}
|
||||
- run: pip install --upgrade pip
|
||||
- run: pip install git+https://github.com/huggingface/datasets
|
||||
- run: pip install .[sklearn,tf-cpu,torch,testing]
|
||||
- run: pip install codecov pytest-cov
|
||||
- save_cache:
|
||||
key: v0.3-{{ checksum "setup.py" }}
|
||||
paths:
|
||||
- '~/.cache/pip'
|
||||
- run: python -m pytest -n 8 --dist=loadfile -rA -s ./tests/ --cov | tee output.txt
|
||||
- run: codecov
|
||||
- store_artifacts:
|
||||
path: ~/transformers/output.txt
|
||||
destination: test_output.txt
|
||||
run_tests_torch:
|
||||
working_directory: ~/transformers
|
||||
docker:
|
||||
- image: circleci/python:3.7
|
||||
environment:
|
||||
OMP_NUM_THREADS: 1
|
||||
resource_class: xlarge
|
||||
parallelism: 1
|
||||
steps:
|
||||
- checkout
|
||||
- restore_cache:
|
||||
keys:
|
||||
- v0.3-torch-{{ checksum "setup.py" }}
|
||||
- v0.3-{{ checksum "setup.py" }}
|
||||
- run: pip install --upgrade pip
|
||||
- run: pip install git+https://github.com/huggingface/datasets
|
||||
- run: pip install .[sklearn,torch,testing]
|
||||
- save_cache:
|
||||
key: v0.3-torch-{{ checksum "setup.py" }}
|
||||
paths:
|
||||
- '~/.cache/pip'
|
||||
- run: python -m pytest -n 8 --dist=loadfile -rA -s ./tests/ | tee output.txt
|
||||
- store_artifacts:
|
||||
path: ~/transformers/output.txt
|
||||
destination: test_output.txt
|
||||
run_tests_tf:
|
||||
working_directory: ~/transformers
|
||||
docker:
|
||||
- image: circleci/python:3.7
|
||||
environment:
|
||||
OMP_NUM_THREADS: 1
|
||||
resource_class: xlarge
|
||||
parallelism: 1
|
||||
steps:
|
||||
- checkout
|
||||
- restore_cache:
|
||||
keys:
|
||||
- v0.3-tf-{{ checksum "setup.py" }}
|
||||
- v0.3-{{ checksum "setup.py" }}
|
||||
- run: pip install --upgrade pip
|
||||
- run: pip install git+https://github.com/huggingface/datasets
|
||||
- run: pip install .[sklearn,tf-cpu,testing]
|
||||
- save_cache:
|
||||
key: v0.3-tf-{{ checksum "setup.py" }}
|
||||
paths:
|
||||
- '~/.cache/pip'
|
||||
- run: python -m pytest -n 8 --dist=loadfile -rA -s ./tests/ | tee output.txt
|
||||
- store_artifacts:
|
||||
path: ~/transformers/output.txt
|
||||
destination: test_output.txt
|
||||
run_tests_custom_tokenizers:
|
||||
working_directory: ~/transformers
|
||||
docker:
|
||||
- image: circleci/python:3.6
|
||||
environment:
|
||||
RUN_CUSTOM_TOKENIZERS: yes
|
||||
steps:
|
||||
- checkout
|
||||
- restore_cache:
|
||||
keys:
|
||||
- v0.3-custom_tokenizers-{{ checksum "setup.py" }}
|
||||
- v0.3-{{ checksum "setup.py" }}
|
||||
- run: pip install --upgrade pip
|
||||
- run: pip install .[ja,testing]
|
||||
- run: python -m unidic download
|
||||
- save_cache:
|
||||
key: v0.3-custom_tokenizers-{{ checksum "setup.py" }}
|
||||
paths:
|
||||
- '~/.cache/pip'
|
||||
- run: python -m pytest -s ./tests/test_tokenization_bert_japanese.py | tee output.txt
|
||||
- store_artifacts:
|
||||
path: ~/transformers/output.txt
|
||||
destination: test_output.txt
|
||||
run_examples_torch:
|
||||
working_directory: ~/transformers
|
||||
docker:
|
||||
- image: circleci/python:3.6
|
||||
environment:
|
||||
OMP_NUM_THREADS: 1
|
||||
resource_class: xlarge
|
||||
parallelism: 1
|
||||
steps:
|
||||
- checkout
|
||||
- restore_cache:
|
||||
keys:
|
||||
- v0.3-torch_examples-{{ checksum "setup.py" }}
|
||||
- v0.3-{{ checksum "setup.py" }}
|
||||
- run: pip install --upgrade pip
|
||||
- run: pip install .[sklearn,torch,testing]
|
||||
- run: pip install -r examples/requirements.txt
|
||||
- save_cache:
|
||||
key: v0.3-torch_examples-{{ checksum "setup.py" }}
|
||||
paths:
|
||||
- '~/.cache/pip'
|
||||
- run: python -m pytest -n 8 --dist=loadfile -rA -s ./examples/ | tee output.txt
|
||||
- store_artifacts:
|
||||
path: ~/transformers/output.txt
|
||||
destination: test_output.txt
|
||||
build_doc:
|
||||
working_directory: ~/transformers
|
||||
docker:
|
||||
- image: circleci/python:3.6
|
||||
steps:
|
||||
- checkout
|
||||
- restore_cache:
|
||||
keys:
|
||||
- v0.3-build_doc-{{ checksum "setup.py" }}
|
||||
- v0.3-{{ checksum "setup.py" }}
|
||||
- run: pip install --upgrade pip
|
||||
- run: pip install .[tf,torch,docs]
|
||||
- save_cache:
|
||||
key: v0.3-build_doc-{{ checksum "setup.py" }}
|
||||
paths:
|
||||
- '~/.cache/pip'
|
||||
- run: cd docs && make html SPHINXOPTS="-W"
|
||||
- store_artifacts:
|
||||
path: ./docs/_build
|
||||
deploy_doc:
|
||||
working_directory: ~/transformers
|
||||
docker:
|
||||
- image: circleci/python:3.6
|
||||
steps:
|
||||
- add_ssh_keys:
|
||||
fingerprints:
|
||||
- "5b:7a:95:18:07:8c:aa:76:4c:60:35:88:ad:60:56:71"
|
||||
- checkout
|
||||
- restore_cache:
|
||||
keys:
|
||||
- v0.3-deploy_doc-{{ checksum "setup.py" }}
|
||||
- v0.3-{{ checksum "setup.py" }}
|
||||
- run: pip install .[tf,torch,docs]
|
||||
- save_cache:
|
||||
key: v0.3-deploy_doc-{{ checksum "setup.py" }}
|
||||
paths:
|
||||
- '~/.cache/pip'
|
||||
- run: ./.circleci/deploy.sh
|
||||
check_code_quality:
|
||||
working_directory: ~/transformers
|
||||
docker:
|
||||
- image: circleci/python:3.6
|
||||
resource_class: medium
|
||||
parallelism: 1
|
||||
steps:
|
||||
- checkout
|
||||
- restore_cache:
|
||||
keys:
|
||||
- v0.3-code_quality-{{ checksum "setup.py" }}
|
||||
- v0.3-{{ checksum "setup.py" }}
|
||||
- run: pip install --upgrade pip
|
||||
- run: pip install isort
|
||||
- run: pip install .[tf,torch,quality]
|
||||
- save_cache:
|
||||
key: v0.3-code_quality-{{ checksum "setup.py" }}
|
||||
paths:
|
||||
- '~/.cache/pip'
|
||||
- run: black --check --line-length 119 --target-version py35 examples templates tests src utils
|
||||
- run: isort --check-only --recursive examples templates tests src utils
|
||||
- run: flake8 examples templates tests src utils
|
||||
- run: python utils/check_repo.py
|
||||
check_repository_consistency:
|
||||
working_directory: ~/transformers
|
||||
docker:
|
||||
- image: circleci/python:3.6
|
||||
resource_class: small
|
||||
parallelism: 1
|
||||
steps:
|
||||
- checkout
|
||||
- run: pip install requests
|
||||
- run: python ./utils/link_tester.py
|
||||
|
||||
# TPU JOBS
|
||||
run_examples_tpu:
|
||||
docker:
|
||||
- image: circleci/python:3.6
|
||||
environment:
|
||||
OMP_NUM_THREADS: 1
|
||||
resource_class: xlarge
|
||||
parallelism: 1
|
||||
steps:
|
||||
- checkout
|
||||
- go/install
|
||||
- *checkout_ml_testing
|
||||
- gcp-gke/install
|
||||
- gcp-gke/update-kubeconfig-with-credentials:
|
||||
cluster: $GKE_CLUSTER
|
||||
perform-login: true
|
||||
- setup_remote_docker
|
||||
- *build_push_docker
|
||||
- *deploy_cluster
|
||||
cleanup-gke-jobs:
|
||||
docker:
|
||||
- image: circleci/python:3.6
|
||||
steps:
|
||||
- gcp-gke/install
|
||||
- gcp-gke/update-kubeconfig-with-credentials:
|
||||
cluster: $GKE_CLUSTER
|
||||
perform-login: true
|
||||
- *delete_gke_jobs
|
||||
workflow_filters: &workflow_filters
|
||||
filters:
|
||||
branches:
|
||||
only:
|
||||
- master
|
||||
workflows:
|
||||
version: 2
|
||||
build_and_test:
|
||||
jobs:
|
||||
- check_code_quality
|
||||
- check_repository_consistency
|
||||
- run_examples_torch
|
||||
- run_tests_custom_tokenizers
|
||||
- run_tests_torch_and_tf
|
||||
- run_tests_torch
|
||||
- run_tests_tf
|
||||
- build_doc
|
||||
- deploy_doc: *workflow_filters
|
||||
tpu_testing_jobs:
|
||||
triggers:
|
||||
- schedule:
|
||||
# Set to run at the first minute of every hour.
|
||||
cron: "0 8 * * *"
|
||||
filters:
|
||||
branches:
|
||||
only:
|
||||
- master
|
||||
jobs:
|
||||
- cleanup-gke-jobs
|
||||
- run_examples_tpu
|
||||
@@ -1,51 +0,0 @@
|
||||
cd docs
|
||||
|
||||
function deploy_doc(){
|
||||
echo "Creating doc at commit $1 and pushing to folder $2"
|
||||
git checkout $1
|
||||
if [ ! -z "$2" ]
|
||||
then
|
||||
if [ "$2" == "master" ]; then
|
||||
echo "Pushing master"
|
||||
make clean && make html && scp -r -oStrictHostKeyChecking=no _build/html/* $doc:$dir/$2/
|
||||
cp -r _build/html/_static .
|
||||
elif ssh -oStrictHostKeyChecking=no $doc "[ -d $dir/$2 ]"; then
|
||||
echo "Directory" $2 "already exists"
|
||||
scp -r -oStrictHostKeyChecking=no _static/* $doc:$dir/$2/_static/
|
||||
else
|
||||
echo "Pushing version" $2
|
||||
make clean && make html
|
||||
rm -rf _build/html/_static
|
||||
cp -r _static _build/html
|
||||
scp -r -oStrictHostKeyChecking=no _build/html $doc:$dir/$2
|
||||
fi
|
||||
else
|
||||
echo "Pushing stable"
|
||||
make clean && make html
|
||||
rm -rf _build/html/_static
|
||||
cp -r _static _build/html
|
||||
scp -r -oStrictHostKeyChecking=no _build/html/* $doc:$dir
|
||||
fi
|
||||
}
|
||||
|
||||
# You can find the commit for each tag on https://github.com/huggingface/transformers/tags
|
||||
deploy_doc "master" master
|
||||
deploy_doc "b33a385" v1.0.0
|
||||
deploy_doc "fe02e45" v1.1.0
|
||||
deploy_doc "89fd345" v1.2.0
|
||||
deploy_doc "fc9faa8" v2.0.0
|
||||
deploy_doc "3ddce1d" v2.1.1
|
||||
deploy_doc "3616209" v2.2.0
|
||||
deploy_doc "d0f8b9a" v2.3.0
|
||||
deploy_doc "6664ea9" v2.4.0
|
||||
deploy_doc "fb560dc" v2.5.0
|
||||
deploy_doc "b90745c" v2.5.1
|
||||
deploy_doc "fbc5bf1" v2.6.0
|
||||
deploy_doc "6f5a12a" v2.7.0
|
||||
deploy_doc "11c3257" v2.8.0
|
||||
deploy_doc "e7cfc1a" v2.9.0
|
||||
deploy_doc "7cb203f" v2.9.1
|
||||
deploy_doc "10d7239" v2.10.0
|
||||
deploy_doc "b42586e" v2.11.0
|
||||
deploy_doc "7fb8bdf" v3.0.2
|
||||
deploy_doc "4b3ee9c" # v3.1.0 Latest stable release
|
||||
-12
@@ -1,12 +0,0 @@
|
||||
[run]
|
||||
source=transformers
|
||||
omit =
|
||||
# skip convertion scripts from testing for now
|
||||
*/convert_*
|
||||
*/__main__.py
|
||||
[report]
|
||||
exclude_lines =
|
||||
pragma: no cover
|
||||
raise
|
||||
except
|
||||
register_parameter
|
||||
@@ -1,22 +0,0 @@
|
||||
---
|
||||
name: "\U0001F5A5 New benchmark"
|
||||
about: Benchmark a part of this library and share your results
|
||||
title: "[Benchmark]"
|
||||
labels: ''
|
||||
assignees: ''
|
||||
|
||||
---
|
||||
|
||||
# 🖥 Benchmarking `transformers`
|
||||
|
||||
## Benchmark
|
||||
|
||||
Which part of `transformers` did you benchmark?
|
||||
|
||||
## Set-up
|
||||
|
||||
What did you run your benchmarks on? Please include details, such as: CPU, GPU? If using multiple GPUs, which parallelization did you use?
|
||||
|
||||
## Results
|
||||
|
||||
Put your results here!
|
||||
@@ -1,20 +0,0 @@
|
||||
---
|
||||
name: "\U0001F31F New model addition"
|
||||
about: Submit a proposal/request to implement a new Transformer-based model
|
||||
title: ''
|
||||
labels: New model
|
||||
assignees: ''
|
||||
|
||||
---
|
||||
|
||||
# 🌟 New model addition
|
||||
|
||||
## Model description
|
||||
|
||||
<!-- Important information -->
|
||||
|
||||
## Open source status
|
||||
|
||||
* [ ] the model implementation is available: (give details)
|
||||
* [ ] the model weights are available: (give details)
|
||||
* [ ] who are the authors: (mention them, if possible by @gh-username)
|
||||
@@ -1,79 +0,0 @@
|
||||
---
|
||||
name: "\U0001F41B Bug Report"
|
||||
about: Submit a bug report to help us improve transformers
|
||||
title: ''
|
||||
labels: ''
|
||||
assignees: ''
|
||||
|
||||
---
|
||||
|
||||
|
||||
## Environment info
|
||||
<!-- You can run the command `transformers-cli env` and copy-and-paste its output below.
|
||||
Don't forget to fill out the missing fields in that output! -->
|
||||
|
||||
- `transformers` version:
|
||||
- Platform:
|
||||
- Python version:
|
||||
- PyTorch version (GPU?):
|
||||
- Tensorflow version (GPU?):
|
||||
- Using GPU in script?:
|
||||
- Using distributed or parallel set-up in script?:
|
||||
|
||||
### Who can help
|
||||
<!-- Your issue will be replied to more quickly if you can figure out the right person to tag with @
|
||||
If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
|
||||
Please tag fewer than 3 people.
|
||||
|
||||
albert, bert, GPT2, XLM: @LysandreJik
|
||||
tokenizers: @mfuntowicz
|
||||
Trainer: @sgugger
|
||||
Speed and Memory Benchmarks: @patrickvonplaten
|
||||
Model Cards: @julien-c
|
||||
Translation: @sshleifer
|
||||
Summarization: @sshleifer
|
||||
TextGeneration: @TevenLeScao
|
||||
examples/distillation: @VictorSanh
|
||||
nlp datasets: [different repo](https://github.com/huggingface/nlp)
|
||||
rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)
|
||||
Text Generation: @TevenLeScao
|
||||
blenderbot: @mariamabarham
|
||||
Bart: @sshleifer
|
||||
Marian: @sshleifer
|
||||
T5: @patrickvonplaten
|
||||
Longformer/Reformer: @patrickvonplaten
|
||||
TransfoXL/XLNet: @TevenLeScao
|
||||
examples/seq2seq: @sshleifer
|
||||
examples/bert-loses-patience: @JetRunner
|
||||
tensorflow: @jplu
|
||||
examples/token-classification: @stefan-it
|
||||
documentation: @sgugger
|
||||
-->
|
||||
|
||||
## Information
|
||||
|
||||
Model I am using (Bert, XLNet ...):
|
||||
|
||||
The problem arises when using:
|
||||
* [ ] the official example scripts: (give details below)
|
||||
* [ ] my own modified scripts: (give details below)
|
||||
|
||||
The tasks I am working on is:
|
||||
* [ ] an official GLUE/SQUaD task: (give the name)
|
||||
* [ ] my own task or dataset: (give details below)
|
||||
|
||||
## To reproduce
|
||||
|
||||
Steps to reproduce the behavior:
|
||||
|
||||
1.
|
||||
2.
|
||||
3.
|
||||
|
||||
<!-- If you have code snippets, error messages, stack traces please provide them here as well.
|
||||
Important! Use code tags to correctly format your code. See https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting
|
||||
Do not use screenshots, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.-->
|
||||
|
||||
## Expected behavior
|
||||
|
||||
<!-- A clear and concise description of what you would expect to happen. -->
|
||||
@@ -1,25 +0,0 @@
|
||||
---
|
||||
name: "\U0001F680 Feature request"
|
||||
about: Submit a proposal/request for a new transformers feature
|
||||
title: ''
|
||||
labels: ''
|
||||
assignees: ''
|
||||
|
||||
---
|
||||
|
||||
# 🚀 Feature request
|
||||
|
||||
<!-- A clear and concise description of the feature proposal.
|
||||
Please provide a link to the paper and code in case they exist. -->
|
||||
|
||||
## Motivation
|
||||
|
||||
<!-- Please outline the motivation for the proposal. Is your feature request
|
||||
related to a problem? e.g., I'm always frustrated when [...]. If this is related
|
||||
to another GitHub issue, please link here too. -->
|
||||
|
||||
## Your contribution
|
||||
|
||||
<!-- Is there any way that you could help, e.g. by submitting a PR?
|
||||
Make sure to read the CONTRIBUTING.MD readme:
|
||||
https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md -->
|
||||
@@ -1,58 +0,0 @@
|
||||
---
|
||||
name: "\U0001F4DA Migration from pytorch-pretrained-bert or pytorch-transformers"
|
||||
about: Report a problem when migrating from pytorch-pretrained-bert or pytorch-transformers
|
||||
to transformers
|
||||
title: ''
|
||||
labels: Migration
|
||||
assignees: ''
|
||||
|
||||
---
|
||||
|
||||
# 📚 Migration
|
||||
|
||||
## Information
|
||||
|
||||
<!-- Important information -->
|
||||
|
||||
Model I am using (Bert, XLNet ...):
|
||||
|
||||
Language I am using the model on (English, Chinese ...):
|
||||
|
||||
The problem arises when using:
|
||||
* [ ] the official example scripts: (give details below)
|
||||
* [ ] my own modified scripts: (give details below)
|
||||
|
||||
The tasks I am working on is:
|
||||
* [ ] an official GLUE/SQUaD task: (give the name)
|
||||
* [ ] my own task or dataset: (give details below)
|
||||
|
||||
## Details
|
||||
|
||||
<!-- A clear and concise description of the migration issue.
|
||||
If you have code snippets, please provide it here as well.
|
||||
Important! Use code tags to correctly format your code. See https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting
|
||||
Do not use screenshots, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.
|
||||
-->
|
||||
|
||||
## Environment info
|
||||
<!-- You can run the command `python transformers-cli env` and copy-and-paste its output below.
|
||||
Don't forget to fill out the missing fields in that output! -->
|
||||
|
||||
- `transformers` version:
|
||||
- Platform:
|
||||
- Python version:
|
||||
- PyTorch version (GPU?):
|
||||
- Tensorflow version (GPU?):
|
||||
- Using GPU in script?:
|
||||
- Using distributed or parallel set-up in script?:
|
||||
|
||||
<!-- IMPORTANT: which version of the former library do you use? -->
|
||||
* `pytorch-transformers` or `pytorch-pretrained-bert` version (or branch):
|
||||
|
||||
|
||||
## Checklist
|
||||
|
||||
- [ ] I have read the migration guide in the readme.
|
||||
([pytorch-transformers](https://github.com/huggingface/transformers#migrating-from-pytorch-transformers-to-transformers);
|
||||
[pytorch-pretrained-bert](https://github.com/huggingface/transformers#migrating-from-pytorch-pretrained-bert-to-transformers))
|
||||
- [ ] I checked if a related official extension example runs on my machine.
|
||||
@@ -1,27 +0,0 @@
|
||||
---
|
||||
name: "❓ Questions & Help"
|
||||
about: Post your general questions on the Hugging Face forum or Stack Overflow tagged huggingface-transformers
|
||||
title: ''
|
||||
labels: ''
|
||||
assignees: ''
|
||||
|
||||
---
|
||||
|
||||
# ❓ Questions & Help
|
||||
|
||||
<!-- The GitHub issue tracker is primarly intended for bugs, feature requests,
|
||||
new models and benchmarks, and migration questions. For all other questions,
|
||||
we direct you to the Hugging Face forum: https://discuss.huggingface.co/ .
|
||||
You can also try Stack Overflow (SO) where a whole community of PyTorch and
|
||||
Tensorflow enthusiast can help you out. In this case, make sure to tag your
|
||||
question with the right deep learning framework as well as the
|
||||
huggingface-transformers tag:
|
||||
https://stackoverflow.com/questions/tagged/huggingface-transformers
|
||||
-->
|
||||
|
||||
## Details
|
||||
<!-- Description of your issue -->
|
||||
|
||||
<!-- You should first ask your question on the forum or SO, and only if
|
||||
you didn't get an answer ask it here on GitHub. -->
|
||||
**A link to original question on the forum/Stack Overflow**:
|
||||
@@ -1,2 +0,0 @@
|
||||
<!-- This line specifies which issue to close after the pull request is merged. -->
|
||||
Fixes #{issue number}
|
||||
@@ -1,17 +0,0 @@
|
||||
# Number of days of inactivity before an issue becomes stale
|
||||
daysUntilStale: 60
|
||||
# Number of days of inactivity before a stale issue is closed
|
||||
daysUntilClose: 7
|
||||
# Issues with these labels will never be considered stale
|
||||
exemptLabels:
|
||||
- pinned
|
||||
- security
|
||||
# Label to use when marking an issue as stale
|
||||
staleLabel: wontfix
|
||||
# Comment to post when marking an issue as stale. Set to `false` to disable
|
||||
markComment: >
|
||||
This issue has been automatically marked as stale because it has not had
|
||||
recent activity. It will be closed if no further activity occurs. Thank you
|
||||
for your contributions.
|
||||
# Comment to post when closing a stale issue. Set to `false` to disable
|
||||
closeComment: false
|
||||
@@ -1,41 +0,0 @@
|
||||
name: Torch hub integration
|
||||
|
||||
on:
|
||||
push:
|
||||
branches:
|
||||
- "*"
|
||||
|
||||
jobs:
|
||||
torch_hub_integration:
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
# no checkout necessary here.
|
||||
- name: Extract branch name
|
||||
run: echo "::set-env name=BRANCH::${GITHUB_REF#refs/heads/}"
|
||||
- name: Check branch name
|
||||
run: echo $BRANCH
|
||||
- name: Set up Python
|
||||
uses: actions/setup-python@v1
|
||||
with:
|
||||
python-version: 3.7
|
||||
|
||||
- name: Loading cache
|
||||
uses: actions/cache@v2
|
||||
id: cache
|
||||
with:
|
||||
path: ~/.cache/pip
|
||||
key: v0-torch_hub-${{ hashFiles('setup.py') }}
|
||||
|
||||
- name: Install dependencies
|
||||
run: |
|
||||
pip install --upgrade pip
|
||||
pip install torch
|
||||
pip install numpy tokenizers filelock requests tqdm regex sentencepiece sacremoses packaging
|
||||
|
||||
- name: Torch hub list
|
||||
run: |
|
||||
python -c "import torch; print(torch.hub.list('huggingface/transformers:$BRANCH'))"
|
||||
|
||||
- name: Torch hub help
|
||||
run: |
|
||||
python -c "import torch; print(torch.hub.help('huggingface/transformers:$BRANCH', 'modelForSequenceClassification'))"
|
||||
@@ -1,64 +0,0 @@
|
||||
name: Self-hosted runner (push)
|
||||
|
||||
on:
|
||||
push:
|
||||
branches:
|
||||
- master
|
||||
paths:
|
||||
- "src/**"
|
||||
- "tests/**"
|
||||
- ".github/**"
|
||||
# pull_request:
|
||||
repository_dispatch:
|
||||
|
||||
|
||||
jobs:
|
||||
run_tests_torch_and_tf_gpu:
|
||||
runs-on: self-hosted
|
||||
steps:
|
||||
- uses: actions/checkout@v2
|
||||
- name: Python version
|
||||
run: |
|
||||
which python
|
||||
python --version
|
||||
pip --version
|
||||
- name: Current dir
|
||||
run: pwd
|
||||
- run: nvidia-smi
|
||||
|
||||
- name: Loading cache.
|
||||
uses: actions/cache@v2
|
||||
id: cache
|
||||
with:
|
||||
path: .env
|
||||
key: v0-tests_tf_torch_gpu-${{ hashFiles('setup.py') }}
|
||||
|
||||
- name: Create new python env (on self-hosted runners we have to handle isolation ourselves)
|
||||
run: |
|
||||
python -m venv .env
|
||||
source .env/bin/activate
|
||||
which python
|
||||
python --version
|
||||
pip --version
|
||||
- name: Install dependencies
|
||||
run: |
|
||||
source .env/bin/activate
|
||||
pip install --upgrade pip
|
||||
pip install torch!=1.6.0
|
||||
pip install .[sklearn,testing,onnxruntime]
|
||||
pip install git+https://github.com/huggingface/datasets
|
||||
|
||||
- name: Are GPUs recognized by our DL frameworks
|
||||
run: |
|
||||
source .env/bin/activate
|
||||
python -c "import torch; print(torch.cuda.is_available())"
|
||||
|
||||
- name: Run all non-slow tests on GPU
|
||||
env:
|
||||
TF_FORCE_GPU_ALLOW_GROWTH: "true"
|
||||
# TF_GPU_MEMORY_LIMIT: 4096
|
||||
OMP_NUM_THREADS: 1
|
||||
USE_CUDA: yes
|
||||
run: |
|
||||
source .env/bin/activate
|
||||
python -m pytest -n 2 --dist=loadfile -s ./tests/
|
||||
@@ -1,72 +0,0 @@
|
||||
name: Self-hosted runner (scheduled)
|
||||
|
||||
on:
|
||||
push:
|
||||
branches:
|
||||
- ci_*
|
||||
repository_dispatch:
|
||||
schedule:
|
||||
- cron: "0 0 * * *"
|
||||
|
||||
jobs:
|
||||
run_all_tests_torch_and_tf_gpu:
|
||||
runs-on: self-hosted
|
||||
steps:
|
||||
- uses: actions/checkout@v2
|
||||
|
||||
- name: Loading cache.
|
||||
uses: actions/cache@v2
|
||||
id: cache
|
||||
with:
|
||||
path: .env
|
||||
key: v0-slow_tests_tf_torch_gpu-${{ hashFiles('setup.py') }}
|
||||
|
||||
- name: Python version
|
||||
run: |
|
||||
which python
|
||||
python --version
|
||||
pip --version
|
||||
- name: Current dir
|
||||
run: pwd
|
||||
- run: nvidia-smi
|
||||
- name: Create new python env (on self-hosted runners we have to handle isolation ourselves)
|
||||
if: steps.cache.outputs.cache-hit != 'true'
|
||||
run: |
|
||||
python -m venv .env
|
||||
source .env/bin/activate
|
||||
which python
|
||||
python --version
|
||||
pip --version
|
||||
- name: Install dependencies
|
||||
run: |
|
||||
source .env/bin/activate
|
||||
pip install --upgrade pip
|
||||
pip install torch!=1.6.0
|
||||
pip install .[sklearn,testing,onnxruntime]
|
||||
pip install git+https://github.com/huggingface/datasets
|
||||
|
||||
- name: Are GPUs recognized by our DL frameworks
|
||||
run: |
|
||||
source .env/bin/activate
|
||||
python -c "import torch; print(torch.cuda.is_available())"
|
||||
|
||||
- name: Run all tests on GPU
|
||||
env:
|
||||
TF_FORCE_GPU_ALLOW_GROWTH: "true"
|
||||
OMP_NUM_THREADS: 1
|
||||
RUN_SLOW: yes
|
||||
USE_CUDA: yes
|
||||
run: |
|
||||
source .env/bin/activate
|
||||
python -m pytest -n 1 --dist=loadfile -s ./tests/
|
||||
|
||||
- name: Run examples tests on GPU
|
||||
env:
|
||||
TF_FORCE_GPU_ALLOW_GROWTH: "true"
|
||||
OMP_NUM_THREADS: 1
|
||||
RUN_SLOW: yes
|
||||
USE_CUDA: yes
|
||||
run: |
|
||||
source .env/bin/activate
|
||||
pip install -r examples/requirements.txt
|
||||
python -m pytest -n 1 --dist=loadfile -s examples
|
||||
+1
-36
@@ -8,11 +8,6 @@ __pycache__/
|
||||
# C extensions
|
||||
*.so
|
||||
|
||||
# tests and logs
|
||||
tests/fixtures
|
||||
logs/
|
||||
lightning_logs/
|
||||
|
||||
# Distribution / packaging
|
||||
.Python
|
||||
build/
|
||||
@@ -121,37 +116,7 @@ dmypy.json
|
||||
.pyre/
|
||||
|
||||
# vscode
|
||||
.vs
|
||||
.vscode
|
||||
|
||||
# Pycharm
|
||||
.idea
|
||||
|
||||
# TF code
|
||||
tensorflow_code
|
||||
|
||||
# Models
|
||||
models
|
||||
proc_data
|
||||
|
||||
# examples
|
||||
runs
|
||||
/runs_old
|
||||
/wandb
|
||||
/examples/runs
|
||||
/examples/**/*.args
|
||||
/examples/rag/sweep
|
||||
|
||||
# data
|
||||
/data
|
||||
serialization_dir
|
||||
|
||||
# emacs
|
||||
*.*~
|
||||
debug.env
|
||||
|
||||
# vim
|
||||
.*.swp
|
||||
|
||||
#ctags
|
||||
tags
|
||||
tensorflow_code
|
||||
-300
@@ -1,300 +0,0 @@
|
||||
# How to contribute to transformers?
|
||||
|
||||
Everyone is welcome to contribute, and we value everybody's contribution. Code
|
||||
is thus not the only way to help the community. Answering questions, helping
|
||||
others, reaching out and improving the documentations are immensely valuable to
|
||||
the community.
|
||||
|
||||
It also helps us if you spread the word: reference the library from blog posts
|
||||
on the awesome projects it made possible, shout out on Twitter every time it has
|
||||
helped you, or simply star the repo to say "thank you".
|
||||
|
||||
## You can contribute in so many ways!
|
||||
|
||||
There are 4 ways you can contribute to transformers:
|
||||
* Fixing outstanding issues with the existing code;
|
||||
* Implementing new models;
|
||||
* Contributing to the examples or to the documentation;
|
||||
* Submitting issues related to bugs or desired new features.
|
||||
|
||||
*All are equally valuable to the community.*
|
||||
|
||||
## Submitting a new issue or feature request
|
||||
|
||||
Do your best to follow these guidelines when submitting an issue or a feature
|
||||
request. It will make it easier for us to come back to you quickly and with good
|
||||
feedback.
|
||||
|
||||
### Did you find a bug?
|
||||
|
||||
The transformers are robust and reliable thanks to the users who notify us of
|
||||
the problems they encounter. So thank you for reporting an issue.
|
||||
|
||||
First, we would really appreciate it if you could **make sure the bug was not
|
||||
already reported** (use the search bar on Github under Issues).
|
||||
|
||||
Did not find it? :( So we can act quickly on it, please follow these steps:
|
||||
|
||||
* Include your **OS type and version**, the versions of **Python**, **PyTorch** and
|
||||
**Tensorflow** when applicable;
|
||||
* A short, self-contained, code snippet that allows us to reproduce the bug in
|
||||
less than 30s;
|
||||
* Provide the *full* traceback if an exception is raised.
|
||||
|
||||
To get the OS and software versions automatically, you can run the following command:
|
||||
|
||||
```bash
|
||||
transformers-cli env
|
||||
```
|
||||
|
||||
or from the root of the repository the following command:
|
||||
|
||||
```bash
|
||||
python src/transformers/commands/transformers_cli.py env
|
||||
```
|
||||
|
||||
|
||||
### Do you want to implement a new model?
|
||||
|
||||
Awesome! Please provide the following information:
|
||||
|
||||
* Short description of the model and link to the paper;
|
||||
* Link to the implementation if it is open-source;
|
||||
* Link to the model weights if they are available.
|
||||
|
||||
If you are willing to contribute the model yourself, let us know so we can best
|
||||
guide you.
|
||||
|
||||
We have added a **detailed guide and templates** to guide you in the process of adding a new model. You can find them
|
||||
in the [`templates`](https://github.com/huggingface/transformers/tree/master/templates) folder.
|
||||
|
||||
### Do you want a new feature (that is not a model)?
|
||||
|
||||
A world-class feature request addresses the following points:
|
||||
|
||||
1. Motivation first:
|
||||
* Is it related to a problem/frustration with the library? If so, please explain
|
||||
why. Providing a code snippet that demonstrates the problem is best.
|
||||
* Is it related to something you would need for a project? We'd love to hear
|
||||
about it!
|
||||
* Is it something you worked on and think could benefit the community?
|
||||
Awesome! Tell us what problem it solved for you.
|
||||
2. Write a *full paragraph* describing the feature;
|
||||
3. Provide a **code snippet** that demonstrates its future use;
|
||||
4. In case this is related to a paper, please attach a link;
|
||||
5. Attach any additional information (drawings, screenshots, etc.) you think may help.
|
||||
|
||||
If your issue is well written we're already 80% of the way there by the time you
|
||||
post it.
|
||||
|
||||
We have added **templates** to guide you in the process of adding a new example script for training or testing the
|
||||
models in the library. You can find them in the [`templates`](https://github.com/huggingface/transformers/tree/master/templates)
|
||||
folder.
|
||||
|
||||
## Start contributing! (Pull Requests)
|
||||
|
||||
Before writing code, we strongly advise you to search through the exising PRs or
|
||||
issues to make sure that nobody is already working on the same thing. If you are
|
||||
unsure, it is always a good idea to open an issue to get some feedback.
|
||||
|
||||
You will need basic `git` proficiency to be able to contribute to
|
||||
`transformers`. `git` is not the easiest tool to use but it has the greatest
|
||||
manual. Type `git --help` in a shell and enjoy. If you prefer books, [Pro
|
||||
Git](https://git-scm.com/book/en/v2) is a very good reference.
|
||||
|
||||
Follow these steps to start contributing:
|
||||
|
||||
1. Fork the [repository](https://github.com/huggingface/transformers) by
|
||||
clicking on the 'Fork' button on the repository's page. This creates a copy of the code
|
||||
under your GitHub user account.
|
||||
|
||||
2. Clone your fork to your local disk, and add the base repository as a remote:
|
||||
|
||||
```bash
|
||||
$ git clone git@github.com:<your Github handle>/transformers.git
|
||||
$ cd transformers
|
||||
$ git remote add upstream https://github.com/huggingface/transformers.git
|
||||
```
|
||||
|
||||
3. Create a new branch to hold your development changes:
|
||||
|
||||
```bash
|
||||
$ git checkout -b a-descriptive-name-for-my-changes
|
||||
```
|
||||
|
||||
**do not** work on the `master` branch.
|
||||
|
||||
4. Set up a development environment by running the following command in a virtual environment:
|
||||
|
||||
```bash
|
||||
$ pip install -e ".[dev]"
|
||||
```
|
||||
|
||||
(If transformers was already installed in the virtual environment, remove
|
||||
it with `pip uninstall transformers` before reinstalling it in editable
|
||||
mode with the `-e` flag.)
|
||||
|
||||
To run the full test suite, you might need the additional dependency on `datasets` which requires a separate source
|
||||
install:
|
||||
|
||||
```bash
|
||||
$ git clone https://github.com/huggingface/datasets
|
||||
$ cd datasets
|
||||
$ pip install -e .
|
||||
```
|
||||
|
||||
If you have already cloned that repo, you might need to `git pull` to get the most recent changes in the `datasets`
|
||||
library.
|
||||
|
||||
5. Develop the features on your branch.
|
||||
|
||||
As you work on the features, you should make sure that the test suite
|
||||
passes:
|
||||
|
||||
```bash
|
||||
$ make test
|
||||
```
|
||||
|
||||
Note, that this command uses `-n auto` pytest flag, therefore, it will start as many parallel `pytest` processes as the number of your computer's CPU-cores, and if you have lots of those and a few GPUs and not a great amount of RAM, it's likely to overload your computer. Therefore, to run the test suite, you may want to consider using this command instead:
|
||||
|
||||
```bash
|
||||
$ python -m pytest -n 3 --dist=loadfile -s -v ./tests/
|
||||
```
|
||||
|
||||
Adjust the value of `-n` to fit the load your hardware can support.
|
||||
|
||||
`transformers` relies on `black` and `isort` to format its source code
|
||||
consistently. After you make changes, format them with:
|
||||
|
||||
```bash
|
||||
$ make style
|
||||
```
|
||||
|
||||
`transformers` also uses `flake8` to check for coding mistakes. Quality
|
||||
control runs in CI, however you can also run the same checks with:
|
||||
|
||||
```bash
|
||||
$ make quality
|
||||
```
|
||||
|
||||
If you're modifying documents under `docs/source`, make sure to validate that
|
||||
they can still be built. This check also runs in CI. To run a local check
|
||||
make sure you have installed the documentation builder requirements, by
|
||||
running `pip install .[tf,torch,docs]` once from the root of this repository
|
||||
and then run:
|
||||
|
||||
```bash
|
||||
$ make docs
|
||||
```
|
||||
|
||||
Once you're happy with your changes, add changed files using `git add` and
|
||||
make a commit with `git commit` to record your changes locally:
|
||||
|
||||
```bash
|
||||
$ git add modified_file.py
|
||||
$ git commit
|
||||
```
|
||||
|
||||
Please write [good commit
|
||||
messages](https://chris.beams.io/posts/git-commit/).
|
||||
|
||||
It is a good idea to sync your copy of the code with the original
|
||||
repository regularly. This way you can quickly account for changes:
|
||||
|
||||
```bash
|
||||
$ git fetch upstream
|
||||
$ git rebase upstream/master
|
||||
```
|
||||
|
||||
Push the changes to your account using:
|
||||
|
||||
```bash
|
||||
$ git push -u origin a-descriptive-name-for-my-changes
|
||||
```
|
||||
|
||||
6. Once you are satisfied (**and the checklist below is happy too**), go to the
|
||||
webpage of your fork on GitHub. Click on 'Pull request' to send your changes
|
||||
to the project maintainers for review.
|
||||
|
||||
7. It's ok if maintainers ask you for changes. It happens to core contributors
|
||||
too! So everyone can see the changes in the Pull request, work in your local
|
||||
branch and push the changes to your fork. They will automatically appear in
|
||||
the pull request.
|
||||
|
||||
|
||||
### Checklist
|
||||
|
||||
1. The title of your pull request should be a summary of its contribution;
|
||||
2. If your pull request adresses an issue, please mention the issue number in
|
||||
the pull request description to make sure they are linked (and people
|
||||
consulting the issue know you are working on it);
|
||||
3. To indicate a work in progress please prefix the title with `[WIP]`. These
|
||||
are useful to avoid duplicated work, and to differentiate it from PRs ready
|
||||
to be merged;
|
||||
4. Make sure existing tests pass;
|
||||
5. Add high-coverage tests. No quality testing = no merge.
|
||||
- If you are adding a new model, make sure that you use
|
||||
`ModelTester.all_model_classes = (MyModel, MyModelWithLMHead,...)`, which triggers the common tests.
|
||||
- If you are adding new `@slow` tests, make sure they pass using
|
||||
`RUN_SLOW=1 python -m pytest tests/test_my_new_model.py`.
|
||||
- If you are adding a new tokenizer, write tests, and make sure
|
||||
`RUN_SLOW=1 python -m pytest tests/test_tokenization_{your_model_name}.py` passes.
|
||||
CircleCI does not run the slow tests, but github actions does every night!
|
||||
6. All public methods must have informative docstrings that work nicely with sphinx. See `modeling_ctrl.py` for an
|
||||
example.
|
||||
|
||||
### Tests
|
||||
|
||||
An extensive test suite is included to test the library behavior and several examples. Library tests can be found in
|
||||
the [tests folder](https://github.com/huggingface/transformers/tree/master/tests) and examples tests in the
|
||||
[examples folder](https://github.com/huggingface/transformers/tree/master/examples).
|
||||
|
||||
We like `pytest` and `pytest-xdist` because it's faster. From the root of the
|
||||
repository, here's how to run tests with `pytest` for the library:
|
||||
|
||||
```bash
|
||||
$ python -m pytest -n auto --dist=loadfile -s -v ./tests/
|
||||
```
|
||||
|
||||
and for the examples:
|
||||
|
||||
```bash
|
||||
$ pip install -r examples/requirements.txt # only needed the first time
|
||||
$ python -m pytest -n auto --dist=loadfile -s -v ./examples/
|
||||
```
|
||||
In fact, that's how `make test` and `make test-examples` are implemented (sans the `pip install` line)!
|
||||
|
||||
You can specify a smaller set of tests in order to test only the feature
|
||||
you're working on.
|
||||
|
||||
By default, slow tests are skipped. Set the `RUN_SLOW` environment variable to
|
||||
`yes` to run them. This will download many gigabytes of models — make sure you
|
||||
have enough disk space and a good Internet connection, or a lot of patience!
|
||||
|
||||
```bash
|
||||
$ RUN_SLOW=yes python -m pytest -n auto --dist=loadfile -s -v ./tests/
|
||||
$ RUN_SLOW=yes python -m pytest -n auto --dist=loadfile -s -v ./examples/
|
||||
```
|
||||
|
||||
Likewise, set the `RUN_CUSTOM_TOKENIZERS` environment variable to `yes` to run
|
||||
tests for custom tokenizers, which don't run by default either.
|
||||
|
||||
🤗 Transformers uses `pytest` as a test runner only. It doesn't use any
|
||||
`pytest`-specific features in the test suite itself.
|
||||
|
||||
This means `unittest` is fully supported. Here's how to run tests with
|
||||
`unittest`:
|
||||
|
||||
```bash
|
||||
$ python -m unittest discover -s tests -t . -v
|
||||
$ python -m unittest discover -s examples -t examples -v
|
||||
```
|
||||
|
||||
|
||||
### Style guide
|
||||
|
||||
For documentation strings, `transformers` follows the [google style](https://google.github.io/styleguide/pyguide.html).
|
||||
Check our [documentation writing guide](https://github.com/huggingface/transformers/tree/master/docs#writing-documentation---specification)
|
||||
for more information.
|
||||
|
||||
#### This guide was heavily inspired by the awesome [scikit-learn guide to contributing](https://github.com/scikit-learn/scikit-learn/blob/master/CONTRIBUTING.md)
|
||||
@@ -1 +0,0 @@
|
||||
include LICENSE
|
||||
@@ -1,30 +0,0 @@
|
||||
.PHONY: quality style test test-examples docs
|
||||
|
||||
# Check that source code meets quality standards
|
||||
|
||||
quality:
|
||||
black --check --line-length 119 --target-version py35 examples templates tests src utils
|
||||
isort --check-only examples templates tests src utils
|
||||
flake8 examples templates tests src utils
|
||||
python utils/check_repo.py
|
||||
|
||||
# Format source code automatically
|
||||
|
||||
style:
|
||||
black --line-length 119 --target-version py35 examples templates tests src utils
|
||||
isort examples templates tests src utils
|
||||
|
||||
# Run tests for the library
|
||||
|
||||
test:
|
||||
python -m pytest -n auto --dist=loadfile -s -v ./tests/
|
||||
|
||||
# Run tests for examples
|
||||
|
||||
test-examples:
|
||||
python -m pytest -n auto --dist=loadfile -s -v ./examples/
|
||||
|
||||
# Check that docs can build
|
||||
|
||||
docs:
|
||||
cd docs && make html SPHINXOPTS="-W"
|
||||
@@ -0,0 +1,2 @@
|
||||
#!/bin/sh
|
||||
python -m pytorch_pretrained_bert "$@"
|
||||
-10
@@ -1,10 +0,0 @@
|
||||
coverage:
|
||||
status:
|
||||
project:
|
||||
default:
|
||||
informational: true
|
||||
patch: off
|
||||
comment:
|
||||
require_changes: true # only comment if there was change in coverage
|
||||
require_head: yes # don't report if there is no head coverage report
|
||||
require_base: yes # don't report if there is no base coverage report
|
||||
@@ -0,0 +1,7 @@
|
||||
FROM pytorch/pytorch:latest
|
||||
|
||||
RUN git clone https://github.com/NVIDIA/apex.git && cd apex && python setup.py install --cuda_ext --cpp_ext
|
||||
|
||||
RUN pip install pytorch-pretrained-bert
|
||||
|
||||
WORKDIR /workspace
|
||||
@@ -1,26 +0,0 @@
|
||||
FROM ubuntu:18.04
|
||||
LABEL maintainer="Hugging Face"
|
||||
LABEL repository="transformers"
|
||||
|
||||
RUN apt update && \
|
||||
apt install -y bash \
|
||||
build-essential \
|
||||
git \
|
||||
curl \
|
||||
ca-certificates \
|
||||
python3 \
|
||||
python3-pip && \
|
||||
rm -rf /var/lib/apt/lists
|
||||
|
||||
RUN python3 -m pip install --no-cache-dir --upgrade pip && \
|
||||
python3 -m pip install --no-cache-dir \
|
||||
jupyter \
|
||||
tensorflow-cpu \
|
||||
torch
|
||||
|
||||
WORKDIR /workspace
|
||||
COPY . transformers/
|
||||
RUN cd transformers/ && \
|
||||
python3 -m pip install --no-cache-dir .
|
||||
|
||||
CMD ["/bin/bash"]
|
||||
@@ -1,26 +0,0 @@
|
||||
FROM nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04
|
||||
LABEL maintainer="Hugging Face"
|
||||
LABEL repository="transformers"
|
||||
|
||||
RUN apt update && \
|
||||
apt install -y bash \
|
||||
build-essential \
|
||||
git \
|
||||
curl \
|
||||
ca-certificates \
|
||||
python3 \
|
||||
python3-pip && \
|
||||
rm -rf /var/lib/apt/lists
|
||||
|
||||
RUN python3 -m pip install --no-cache-dir --upgrade pip && \
|
||||
python3 -m pip install --no-cache-dir \
|
||||
jupyter \
|
||||
tensorflow \
|
||||
torch
|
||||
|
||||
WORKDIR /workspace
|
||||
COPY . transformers/
|
||||
RUN cd transformers/ && \
|
||||
python3 -m pip install --no-cache-dir .
|
||||
|
||||
CMD ["/bin/bash"]
|
||||
@@ -1,25 +0,0 @@
|
||||
FROM ubuntu:18.04
|
||||
LABEL maintainer="Hugging Face"
|
||||
LABEL repository="transformers"
|
||||
|
||||
RUN apt update && \
|
||||
apt install -y bash \
|
||||
build-essential \
|
||||
git \
|
||||
curl \
|
||||
ca-certificates \
|
||||
python3 \
|
||||
python3-pip && \
|
||||
rm -rf /var/lib/apt/lists
|
||||
|
||||
RUN python3 -m pip install --no-cache-dir --upgrade pip && \
|
||||
python3 -m pip install --no-cache-dir \
|
||||
jupyter \
|
||||
torch
|
||||
|
||||
WORKDIR /workspace
|
||||
COPY . transformers/
|
||||
RUN cd transformers/ && \
|
||||
python3 -m pip install --no-cache-dir .
|
||||
|
||||
CMD ["/bin/bash"]
|
||||
@@ -1,25 +0,0 @@
|
||||
FROM nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04
|
||||
LABEL maintainer="Hugging Face"
|
||||
LABEL repository="transformers"
|
||||
|
||||
RUN apt update && \
|
||||
apt install -y bash \
|
||||
build-essential \
|
||||
git \
|
||||
curl \
|
||||
ca-certificates \
|
||||
python3 \
|
||||
python3-pip && \
|
||||
rm -rf /var/lib/apt/lists
|
||||
|
||||
RUN python3 -m pip install --no-cache-dir --upgrade pip && \
|
||||
python3 -m pip install --no-cache-dir \
|
||||
mkl \
|
||||
torch
|
||||
|
||||
WORKDIR /workspace
|
||||
COPY . transformers/
|
||||
RUN cd transformers/ && \
|
||||
python3 -m pip install --no-cache-dir .
|
||||
|
||||
CMD ["/bin/bash"]
|
||||
@@ -1,65 +0,0 @@
|
||||
FROM google/cloud-sdk:slim
|
||||
|
||||
# Build args.
|
||||
ARG GITHUB_REF=refs/heads/master
|
||||
|
||||
# TODO: This Dockerfile installs pytorch/xla 3.6 wheels. There are also 3.7
|
||||
# wheels available; see below.
|
||||
ENV PYTHON_VERSION=3.6
|
||||
|
||||
RUN apt-get update && apt-get install -y --no-install-recommends \
|
||||
build-essential \
|
||||
cmake \
|
||||
git \
|
||||
curl \
|
||||
ca-certificates
|
||||
|
||||
# Install conda and python.
|
||||
# NOTE new Conda does not forward the exit status... https://github.com/conda/conda/issues/8385
|
||||
RUN curl -o ~/miniconda.sh https://repo.anaconda.com/miniconda/Miniconda3-4.7.12-Linux-x86_64.sh && \
|
||||
chmod +x ~/miniconda.sh && \
|
||||
~/miniconda.sh -b && \
|
||||
rm ~/miniconda.sh
|
||||
|
||||
ENV PATH=/root/miniconda3/bin:$PATH
|
||||
|
||||
RUN conda create -y --name container python=$PYTHON_VERSION
|
||||
|
||||
# Run the rest of commands within the new conda env.
|
||||
# Use absolute path to appease Codefactor.
|
||||
SHELL ["/root/miniconda3/bin/conda", "run", "-n", "container", "/bin/bash", "-c"]
|
||||
RUN conda install -y python=$PYTHON_VERSION mkl
|
||||
|
||||
RUN pip uninstall -y torch && \
|
||||
# Python 3.7 wheels are available. Replace cp36-cp36m with cp37-cp37m
|
||||
gsutil cp 'gs://tpu-pytorch/wheels/torch-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' . && \
|
||||
gsutil cp 'gs://tpu-pytorch/wheels/torch_xla-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' . && \
|
||||
gsutil cp 'gs://tpu-pytorch/wheels/torchvision-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' . && \
|
||||
pip install 'torch-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' && \
|
||||
pip install 'torch_xla-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' && \
|
||||
pip install 'torchvision-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' && \
|
||||
rm 'torch-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' && \
|
||||
rm 'torch_xla-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' && \
|
||||
rm 'torchvision-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' && \
|
||||
apt-get install -y libomp5
|
||||
|
||||
ENV LD_LIBRARY_PATH=root/miniconda3/envs/container/lib
|
||||
|
||||
|
||||
# Install huggingface/transformers at the current PR, plus dependencies.
|
||||
RUN git clone https://github.com/huggingface/transformers.git && \
|
||||
cd transformers && \
|
||||
git fetch origin $GITHUB_REF:CI && \
|
||||
git checkout CI && \
|
||||
cd .. && \
|
||||
pip install ./transformers && \
|
||||
pip install -r ./transformers/examples/requirements.txt && \
|
||||
pip install pytest
|
||||
|
||||
RUN python -c "import torch_xla; print(torch_xla.__version__)"
|
||||
RUN python -c "import transformers as trf; print(trf.__version__)"
|
||||
RUN conda init bash
|
||||
COPY docker-entrypoint.sh /usr/local/bin/
|
||||
RUN chmod +x /usr/local/bin/docker-entrypoint.sh
|
||||
ENTRYPOINT ["/usr/local/bin/docker-entrypoint.sh"]
|
||||
CMD ["bash"]
|
||||
@@ -1,38 +0,0 @@
|
||||
local base = import 'templates/base.libsonnet';
|
||||
local tpus = import 'templates/tpus.libsonnet';
|
||||
local utils = import "templates/utils.libsonnet";
|
||||
local volumes = import "templates/volumes.libsonnet";
|
||||
|
||||
local bertBaseCased = base.BaseTest {
|
||||
frameworkPrefix: "hf",
|
||||
modelName: "bert-base-cased",
|
||||
mode: "example",
|
||||
configMaps: [],
|
||||
|
||||
timeout: 3600, # 1 hour, in seconds
|
||||
|
||||
image: std.extVar('image'),
|
||||
imageTag: std.extVar('image-tag'),
|
||||
|
||||
tpuSettings+: {
|
||||
softwareVersion: "pytorch-nightly",
|
||||
},
|
||||
accelerator: tpus.v3_8,
|
||||
|
||||
volumeMap+: {
|
||||
datasets: volumes.PersistentVolumeSpec {
|
||||
name: "huggingface-cluster-disk",
|
||||
mountPath: "/datasets",
|
||||
},
|
||||
},
|
||||
command: utils.scriptCommand(
|
||||
|||
|
||||
python -m pytest -s transformers/examples/test_xla_examples.py -v
|
||||
test_exit_code=$?
|
||||
echo "\nFinished running commands.\n"
|
||||
test $test_exit_code -eq 0
|
||||
|||
|
||||
),
|
||||
};
|
||||
|
||||
bertBaseCased.oneshotJob
|
||||
@@ -1,32 +0,0 @@
|
||||
apiVersion: v1
|
||||
kind: PersistentVolume
|
||||
metadata:
|
||||
name: huggingface-cluster-disk
|
||||
spec:
|
||||
storageClassName: ""
|
||||
capacity:
|
||||
storage: 500Gi
|
||||
accessModes:
|
||||
- ReadOnlyMany
|
||||
claimRef:
|
||||
namespace: default
|
||||
name: huggingface-cluster-disk-claim
|
||||
gcePersistentDisk:
|
||||
pdName: huggingface-cluster-disk
|
||||
fsType: ext4
|
||||
readOnly: true
|
||||
---
|
||||
apiVersion: v1
|
||||
kind: PersistentVolumeClaim
|
||||
metadata:
|
||||
name: huggingface-cluster-disk-claim
|
||||
spec:
|
||||
# Specify "" as the storageClassName so it matches the PersistentVolume's StorageClass.
|
||||
# A nil storageClassName value uses the default StorageClass. For details, see
|
||||
# https://kubernetes.io/docs/concepts/storage/persistent-volumes/#class-1
|
||||
storageClassName: ""
|
||||
accessModes:
|
||||
- ReadOnlyMany
|
||||
resources:
|
||||
requests:
|
||||
storage: 1Ki
|
||||
@@ -1,8 +0,0 @@
|
||||
#!/bin/bash
|
||||
source ~/.bashrc
|
||||
echo "running docker-entrypoint.sh"
|
||||
conda activate container
|
||||
echo $KUBE_GOOGLE_CLOUD_TPU_ENDPOINTS
|
||||
echo "printed TPU info"
|
||||
export XRT_TPU_CONFIG="tpu_worker;0;${KUBE_GOOGLE_CLOUD_TPU_ENDPOINTS:7}"
|
||||
exec "$@"#!/bin/bash
|
||||
@@ -1,25 +0,0 @@
|
||||
FROM ubuntu:18.04
|
||||
LABEL maintainer="Hugging Face"
|
||||
LABEL repository="transformers"
|
||||
|
||||
RUN apt update && \
|
||||
apt install -y bash \
|
||||
build-essential \
|
||||
git \
|
||||
curl \
|
||||
ca-certificates \
|
||||
python3 \
|
||||
python3-pip && \
|
||||
rm -rf /var/lib/apt/lists
|
||||
|
||||
RUN python3 -m pip install --no-cache-dir --upgrade pip && \
|
||||
python3 -m pip install --no-cache-dir \
|
||||
mkl \
|
||||
tensorflow-cpu
|
||||
|
||||
WORKDIR /workspace
|
||||
COPY . transformers/
|
||||
RUN cd transformers/ && \
|
||||
python3 -m pip install --no-cache-dir .
|
||||
|
||||
CMD ["/bin/bash"]
|
||||
@@ -1,25 +0,0 @@
|
||||
FROM nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04
|
||||
LABEL maintainer="Hugging Face"
|
||||
LABEL repository="transformers"
|
||||
|
||||
RUN apt update && \
|
||||
apt install -y bash \
|
||||
build-essential \
|
||||
git \
|
||||
curl \
|
||||
ca-certificates \
|
||||
python3 \
|
||||
python3-pip && \
|
||||
rm -rf /var/lib/apt/lists
|
||||
|
||||
RUN python3 -m pip install --no-cache-dir --upgrade pip && \
|
||||
python3 -m pip install --no-cache-dir \
|
||||
mkl \
|
||||
tensorflow
|
||||
|
||||
WORKDIR /workspace
|
||||
COPY . transformers/
|
||||
RUN cd transformers/ && \
|
||||
python3 -m pip install --no-cache-dir .
|
||||
|
||||
CMD ["/bin/bash"]
|
||||
@@ -1,19 +0,0 @@
|
||||
# Minimal makefile for Sphinx documentation
|
||||
#
|
||||
|
||||
# You can set these variables from the command line.
|
||||
SPHINXOPTS =
|
||||
SPHINXBUILD = sphinx-build
|
||||
SOURCEDIR = source
|
||||
BUILDDIR = _build
|
||||
|
||||
# Put it first so that "make" without argument is like "make help".
|
||||
help:
|
||||
@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
|
||||
|
||||
.PHONY: help Makefile
|
||||
|
||||
# Catch-all target: route all unknown targets to Sphinx using the new
|
||||
# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS).
|
||||
%: Makefile
|
||||
@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
|
||||
-211
@@ -1,211 +0,0 @@
|
||||
# Generating the documentation
|
||||
|
||||
To generate the documentation, you first have to build it. Several packages are necessary to build the doc,
|
||||
you can install them with the following command, at the root of the code repository:
|
||||
|
||||
```bash
|
||||
pip install -e ".[docs]"
|
||||
```
|
||||
|
||||
---
|
||||
**NOTE**
|
||||
|
||||
You only need to generate the documentation to inspect it locally (if you're planning changes and want to
|
||||
check how they look like before committing for instance). You don't have to commit the built documentation.
|
||||
|
||||
---
|
||||
|
||||
## Packages installed
|
||||
|
||||
Here's an overview of all the packages installed. If you ran the previous command installing all packages from
|
||||
`requirements.txt`, you do not need to run the following commands.
|
||||
|
||||
Building it requires the package `sphinx` that you can
|
||||
install using:
|
||||
|
||||
```bash
|
||||
pip install -U sphinx
|
||||
```
|
||||
|
||||
You would also need the custom installed [theme](https://github.com/readthedocs/sphinx_rtd_theme) by
|
||||
[Read The Docs](https://readthedocs.org/). You can install it using the following command:
|
||||
|
||||
```bash
|
||||
pip install sphinx_rtd_theme
|
||||
```
|
||||
|
||||
The third necessary package is the `recommonmark` package to accept Markdown as well as Restructured text:
|
||||
|
||||
```bash
|
||||
pip install recommonmark
|
||||
```
|
||||
|
||||
## Building the documentation
|
||||
|
||||
Once you have setup `sphinx`, you can build the documentation by running the following command in the `/docs` folder:
|
||||
|
||||
```bash
|
||||
make html
|
||||
```
|
||||
|
||||
A folder called ``_build/html`` should have been created. You can now open the file ``_build/html/index.html`` in your
|
||||
browser.
|
||||
|
||||
---
|
||||
**NOTE**
|
||||
|
||||
If you are adding/removing elements from the toc-tree or from any structural item, it is recommended to clean the build
|
||||
directory before rebuilding. Run the following command to clean and build:
|
||||
|
||||
```bash
|
||||
make clean && make html
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
It should build the static app that will be available under `/docs/_build/html`
|
||||
|
||||
## Adding a new element to the tree (toc-tree)
|
||||
|
||||
Accepted files are reStructuredText (.rst) and Markdown (.md). Create a file with its extension and put it
|
||||
in the source directory. You can then link it to the toc-tree by putting the filename without the extension.
|
||||
|
||||
## Preview the documentation in a pull request
|
||||
|
||||
Once you have made your pull request, you can check what the documentation will look like after it's merged by
|
||||
following these steps:
|
||||
|
||||
- Look at the checks at the bottom of the conversation page of your PR (you may need to click on "show all checks" to
|
||||
expand them).
|
||||
- Click on "details" next to the `ci/circleci: build_doc` check.
|
||||
- In the new window, click on the "Artifacts" tab.
|
||||
- Locate the file "docs/_build/html/index.html" (or any specific page you want to check) and click on it to get a
|
||||
preview.
|
||||
|
||||
## Writing Documentation - Specification
|
||||
|
||||
The `huggingface/transformers` documentation follows the
|
||||
[Google documentation](https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html) style. It is
|
||||
mostly written in ReStructuredText
|
||||
([Sphinx simple documentation](https://www.sphinx-doc.org/en/master/usage/restructuredtext/index.html),
|
||||
[Sourceforge complete documentation](https://docutils.sourceforge.io/docs/ref/rst/restructuredtext.html))
|
||||
|
||||
### Adding a new section
|
||||
|
||||
A section is a page held in the `Notes` toc-tree on the documentation. Adding a new section is done in two steps:
|
||||
|
||||
- Add a new file under `./source`. This file can either be ReStructuredText (.rst) or Markdown (.md).
|
||||
- Link that file in `./source/index.rst` on the correct toc-tree.
|
||||
|
||||
### Adding a new model
|
||||
|
||||
When adding a new model:
|
||||
|
||||
- Create a file `xxx.rst` under `./source/model_doc`.
|
||||
- Link that file in `./source/index.rst` on the `model_doc` toc-tree.
|
||||
- Write a short overview of the model:
|
||||
- Overview with paper & authors
|
||||
- Paper abstract
|
||||
- Tips and tricks and how to use it best
|
||||
- Add the classes that should be linked in the model. This generally includes the configuration, the tokenizer, and
|
||||
every model of that class (the base model, alongside models with additional heads), both in PyTorch and TensorFlow.
|
||||
The order is generally:
|
||||
- Configuration,
|
||||
- Tokenizer
|
||||
- PyTorch base model
|
||||
- PyTorch head models
|
||||
- TensorFlow base model
|
||||
- TensorFlow head models
|
||||
|
||||
These classes should be added using the RST syntax. Usually as follows:
|
||||
```
|
||||
XXXConfig
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.XXXConfig
|
||||
:members:
|
||||
```
|
||||
|
||||
This will include every public method of the configuration. If for some reason you wish for a method not to be
|
||||
displayed in the documentation, you can do so by specifying which methods should be in the docs:
|
||||
|
||||
```
|
||||
XXXTokenizer
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.XXXTokenizer
|
||||
:members: build_inputs_with_special_tokens, get_special_tokens_mask,
|
||||
create_token_type_ids_from_sequences, save_vocabulary
|
||||
|
||||
```
|
||||
|
||||
### Writing source documentation
|
||||
|
||||
Values that should be put in `code` should either be surrounded by double backticks: \`\`like so\`\` or be written as
|
||||
an object using the :obj: syntax: :obj:\`like so\`.
|
||||
|
||||
When mentionning a class, it is recommended to use the :class: syntax as the mentioned class will be automatically
|
||||
linked by Sphinx: :class:\`transformers.XXXClass\`
|
||||
|
||||
When mentioning a function, it is recommended to use the :func: syntax as the mentioned method will be automatically
|
||||
linked by Sphinx: :func:\`transformers.XXXClass.method\`
|
||||
|
||||
Links should be done as so (note the double underscore at the end): \`text for the link <./local-link-or-global-link#loc>\`__
|
||||
|
||||
#### Defining arguments in a method
|
||||
|
||||
Arguments should be defined with the `Args:` prefix, followed by a line return and an indentation.
|
||||
The argument should be followed by its type, with its shape if it is a tensor, and a line return.
|
||||
Another indentation is necessary before writing the description of the argument.
|
||||
|
||||
Here's an example showcasing everything so far:
|
||||
|
||||
```
|
||||
Args:
|
||||
input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`):
|
||||
Indices of input sequence tokens in the vocabulary.
|
||||
|
||||
Indices can be obtained using :class:`transformers.AlbertTokenizer`.
|
||||
See :func:`transformers.PreTrainedTokenizer.encode` and
|
||||
:func:`transformers.PreTrainedTokenizer.__call__` for details.
|
||||
|
||||
`What are input IDs? <../glossary.html#input-ids>`__
|
||||
```
|
||||
|
||||
#### Writing a multi-line code block
|
||||
|
||||
Multi-line code blocks can be useful for displaying examples. They are done like so:
|
||||
|
||||
```
|
||||
Example::
|
||||
|
||||
# first line of code
|
||||
# second line
|
||||
# etc
|
||||
```
|
||||
|
||||
The `Example` string at the beginning can be replaced by anything as long as there are two semicolons following it.
|
||||
|
||||
#### Writing a return block
|
||||
|
||||
Arguments should be defined with the `Args:` prefix, followed by a line return and an indentation.
|
||||
The first line should be the type of the return, followed by a line return. No need to indent further for the elements
|
||||
building the return.
|
||||
|
||||
Here's an example for tuple return, comprising several objects:
|
||||
|
||||
```
|
||||
Returns:
|
||||
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
|
||||
loss (`optional`, returned when ``masked_lm_labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
|
||||
Total loss as the sum of the masked language modeling loss and the next sequence prediction (classification) loss.
|
||||
prediction_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`)
|
||||
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
|
||||
```
|
||||
|
||||
Here's an example for a single value return:
|
||||
|
||||
```
|
||||
Returns:
|
||||
A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
|
||||
```
|
||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
@@ -1,16 +0,0 @@
|
||||
|
||||
.highlight .c1, .highlight .sd{
|
||||
color: #999
|
||||
}
|
||||
|
||||
.highlight .nn, .highlight .k, .highlight .s1, .highlight .nb, .highlight .bp, .highlight .kc {
|
||||
color: #FB8D68;
|
||||
}
|
||||
|
||||
.highlight .kn, .highlight .nv, .highlight .s2, .highlight .ow {
|
||||
color: #6670FF;
|
||||
}
|
||||
|
||||
.highlight .gp {
|
||||
color: #FB8D68;
|
||||
}
|
||||
@@ -1,335 +0,0 @@
|
||||
/* Our DOM objects */
|
||||
|
||||
/* Colab dropdown */
|
||||
|
||||
.colab-dropdown {
|
||||
position: relative;
|
||||
display: inline-block;
|
||||
}
|
||||
|
||||
.colab-dropdown-content {
|
||||
display: none;
|
||||
position: absolute;
|
||||
background-color: #f9f9f9;
|
||||
min-width: 117px;
|
||||
box-shadow: 0px 8px 16px 0px rgba(0,0,0,0.2);
|
||||
z-index: 1;
|
||||
}
|
||||
|
||||
.colab-dropdown-content button {
|
||||
color: #6670FF;
|
||||
background-color: #f9f9f9;
|
||||
font-size: 12px;
|
||||
border: none;
|
||||
min-width: 117px;
|
||||
padding: 5px 5px;
|
||||
text-decoration: none;
|
||||
display: block;
|
||||
}
|
||||
|
||||
.colab-dropdown-content button:hover {background-color: #eee;}
|
||||
|
||||
.colab-dropdown:hover .colab-dropdown-content {display: block;}
|
||||
|
||||
/* Version control */
|
||||
|
||||
.version-button {
|
||||
background-color: #6670FF;
|
||||
color: white;
|
||||
border: none;
|
||||
padding: 5px;
|
||||
font-size: 15px;
|
||||
cursor: pointer;
|
||||
}
|
||||
|
||||
.version-button:hover, .version-button:focus {
|
||||
background-color: #A6B0FF;
|
||||
}
|
||||
|
||||
.version-dropdown {
|
||||
display: none;
|
||||
background-color: #6670FF;
|
||||
min-width: 160px;
|
||||
overflow: auto;
|
||||
font-size: 15px;
|
||||
}
|
||||
|
||||
.version-dropdown a {
|
||||
color: white;
|
||||
padding: 3px 4px;
|
||||
text-decoration: none;
|
||||
display: block;
|
||||
}
|
||||
|
||||
.version-dropdown a:hover {
|
||||
background-color: #A6B0FF;
|
||||
}
|
||||
|
||||
.version-show {
|
||||
display: block;
|
||||
}
|
||||
|
||||
/* Framework selector */
|
||||
|
||||
.framework-selector {
|
||||
display: flex;
|
||||
flex-direction: row;
|
||||
justify-content: flex-end;
|
||||
margin-right: 30px;
|
||||
}
|
||||
|
||||
.framework-selector > button {
|
||||
background-color: white;
|
||||
color: #6670FF;
|
||||
border: 1px solid #6670FF;
|
||||
padding: 5px;
|
||||
}
|
||||
|
||||
.framework-selector > button.selected{
|
||||
background-color: #6670FF;
|
||||
color: white;
|
||||
border: 1px solid #6670FF;
|
||||
padding: 5px;
|
||||
}
|
||||
|
||||
/* Copy button */
|
||||
|
||||
a.copybtn {
|
||||
margin: 3px;
|
||||
}
|
||||
|
||||
/* The literal code blocks */
|
||||
.rst-content tt.literal, .rst-content tt.literal, .rst-content code.literal {
|
||||
color: #6670FF;
|
||||
}
|
||||
|
||||
/* To keep the logo centered */
|
||||
.wy-side-scroll {
|
||||
width: auto;
|
||||
font-size: 20px;
|
||||
}
|
||||
|
||||
/* The div that holds the Hugging Face logo */
|
||||
.HuggingFaceDiv {
|
||||
width: 100%
|
||||
}
|
||||
|
||||
/* The research field on top of the toc tree */
|
||||
.wy-side-nav-search{
|
||||
padding-top: 0;
|
||||
background-color: #6670FF;
|
||||
}
|
||||
|
||||
/* The toc tree */
|
||||
.wy-nav-side{
|
||||
background-color: #6670FF;
|
||||
}
|
||||
|
||||
/* The selected items in the toc tree */
|
||||
.wy-menu-vertical li.current{
|
||||
background-color: #A6B0FF;
|
||||
}
|
||||
|
||||
/* When a list item that does belong to the selected block from the toc tree is hovered */
|
||||
.wy-menu-vertical li.current a:hover{
|
||||
background-color: #B6C0FF;
|
||||
}
|
||||
|
||||
/* When a list item that does NOT belong to the selected block from the toc tree is hovered. */
|
||||
.wy-menu-vertical li a:hover{
|
||||
background-color: #A7AFFB;
|
||||
}
|
||||
|
||||
/* The text items on the toc tree */
|
||||
.wy-menu-vertical a {
|
||||
color: #FFFFDD;
|
||||
font-family: Calibre-Light, sans-serif;
|
||||
}
|
||||
.wy-menu-vertical header, .wy-menu-vertical p.caption{
|
||||
color: white;
|
||||
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* Nav Links to other parts of huggingface.co
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File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
|
Before Width: | Height: | Size: 7.6 KiB |
@@ -1,322 +0,0 @@
|
||||
Benchmarks
|
||||
==========
|
||||
|
||||
Let's take a look at how 🤗 Transformer models can be benchmarked, best practices, and already available benchmarks.
|
||||
|
||||
A notebook explaining in more detail how to benchmark 🤗 Transformer models can be found `here <https://github.com/huggingface/transformers/blob/master/notebooks/05-benchmark.ipynb>`__.
|
||||
|
||||
How to benchmark 🤗 Transformer models
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
The classes :class:`~transformers.PyTorchBenchmark` and :class:`~transformers.TensorFlowBenchmark` allow to flexibly benchmark 🤗 Transformer models.
|
||||
The benchmark classes allow us to measure the `peak memory usage` and `required time` for both
|
||||
`inference` and `training`.
|
||||
|
||||
.. note::
|
||||
|
||||
Hereby, `inference` is defined by a single forward pass, and `training` is defined by a single forward pass and backward pass.
|
||||
|
||||
The benchmark classes :class:`~transformers.PyTorchBenchmark` and :class:`~transformers.TensorFlowBenchmark` expect an object of type :class:`~transformers.PyTorchBenchmarkArguments` and :class:`~transformers.TensorFlowBenchmarkArguments`, respectively, for instantiation. :class:`~transformers.PyTorchBenchmarkArguments` and :class:`~transformers.TensorFlowBenchmarkArguments` are data classes and contain all relevant configurations for their corresponding benchmark class.
|
||||
In the following example, it is shown how a BERT model of type `bert-base-cased` can be benchmarked.
|
||||
|
||||
.. code-block::
|
||||
|
||||
>>> ## PYTORCH CODE
|
||||
>>> from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
|
||||
|
||||
>>> args = PyTorchBenchmarkArguments(models=["bert-base-uncased"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512])
|
||||
>>> benchmark = PyTorchBenchmark(args)
|
||||
|
||||
>>> ## TENSORFLOW CODE
|
||||
>>> from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
|
||||
|
||||
>>> args = TensorFlowBenchmarkArguments(models=["bert-base-uncased"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512])
|
||||
>>> benchmark = TensorFlowBenchmark(args)
|
||||
|
||||
|
||||
Here, three arguments are given to the benchmark argument data classes, namely ``models``, ``batch_sizes``, and ``sequence_lengths``. The argument ``models`` is required and expects a :obj:`list` of model identifiers from the `model hub <https://huggingface.co/models>`__
|
||||
The :obj:`list` arguments ``batch_sizes`` and ``sequence_lengths`` define the size of the ``input_ids`` on which the model is benchmarked.
|
||||
There are many more parameters that can be configured via the benchmark argument data classes. For more detail on these one can either directly consult the files
|
||||
``src/transformers/benchmark/benchmark_args_utils.py``, ``src/transformers/benchmark/benchmark_args.py`` (for PyTorch) and ``src/transformers/benchmark/benchmark_args_tf.py`` (for Tensorflow).
|
||||
Alternatively, running the following shell commands from root will print out a descriptive list of all configurable parameters for PyTorch and Tensorflow respectively.
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
## PYTORCH CODE
|
||||
python examples/benchmarking/run_benchmark.py --help
|
||||
|
||||
## TENSORFLOW CODE
|
||||
python examples/benchmarking/run_benchmark_tf.py --help
|
||||
|
||||
|
||||
An instantiated benchmark object can then simply be run by calling ``benchmark.run()``.
|
||||
|
||||
.. code-block::
|
||||
|
||||
>>> ## PYTORCH CODE
|
||||
>>> results = benchmark.run()
|
||||
>>> print(results)
|
||||
==================== INFERENCE - SPEED - RESULT ====================
|
||||
--------------------------------------------------------------------------------
|
||||
Model Name Batch Size Seq Length Time in s
|
||||
--------------------------------------------------------------------------------
|
||||
bert-base-uncased 8 8 0.006
|
||||
bert-base-uncased 8 32 0.006
|
||||
bert-base-uncased 8 128 0.018
|
||||
bert-base-uncased 8 512 0.088
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
==================== INFERENCE - MEMORY - RESULT ====================
|
||||
--------------------------------------------------------------------------------
|
||||
Model Name Batch Size Seq Length Memory in MB
|
||||
--------------------------------------------------------------------------------
|
||||
bert-base-uncased 8 8 1227
|
||||
bert-base-uncased 8 32 1281
|
||||
bert-base-uncased 8 128 1307
|
||||
bert-base-uncased 8 512 1539
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
==================== ENVIRONMENT INFORMATION ====================
|
||||
- transformers_version: 2.11.0
|
||||
- framework: PyTorch
|
||||
- use_torchscript: False
|
||||
- framework_version: 1.4.0
|
||||
- python_version: 3.6.10
|
||||
- system: Linux
|
||||
- cpu: x86_64
|
||||
- architecture: 64bit
|
||||
- date: 2020-06-29
|
||||
- time: 08:58:43.371351
|
||||
- fp16: False
|
||||
- use_multiprocessing: True
|
||||
- only_pretrain_model: False
|
||||
- cpu_ram_mb: 32088
|
||||
- use_gpu: True
|
||||
- num_gpus: 1
|
||||
- gpu: TITAN RTX
|
||||
- gpu_ram_mb: 24217
|
||||
- gpu_power_watts: 280.0
|
||||
- gpu_performance_state: 2
|
||||
- use_tpu: False
|
||||
|
||||
>>> ## TENSORFLOW CODE
|
||||
>>> results = benchmark.run()
|
||||
>>> print(results)
|
||||
==================== INFERENCE - SPEED - RESULT ====================
|
||||
--------------------------------------------------------------------------------
|
||||
Model Name Batch Size Seq Length Time in s
|
||||
--------------------------------------------------------------------------------
|
||||
bert-base-uncased 8 8 0.005
|
||||
bert-base-uncased 8 32 0.008
|
||||
bert-base-uncased 8 128 0.022
|
||||
bert-base-uncased 8 512 0.105
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
==================== INFERENCE - MEMORY - RESULT ====================
|
||||
--------------------------------------------------------------------------------
|
||||
Model Name Batch Size Seq Length Memory in MB
|
||||
--------------------------------------------------------------------------------
|
||||
bert-base-uncased 8 8 1330
|
||||
bert-base-uncased 8 32 1330
|
||||
bert-base-uncased 8 128 1330
|
||||
bert-base-uncased 8 512 1770
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
==================== ENVIRONMENT INFORMATION ====================
|
||||
- transformers_version: 2.11.0
|
||||
- framework: Tensorflow
|
||||
- use_xla: False
|
||||
- framework_version: 2.2.0
|
||||
- python_version: 3.6.10
|
||||
- system: Linux
|
||||
- cpu: x86_64
|
||||
- architecture: 64bit
|
||||
- date: 2020-06-29
|
||||
- time: 09:26:35.617317
|
||||
- fp16: False
|
||||
- use_multiprocessing: True
|
||||
- only_pretrain_model: False
|
||||
- cpu_ram_mb: 32088
|
||||
- use_gpu: True
|
||||
- num_gpus: 1
|
||||
- gpu: TITAN RTX
|
||||
- gpu_ram_mb: 24217
|
||||
- gpu_power_watts: 280.0
|
||||
- gpu_performance_state: 2
|
||||
- use_tpu: False
|
||||
|
||||
By default, the `time` and the `required memory` for `inference` are benchmarked.
|
||||
In the example output above the first two sections show the result corresponding to `inference time` and `inference memory`.
|
||||
In addition, all relevant information about the computing environment, `e.g.` the GPU type, the system, the library versions, etc... are printed out in the third section under `ENVIRONMENT INFORMATION`.
|
||||
This information can optionally be saved in a `.csv` file when adding the argument :obj:`save_to_csv=True` to :class:`~transformers.PyTorchBenchmarkArguments` and :class:`~transformers.TensorFlowBenchmarkArguments` respectively.
|
||||
In this case, every section is saved in a separate `.csv` file. The path to each `.csv` file can optionally be defined via the argument data classes.
|
||||
|
||||
Instead of benchmarking pre-trained models via their model identifier, `e.g.` `bert-base-uncased`, the user can alternatively benchmark an arbitrary configuration of any available model class.
|
||||
In this case, a :obj:`list` of configurations must be inserted with the benchmark args as follows.
|
||||
|
||||
.. code-block::
|
||||
|
||||
>>> ## PYTORCH CODE
|
||||
>>> from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments, BertConfig
|
||||
|
||||
>>> args = PyTorchBenchmarkArguments(models=["bert-base", "bert-384-hid", "bert-6-lay"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512])
|
||||
>>> config_base = BertConfig()
|
||||
>>> config_384_hid = BertConfig(hidden_size=384)
|
||||
>>> config_6_lay = BertConfig(num_hidden_layers=6)
|
||||
|
||||
>>> benchmark = PyTorchBenchmark(args, configs=[config_base, config_384_hid, config_6_lay])
|
||||
>>> benchmark.run()
|
||||
==================== INFERENCE - SPEED - RESULT ====================
|
||||
--------------------------------------------------------------------------------
|
||||
Model Name Batch Size Seq Length Time in s
|
||||
--------------------------------------------------------------------------------
|
||||
bert-base 8 128 0.006
|
||||
bert-base 8 512 0.006
|
||||
bert-base 8 128 0.018
|
||||
bert-base 8 512 0.088
|
||||
bert-384-hid 8 8 0.006
|
||||
bert-384-hid 8 32 0.006
|
||||
bert-384-hid 8 128 0.011
|
||||
bert-384-hid 8 512 0.054
|
||||
bert-6-lay 8 8 0.003
|
||||
bert-6-lay 8 32 0.004
|
||||
bert-6-lay 8 128 0.009
|
||||
bert-6-lay 8 512 0.044
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
==================== INFERENCE - MEMORY - RESULT ====================
|
||||
--------------------------------------------------------------------------------
|
||||
Model Name Batch Size Seq Length Memory in MB
|
||||
--------------------------------------------------------------------------------
|
||||
bert-base 8 8 1277
|
||||
bert-base 8 32 1281
|
||||
bert-base 8 128 1307
|
||||
bert-base 8 512 1539
|
||||
bert-384-hid 8 8 1005
|
||||
bert-384-hid 8 32 1027
|
||||
bert-384-hid 8 128 1035
|
||||
bert-384-hid 8 512 1255
|
||||
bert-6-lay 8 8 1097
|
||||
bert-6-lay 8 32 1101
|
||||
bert-6-lay 8 128 1127
|
||||
bert-6-lay 8 512 1359
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
==================== ENVIRONMENT INFORMATION ====================
|
||||
- transformers_version: 2.11.0
|
||||
- framework: PyTorch
|
||||
- use_torchscript: False
|
||||
- framework_version: 1.4.0
|
||||
- python_version: 3.6.10
|
||||
- system: Linux
|
||||
- cpu: x86_64
|
||||
- architecture: 64bit
|
||||
- date: 2020-06-29
|
||||
- time: 09:35:25.143267
|
||||
- fp16: False
|
||||
- use_multiprocessing: True
|
||||
- only_pretrain_model: False
|
||||
- cpu_ram_mb: 32088
|
||||
- use_gpu: True
|
||||
- num_gpus: 1
|
||||
- gpu: TITAN RTX
|
||||
- gpu_ram_mb: 24217
|
||||
- gpu_power_watts: 280.0
|
||||
- gpu_performance_state: 2
|
||||
- use_tpu: False
|
||||
|
||||
>>> ## TENSORFLOW CODE
|
||||
>>> from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments, BertConfig
|
||||
|
||||
>>> args = TensorFlowBenchmarkArguments(models=["bert-base", "bert-384-hid", "bert-6-lay"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512])
|
||||
>>> config_base = BertConfig()
|
||||
>>> config_384_hid = BertConfig(hidden_size=384)
|
||||
>>> config_6_lay = BertConfig(num_hidden_layers=6)
|
||||
|
||||
>>> benchmark = TensorFlowBenchmark(args, configs=[config_base, config_384_hid, config_6_lay])
|
||||
>>> benchmark.run()
|
||||
==================== INFERENCE - SPEED - RESULT ====================
|
||||
--------------------------------------------------------------------------------
|
||||
Model Name Batch Size Seq Length Time in s
|
||||
--------------------------------------------------------------------------------
|
||||
bert-base 8 8 0.005
|
||||
bert-base 8 32 0.008
|
||||
bert-base 8 128 0.022
|
||||
bert-base 8 512 0.106
|
||||
bert-384-hid 8 8 0.005
|
||||
bert-384-hid 8 32 0.007
|
||||
bert-384-hid 8 128 0.018
|
||||
bert-384-hid 8 512 0.064
|
||||
bert-6-lay 8 8 0.002
|
||||
bert-6-lay 8 32 0.003
|
||||
bert-6-lay 8 128 0.0011
|
||||
bert-6-lay 8 512 0.074
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
==================== INFERENCE - MEMORY - RESULT ====================
|
||||
--------------------------------------------------------------------------------
|
||||
Model Name Batch Size Seq Length Memory in MB
|
||||
--------------------------------------------------------------------------------
|
||||
bert-base 8 8 1330
|
||||
bert-base 8 32 1330
|
||||
bert-base 8 128 1330
|
||||
bert-base 8 512 1770
|
||||
bert-384-hid 8 8 1330
|
||||
bert-384-hid 8 32 1330
|
||||
bert-384-hid 8 128 1330
|
||||
bert-384-hid 8 512 1540
|
||||
bert-6-lay 8 8 1330
|
||||
bert-6-lay 8 32 1330
|
||||
bert-6-lay 8 128 1330
|
||||
bert-6-lay 8 512 1540
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
==================== ENVIRONMENT INFORMATION ====================
|
||||
- transformers_version: 2.11.0
|
||||
- framework: Tensorflow
|
||||
- use_xla: False
|
||||
- framework_version: 2.2.0
|
||||
- python_version: 3.6.10
|
||||
- system: Linux
|
||||
- cpu: x86_64
|
||||
- architecture: 64bit
|
||||
- date: 2020-06-29
|
||||
- time: 09:38:15.487125
|
||||
- fp16: False
|
||||
- use_multiprocessing: True
|
||||
- only_pretrain_model: False
|
||||
- cpu_ram_mb: 32088
|
||||
- use_gpu: True
|
||||
- num_gpus: 1
|
||||
- gpu: TITAN RTX
|
||||
- gpu_ram_mb: 24217
|
||||
- gpu_power_watts: 280.0
|
||||
- gpu_performance_state: 2
|
||||
- use_tpu: False
|
||||
|
||||
|
||||
Again, `inference time` and `required memory` for `inference` are measured, but this time for customized configurations of the :obj:`BertModel` class. This feature can especially be helpful when
|
||||
deciding for which configuration the model should be trained.
|
||||
|
||||
|
||||
Benchmark best practices
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
This section lists a couple of best practices one should be aware of when benchmarking a model.
|
||||
|
||||
- Currently, only single device benchmarking is supported. When benchmarking on GPU, it is recommended that the user
|
||||
specifies on which device the code should be run by setting the ``CUDA_VISIBLE_DEVICES`` environment variable in the shell, `e.g.` ``export CUDA_VISIBLE_DEVICES=0`` before running the code.
|
||||
- The option :obj:`no_multi_processing` should only be set to :obj:`True` for testing and debugging. To ensure accurate memory measurement it is recommended to run each memory benchmark in a separate process by making sure :obj:`no_multi_processing` is set to :obj:`True`.
|
||||
- One should always state the environment information when sharing the results of a model benchmark. Results can vary heavily between different GPU devices, library versions, etc., so that benchmark results on their own are not very useful for the community.
|
||||
|
||||
|
||||
Sharing your benchmark
|
||||
~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
Previously all available core models (10 at the time) have been benchmarked for `inference time`, across many different settings: using PyTorch, with
|
||||
and without TorchScript, using TensorFlow, with and without XLA. All of those tests were done across CPUs (except for
|
||||
TensorFlow XLA) and GPUs.
|
||||
|
||||
The approach is detailed in the `following blogpost <https://medium.com/huggingface/benchmarking-transformers-pytorch-and-tensorflow-e2917fb891c2>`__ and the results are available `here <https://docs.google.com/spreadsheets/d/1sryqufw2D0XlUH4sq3e9Wnxu5EAQkaohzrJbd5HdQ_w/edit?usp=sharing>`__.
|
||||
|
||||
With the new `benchmark` tools, it is easier than ever to share your benchmark results with the community `here <https://github.com/huggingface/transformers/blob/master/examples/benchmarking/README.md>`__.
|
||||
@@ -1,18 +0,0 @@
|
||||
BERTology
|
||||
---------
|
||||
|
||||
There is a growing field of study concerned with investigating the inner working of large-scale transformers like BERT (that some call "BERTology"). Some good examples of this field are:
|
||||
|
||||
|
||||
* BERT Rediscovers the Classical NLP Pipeline by Ian Tenney, Dipanjan Das, Ellie Pavlick: https://arxiv.org/abs/1905.05950
|
||||
* Are Sixteen Heads Really Better than One? by Paul Michel, Omer Levy, Graham Neubig: https://arxiv.org/abs/1905.10650
|
||||
* What Does BERT Look At? An Analysis of BERT's Attention by Kevin Clark, Urvashi Khandelwal, Omer Levy, Christopher D. Manning: https://arxiv.org/abs/1906.04341
|
||||
|
||||
In order to help this new field develop, we have included a few additional features in the BERT/GPT/GPT-2 models to help people access the inner representations, mainly adapted from the great work of Paul Michel (https://arxiv.org/abs/1905.10650):
|
||||
|
||||
|
||||
* accessing all the hidden-states of BERT/GPT/GPT-2,
|
||||
* accessing all the attention weights for each head of BERT/GPT/GPT-2,
|
||||
* retrieving heads output values and gradients to be able to compute head importance score and prune head as explained in https://arxiv.org/abs/1905.10650.
|
||||
|
||||
To help you understand and use these features, we have added a specific example script: `bertology.py <https://github.com/huggingface/transformers/blob/master/examples/bertology/run_bertology.py>`_ while extract information and prune a model pre-trained on GLUE.
|
||||
@@ -1,198 +0,0 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
#
|
||||
# Configuration file for the Sphinx documentation builder.
|
||||
#
|
||||
# This file does only contain a selection of the most common options. For a
|
||||
# full list see the documentation:
|
||||
# http://www.sphinx-doc.org/en/master/config
|
||||
|
||||
# -- Path setup --------------------------------------------------------------
|
||||
|
||||
# If extensions (or modules to document with autodoc) are in another directory,
|
||||
# add these directories to sys.path here. If the directory is relative to the
|
||||
# documentation root, use os.path.abspath to make it absolute, like shown here.
|
||||
#
|
||||
import os
|
||||
import sys
|
||||
sys.path.insert(0, os.path.abspath('../../src'))
|
||||
|
||||
|
||||
# -- Project information -----------------------------------------------------
|
||||
|
||||
project = u'transformers'
|
||||
copyright = u'2020, huggingface'
|
||||
author = u'huggingface'
|
||||
|
||||
# The short X.Y version
|
||||
version = u''
|
||||
# The full version, including alpha/beta/rc tags
|
||||
release = u'3.1.0'
|
||||
|
||||
|
||||
# -- General configuration ---------------------------------------------------
|
||||
|
||||
# If your documentation needs a minimal Sphinx version, state it here.
|
||||
#
|
||||
# needs_sphinx = '1.0'
|
||||
|
||||
# Add any Sphinx extension module names here, as strings. They can be
|
||||
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
|
||||
# ones.
|
||||
extensions = [
|
||||
'sphinx.ext.autodoc',
|
||||
'sphinx.ext.coverage',
|
||||
'sphinx.ext.napoleon',
|
||||
'recommonmark',
|
||||
'sphinx.ext.viewcode',
|
||||
'sphinx_markdown_tables',
|
||||
'sphinx_copybutton'
|
||||
]
|
||||
|
||||
# Add any paths that contain templates here, relative to this directory.
|
||||
templates_path = ['_templates']
|
||||
|
||||
# The suffix(es) of source filenames.
|
||||
# You can specify multiple suffix as a list of string:
|
||||
#
|
||||
source_suffix = ['.rst', '.md']
|
||||
# source_suffix = '.rst'
|
||||
|
||||
# The master toctree document.
|
||||
master_doc = 'index'
|
||||
|
||||
# The language for content autogenerated by Sphinx. Refer to documentation
|
||||
# for a list of supported languages.
|
||||
#
|
||||
# This is also used if you do content translation via gettext catalogs.
|
||||
# Usually you set "language" from the command line for these cases.
|
||||
language = None
|
||||
|
||||
# List of patterns, relative to source directory, that match files and
|
||||
# directories to ignore when looking for source files.
|
||||
# This pattern also affects html_static_path and html_extra_path.
|
||||
exclude_patterns = [u'_build', 'Thumbs.db', '.DS_Store']
|
||||
|
||||
# The name of the Pygments (syntax highlighting) style to use.
|
||||
pygments_style = None
|
||||
|
||||
# Remove the prompt when copying examples
|
||||
copybutton_prompt_text = r">>> |\.\.\. "
|
||||
copybutton_prompt_is_regexp = True
|
||||
|
||||
# -- Options for HTML output -------------------------------------------------
|
||||
|
||||
# The theme to use for HTML and HTML Help pages. See the documentation for
|
||||
# a list of builtin themes.
|
||||
#
|
||||
html_theme = 'sphinx_rtd_theme'
|
||||
|
||||
# Theme options are theme-specific and customize the look and feel of a theme
|
||||
# further. For a list of options available for each theme, see the
|
||||
# documentation.
|
||||
#
|
||||
html_theme_options = {
|
||||
'analytics_id': 'UA-83738774-2'
|
||||
}
|
||||
|
||||
# Add any paths that contain custom static files (such as style sheets) here,
|
||||
# relative to this directory. They are copied after the builtin static files,
|
||||
# so a file named "default.css" will overwrite the builtin "default.css".
|
||||
html_static_path = ['_static']
|
||||
|
||||
# Custom sidebar templates, must be a dictionary that maps document names
|
||||
# to template names.
|
||||
#
|
||||
# The default sidebars (for documents that don't match any pattern) are
|
||||
# defined by theme itself. Builtin themes are using these templates by
|
||||
# default: ``['localtoc.html', 'relations.html', 'sourcelink.html',
|
||||
# 'searchbox.html']``.
|
||||
#
|
||||
# html_sidebars = {}
|
||||
|
||||
# This must be the name of an image file (path relative to the configuration
|
||||
# directory) that is the favicon of the docs. Modern browsers use this as
|
||||
# the icon for tabs, windows and bookmarks. It should be a Windows-style
|
||||
# icon file (.ico).
|
||||
html_favicon = 'favicon.ico'
|
||||
|
||||
|
||||
# -- Options for HTMLHelp output ---------------------------------------------
|
||||
|
||||
# Output file base name for HTML help builder.
|
||||
htmlhelp_basename = 'transformersdoc'
|
||||
|
||||
|
||||
# -- Options for LaTeX output ------------------------------------------------
|
||||
|
||||
latex_elements = {
|
||||
# The paper size ('letterpaper' or 'a4paper').
|
||||
#
|
||||
# 'papersize': 'letterpaper',
|
||||
|
||||
# The font size ('10pt', '11pt' or '12pt').
|
||||
#
|
||||
# 'pointsize': '10pt',
|
||||
|
||||
# Additional stuff for the LaTeX preamble.
|
||||
#
|
||||
# 'preamble': '',
|
||||
|
||||
# Latex figure (float) alignment
|
||||
#
|
||||
# 'figure_align': 'htbp',
|
||||
}
|
||||
|
||||
# Grouping the document tree into LaTeX files. List of tuples
|
||||
# (source start file, target name, title,
|
||||
# author, documentclass [howto, manual, or own class]).
|
||||
latex_documents = [
|
||||
(master_doc, 'transformers.tex', u'transformers Documentation',
|
||||
u'huggingface', 'manual'),
|
||||
]
|
||||
|
||||
|
||||
# -- Options for manual page output ------------------------------------------
|
||||
|
||||
# One entry per manual page. List of tuples
|
||||
# (source start file, name, description, authors, manual section).
|
||||
man_pages = [
|
||||
(master_doc, 'transformers', u'transformers Documentation',
|
||||
[author], 1)
|
||||
]
|
||||
|
||||
|
||||
# -- Options for Texinfo output ----------------------------------------------
|
||||
|
||||
# Grouping the document tree into Texinfo files. List of tuples
|
||||
# (source start file, target name, title, author,
|
||||
# dir menu entry, description, category)
|
||||
texinfo_documents = [
|
||||
(master_doc, 'transformers', u'transformers Documentation',
|
||||
author, 'transformers', 'One line description of project.',
|
||||
'Miscellaneous'),
|
||||
]
|
||||
|
||||
|
||||
# -- Options for Epub output -------------------------------------------------
|
||||
|
||||
# Bibliographic Dublin Core info.
|
||||
epub_title = project
|
||||
|
||||
# The unique identifier of the text. This can be a ISBN number
|
||||
# or the project homepage.
|
||||
#
|
||||
# epub_identifier = ''
|
||||
|
||||
# A unique identification for the text.
|
||||
#
|
||||
# epub_uid = ''
|
||||
|
||||
# A list of files that should not be packed into the epub file.
|
||||
epub_exclude_files = ['search.html']
|
||||
|
||||
def setup(app):
|
||||
app.add_css_file('css/huggingface.css')
|
||||
app.add_css_file('css/code-snippets.css')
|
||||
app.add_js_file('js/custom.js')
|
||||
|
||||
# -- Extension configuration -------------------------------------------------
|
||||
@@ -1 +0,0 @@
|
||||
../../CONTRIBUTING.md
|
||||
@@ -1,133 +0,0 @@
|
||||
Converting Tensorflow Checkpoints
|
||||
================================================
|
||||
|
||||
A command-line interface is provided to convert original Bert/GPT/GPT-2/Transformer-XL/XLNet/XLM checkpoints in models than be loaded using the ``from_pretrained`` methods of the library.
|
||||
|
||||
.. note::
|
||||
Since 2.3.0 the conversion script is now part of the transformers CLI (**transformers-cli**)
|
||||
available in any transformers >= 2.3.0 installation.
|
||||
|
||||
The documentation below reflects the **transformers-cli convert** command format.
|
||||
|
||||
BERT
|
||||
^^^^
|
||||
|
||||
You can convert any TensorFlow checkpoint for BERT (in particular `the pre-trained models released by Google <https://github.com/google-research/bert#pre-trained-models>`_\ ) in a PyTorch save file by using the `convert_bert_original_tf_checkpoint_to_pytorch.py <https://github.com/huggingface/transformers/blob/master/src/transformers/convert_bert_original_tf_checkpoint_to_pytorch.py>`_ script.
|
||||
|
||||
This CLI takes as input a TensorFlow checkpoint (three files starting with ``bert_model.ckpt``\ ) and the associated configuration file (\ ``bert_config.json``\ ), and creates a PyTorch model for this configuration, loads the weights from the TensorFlow checkpoint in the PyTorch model and saves the resulting model in a standard PyTorch save file that can be imported using ``torch.load()`` (see examples in `run_bert_extract_features.py <https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/examples/run_bert_extract_features.py>`_\ , `run_bert_classifier.py <https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/examples/run_bert_classifier.py>`_ and `run_bert_squad.py <https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/examples/run_bert_squad.py>`_\ ).
|
||||
|
||||
You only need to run this conversion script **once** to get a PyTorch model. You can then disregard the TensorFlow checkpoint (the three files starting with ``bert_model.ckpt``\ ) but be sure to keep the configuration file (\ ``bert_config.json``\ ) and the vocabulary file (\ ``vocab.txt``\ ) as these are needed for the PyTorch model too.
|
||||
|
||||
To run this specific conversion script you will need to have TensorFlow and PyTorch installed (\ ``pip install tensorflow``\ ). The rest of the repository only requires PyTorch.
|
||||
|
||||
Here is an example of the conversion process for a pre-trained ``BERT-Base Uncased`` model:
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
export BERT_BASE_DIR=/path/to/bert/uncased_L-12_H-768_A-12
|
||||
|
||||
transformers-cli convert --model_type bert \
|
||||
--tf_checkpoint $BERT_BASE_DIR/bert_model.ckpt \
|
||||
--config $BERT_BASE_DIR/bert_config.json \
|
||||
--pytorch_dump_output $BERT_BASE_DIR/pytorch_model.bin
|
||||
|
||||
You can download Google's pre-trained models for the conversion `here <https://github.com/google-research/bert#pre-trained-models>`__.
|
||||
|
||||
ALBERT
|
||||
^^^^^^
|
||||
|
||||
Convert TensorFlow model checkpoints of ALBERT to PyTorch using the `convert_albert_original_tf_checkpoint_to_pytorch.py <https://github.com/huggingface/transformers/blob/master/src/transformers/convert_bert_original_tf_checkpoint_to_pytorch.py>`_ script.
|
||||
|
||||
The CLI takes as input a TensorFlow checkpoint (three files starting with ``model.ckpt-best``\ ) and the accompanying configuration file (\ ``albert_config.json``\ ), then creates and saves a PyTorch model. To run this conversion you will need to have TensorFlow and PyTorch installed.
|
||||
|
||||
Here is an example of the conversion process for the pre-trained ``ALBERT Base`` model:
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
export ALBERT_BASE_DIR=/path/to/albert/albert_base
|
||||
|
||||
transformers-cli convert --model_type albert \
|
||||
--tf_checkpoint $ALBERT_BASE_DIR/model.ckpt-best \
|
||||
--config $ALBERT_BASE_DIR/albert_config.json \
|
||||
--pytorch_dump_output $ALBERT_BASE_DIR/pytorch_model.bin
|
||||
|
||||
You can download Google's pre-trained models for the conversion `here <https://github.com/google-research/albert#pre-trained-models>`__.
|
||||
|
||||
OpenAI GPT
|
||||
^^^^^^^^^^
|
||||
|
||||
Here is an example of the conversion process for a pre-trained OpenAI GPT model, assuming that your NumPy checkpoint save as the same format than OpenAI pretrained model (see `here <https://github.com/openai/finetune-transformer-lm>`__\ )
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
export OPENAI_GPT_CHECKPOINT_FOLDER_PATH=/path/to/openai/pretrained/numpy/weights
|
||||
|
||||
transformers-cli convert --model_type gpt \
|
||||
--tf_checkpoint $OPENAI_GPT_CHECKPOINT_FOLDER_PATH \
|
||||
--pytorch_dump_output $PYTORCH_DUMP_OUTPUT \
|
||||
[--config OPENAI_GPT_CONFIG] \
|
||||
[--finetuning_task_name OPENAI_GPT_FINETUNED_TASK] \
|
||||
|
||||
|
||||
OpenAI GPT-2
|
||||
^^^^^^^^^^^^
|
||||
|
||||
Here is an example of the conversion process for a pre-trained OpenAI GPT-2 model (see `here <https://github.com/openai/gpt-2>`__\ )
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
export OPENAI_GPT2_CHECKPOINT_PATH=/path/to/gpt2/pretrained/weights
|
||||
|
||||
transformers-cli convert --model_type gpt2 \
|
||||
--tf_checkpoint $OPENAI_GPT2_CHECKPOINT_PATH \
|
||||
--pytorch_dump_output $PYTORCH_DUMP_OUTPUT \
|
||||
[--config OPENAI_GPT2_CONFIG] \
|
||||
[--finetuning_task_name OPENAI_GPT2_FINETUNED_TASK]
|
||||
|
||||
Transformer-XL
|
||||
^^^^^^^^^^^^^^
|
||||
|
||||
Here is an example of the conversion process for a pre-trained Transformer-XL model (see `here <https://github.com/kimiyoung/transformer-xl/tree/master/tf#obtain-and-evaluate-pretrained-sota-models>`__\ )
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
export TRANSFO_XL_CHECKPOINT_FOLDER_PATH=/path/to/transfo/xl/checkpoint
|
||||
|
||||
transformers-cli convert --model_type transfo_xl \
|
||||
--tf_checkpoint $TRANSFO_XL_CHECKPOINT_FOLDER_PATH \
|
||||
--pytorch_dump_output $PYTORCH_DUMP_OUTPUT \
|
||||
[--config TRANSFO_XL_CONFIG] \
|
||||
[--finetuning_task_name TRANSFO_XL_FINETUNED_TASK]
|
||||
|
||||
|
||||
XLNet
|
||||
^^^^^
|
||||
|
||||
Here is an example of the conversion process for a pre-trained XLNet model:
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
export TRANSFO_XL_CHECKPOINT_PATH=/path/to/xlnet/checkpoint
|
||||
export TRANSFO_XL_CONFIG_PATH=/path/to/xlnet/config
|
||||
|
||||
transformers-cli convert --model_type xlnet \
|
||||
--tf_checkpoint $TRANSFO_XL_CHECKPOINT_PATH \
|
||||
--config $TRANSFO_XL_CONFIG_PATH \
|
||||
--pytorch_dump_output $PYTORCH_DUMP_OUTPUT \
|
||||
[--finetuning_task_name XLNET_FINETUNED_TASK] \
|
||||
|
||||
|
||||
XLM
|
||||
^^^
|
||||
|
||||
Here is an example of the conversion process for a pre-trained XLM model:
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
export XLM_CHECKPOINT_PATH=/path/to/xlm/checkpoint
|
||||
|
||||
transformers-cli convert --model_type xlm \
|
||||
--tf_checkpoint $XLM_CHECKPOINT_PATH \
|
||||
--pytorch_dump_output $PYTORCH_DUMP_OUTPUT
|
||||
[--config XML_CONFIG] \
|
||||
[--finetuning_task_name XML_FINETUNED_TASK]
|
||||
@@ -1,715 +0,0 @@
|
||||
Fine-tuning with custom datasets
|
||||
================================
|
||||
|
||||
.. note::
|
||||
|
||||
The datasets used in this tutorial are available and can be more easily accessed using the
|
||||
`🤗 NLP library <https://github.com/huggingface/nlp>`_. We do not use this library to access the datasets here
|
||||
since this tutorial meant to illustrate how to work with your own data. A brief of introduction can be found
|
||||
at the end of the tutorial in the section ":ref:`nlplib`".
|
||||
|
||||
This tutorial will take you through several examples of using 🤗 Transformers models with your own datasets. The
|
||||
guide shows one of many valid workflows for using these models and is meant to be illustrative rather than
|
||||
definitive. We show examples of reading in several data formats, preprocessing the data for several types of tasks,
|
||||
and then preparing the data into PyTorch/TensorFlow ``Dataset`` objects which can easily be used either with
|
||||
:class:`~transformers.Trainer`/:class:`~transformers.TFTrainer` or with native PyTorch/TensorFlow.
|
||||
|
||||
We include several examples, each of which demonstrates a different type of common downstream task:
|
||||
|
||||
- :ref:`seq_imdb`
|
||||
- :ref:`tok_ner`
|
||||
- :ref:`qa_squad`
|
||||
- :ref:`resources`
|
||||
|
||||
.. _seq_imdb:
|
||||
|
||||
Sequence Classification with IMDb Reviews
|
||||
-----------------------------------------
|
||||
|
||||
.. note::
|
||||
|
||||
This dataset can be explored in the Hugging Face model hub (`IMDb <https://huggingface.co/datasets/imdb>`_), and can
|
||||
be alternatively downloaded with the 🤗 NLP library with ``load_dataset("imdb")``.
|
||||
|
||||
In this example, we'll show how to download, tokenize, and train a model on the IMDb reviews dataset. This task
|
||||
takes the text of a review and requires the model to predict whether the sentiment of the review is positive or
|
||||
negative. Let's start by downloading the dataset from the
|
||||
`Large Movie Review Dataset <http://ai.stanford.edu/~amaas/data/sentiment/>`_ webpage.
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
wget http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz
|
||||
tar -xf aclImdb_v1.tar.gz
|
||||
|
||||
This data is organized into ``pos`` and ``neg`` folders with one text file per example. Let's write a function that can
|
||||
read this in.
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
from pathlib import Path
|
||||
|
||||
def read_imdb_split(split_dir):
|
||||
split_dir = Path(split_dir)
|
||||
texts = []
|
||||
labels = []
|
||||
for label_dir in ["pos", "neg"]:
|
||||
for text_file in (split_dir/label_dir).iterdir():
|
||||
texts.append(text_file.read_text())
|
||||
labels.append(0 if label_dir is "neg" else 1)
|
||||
|
||||
return texts, labels
|
||||
|
||||
train_texts, train_labels = read_imdb_split('aclImdb/train')
|
||||
test_texts, test_labels = read_imdb_split('aclImdb/test')
|
||||
|
||||
We now have a train and test dataset, but let's also also create a validation set which we can use for for
|
||||
evaluation and tuning without training our test set results. Sklearn has a convenient utility for creating such
|
||||
splits:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
from sklearn.model_selection import train_test_split
|
||||
train_texts, val_texts, train_labels, val_labels = train_test_split(train_texts, train_labels, test_size=.2)
|
||||
|
||||
Alright, we've read in our dataset. Now let's tackle tokenization. We'll eventually train a classifier using
|
||||
pre-trained DistilBert, so let's use the DistilBert tokenizer.
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
from transformers import DistilBertTokenizerFast
|
||||
tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-uncased')
|
||||
|
||||
Now we can simply pass our texts to the tokenizer. We'll pass ``truncation=True`` and ``padding=True``, which will
|
||||
ensure that all of our sequences are padded to the same length and are truncated to be no longer model's maximum
|
||||
input length. This will allow us to feed batches of sequences into the model at the same time.
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
train_encodings = tokenizer(train_texts, truncation=True, padding=True)
|
||||
val_encodings = tokenizer(val_texts, truncation=True, padding=True)
|
||||
test_encodings = tokenizer(test_texts, truncation=True, padding=True)
|
||||
|
||||
Now, let's turn our labels and encodings into a Dataset object. In PyTorch, this is done by subclassing a
|
||||
``torch.utils.data.Dataset`` object and implementing ``__len__`` and ``__getitem__``. In TensorFlow, we pass our input encodings and
|
||||
labels to the ``from_tensor_slices`` constructor method. We put the data in this format so that the data can be
|
||||
easily batched such that each key in the batch encoding corresponds to a named parameter of the
|
||||
:meth:`~transformers.DistilBertForSequenceClassification.forward` method of the model we will train.
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
## PYTORCH CODE
|
||||
import torch
|
||||
|
||||
class IMDbDataset(torch.utils.data.Dataset):
|
||||
def __init__(self, encodings, labels):
|
||||
self.encodings = encodings
|
||||
self.labels = labels
|
||||
|
||||
def __getitem__(self, idx):
|
||||
item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
|
||||
item['labels'] = torch.tensor(self.labels[idx])
|
||||
return item
|
||||
|
||||
def __len__(self):
|
||||
return len(self.labels)
|
||||
|
||||
train_dataset = IMDbDataset(train_encodings, train_labels)
|
||||
val_dataset = IMDbDataset(val_encodings, val_labels)
|
||||
test_dataset = IMDbDataset(test_encodings, test_labels)
|
||||
## TENSORFLOW CODE
|
||||
import tensorflow as tf
|
||||
|
||||
train_dataset = tf.data.Dataset.from_tensor_slices((
|
||||
dict(train_encodings),
|
||||
train_labels
|
||||
))
|
||||
val_dataset = tf.data.Dataset.from_tensor_slices((
|
||||
dict(val_encodings),
|
||||
val_labels
|
||||
))
|
||||
test_dataset = tf.data.Dataset.from_tensor_slices((
|
||||
dict(test_encodings),
|
||||
test_labels
|
||||
))
|
||||
|
||||
Now that our datasets our ready, we can fine-tune a model either with the 🤗
|
||||
:class:`~transformers.Trainer`/:class:`~transformers.TFTrainer` or with native PyTorch/TensorFlow. See
|
||||
:doc:`training <training>`.
|
||||
|
||||
.. _ft_trainer:
|
||||
|
||||
Fine-tuning with Trainer
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
The steps above prepared the datasets in the way that the trainer is expected. Now all we need to do is create a
|
||||
model to fine-tune, define the :class:`~transformers.TrainingArguments`/:class:`~transformers.TFTrainingArguments`
|
||||
and instantiate a :class:`~transformers.Trainer`/:class:`~transformers.TFTrainer`.
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
## PYTORCH CODE
|
||||
from transformers import DistilBertForSequenceClassification, Trainer, TrainingArguments
|
||||
|
||||
training_args = TrainingArguments(
|
||||
output_dir='./results', # output directory
|
||||
num_train_epochs=3, # total number of training epochs
|
||||
per_device_train_batch_size=16, # batch size per device during training
|
||||
per_device_eval_batch_size=64, # batch size for evaluation
|
||||
warmup_steps=500, # number of warmup steps for learning rate scheduler
|
||||
weight_decay=0.01, # strength of weight decay
|
||||
logging_dir='./logs', # directory for storing logs
|
||||
logging_steps=10,
|
||||
)
|
||||
|
||||
model = DistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased")
|
||||
|
||||
trainer = Trainer(
|
||||
model=model, # the instantiated 🤗 Transformers model to be trained
|
||||
args=training_args, # training arguments, defined above
|
||||
train_dataset=train_dataset, # training dataset
|
||||
eval_dataset=val_dataset # evaluation dataset
|
||||
)
|
||||
|
||||
trainer.train()
|
||||
## TENSORFLOW CODE
|
||||
from transformers import TFDistilBertForSequenceClassification, TFTrainer, TFTrainingArguments
|
||||
|
||||
training_args = TFTrainingArguments(
|
||||
output_dir='./results', # output directory
|
||||
num_train_epochs=3, # total number of training epochs
|
||||
per_device_train_batch_size=16, # batch size per device during training
|
||||
per_device_eval_batch_size=64, # batch size for evaluation
|
||||
warmup_steps=500, # number of warmup steps for learning rate scheduler
|
||||
weight_decay=0.01, # strength of weight decay
|
||||
logging_dir='./logs', # directory for storing logs
|
||||
logging_steps=10,
|
||||
)
|
||||
|
||||
with training_args.strategy.scope():
|
||||
model = TFDistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased")
|
||||
|
||||
trainer = TFTrainer(
|
||||
model=model, # the instantiated 🤗 Transformers model to be trained
|
||||
args=training_args, # training arguments, defined above
|
||||
train_dataset=train_dataset, # training dataset
|
||||
eval_dataset=val_dataset # evaluation dataset
|
||||
)
|
||||
|
||||
trainer.train()
|
||||
|
||||
.. _ft_native:
|
||||
|
||||
Fine-tuning with native PyTorch/TensorFlow
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
We can also train use native PyTorch or TensorFlow:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
## PYTORCH CODE
|
||||
from torch.utils.data import DataLoader
|
||||
from transformers import DistilBertForSequenceClassification, AdamW
|
||||
|
||||
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
|
||||
|
||||
model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased')
|
||||
model.to(device)
|
||||
model.train()
|
||||
|
||||
train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)
|
||||
|
||||
optim = AdamW(model.parameters(), lr=5e-5)
|
||||
|
||||
for epoch in range(3):
|
||||
for batch in train_loader:
|
||||
optim.zero_grad()
|
||||
input_ids = batch['input_ids'].to(device)
|
||||
attention_mask = batch['attention_mask'].to(device)
|
||||
labels = batch['labels'].to(device)
|
||||
outputs = model(input_ids, attention_mask=attention_mask, labels=labels)
|
||||
loss = outputs[0]
|
||||
loss.backward()
|
||||
optim.step()
|
||||
|
||||
model.eval()
|
||||
## TENSORFLOW CODE
|
||||
from transformers import TFDistilBertForSequenceClassification
|
||||
|
||||
model = TFDistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased')
|
||||
|
||||
optimizer = tf.keras.optimizers.Adam(learning_rate=5e-5)
|
||||
model.compile(optimizer=optimizer, loss=model.compute_loss) # can also use any keras loss fn
|
||||
model.fit(train_dataset.shuffle(1000).batch(16), epochs=3, batch_size=16)
|
||||
|
||||
.. _tok_ner:
|
||||
|
||||
Token Classification with W-NUT Emerging Entities
|
||||
-------------------------------------------------
|
||||
|
||||
.. note::
|
||||
|
||||
This dataset can be explored in the Hugging Face model hub (`WNUT-17 <https://huggingface.co/datasets/wnut_17>`_), and can
|
||||
be alternatively downloaded with the 🤗 NLP library with ``load_dataset("wnut_17")``.
|
||||
|
||||
Next we will look at token classification. Rather than classifying an entire sequence, this task classifies token by
|
||||
token. We'll demonstrate how to do this with
|
||||
`Named Entity Recognition <http://nlpprogress.com/english/named_entity_recognition.html>`_, which involves
|
||||
identifying tokens which correspond to a predefined set of "entities". Specifically, we'll use the
|
||||
`W-NUT Emerging and Rare entities <http://noisy-text.github.io/2017/emerging-rare-entities.html>`_ corpus. The data
|
||||
is given as a collection of pre-tokenized documents where each token is assigned a tag.
|
||||
|
||||
Let's start by downloading the data.
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
wget http://noisy-text.github.io/2017/files/wnut17train.conll
|
||||
|
||||
In this case, we'll just download the train set, which is a single text file. Each line of the file contains either
|
||||
(1) a word and tag separated by a tab, or (2) a blank line indicating the end of a document. Let's write a
|
||||
function to read this in. We'll take in the file path and return ``token_docs`` which is a list of lists of token
|
||||
strings, and ``token_tags`` which is a list of lists of tag strings.
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
from pathlib import Path
|
||||
import re
|
||||
|
||||
def read_wnut(file_path):
|
||||
file_path = Path(file_path)
|
||||
|
||||
raw_text = file_path.read_text().strip()
|
||||
raw_docs = re.split(r'\n\t?\n', raw_text)
|
||||
token_docs = []
|
||||
tag_docs = []
|
||||
for doc in raw_docs:
|
||||
tokens = []
|
||||
tags = []
|
||||
for line in doc.split('\n'):
|
||||
token, tag = line.split('\t')
|
||||
tokens.append(token)
|
||||
tags.append(tag)
|
||||
token_docs.append(tokens)
|
||||
tag_docs.append(tags)
|
||||
|
||||
return token_docs, tag_docs
|
||||
|
||||
texts, tags = read_wnut('wnut17train.conll')
|
||||
|
||||
Just to see what this data looks like, let's take a look at a segment of the first document.
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
>>> print(texts[0][10:17], tags[0][10:17], sep='\n')
|
||||
['for', 'two', 'weeks', '.', 'Empire', 'State', 'Building']
|
||||
['O', 'O', 'O', 'O', 'B-location', 'I-location', 'I-location']
|
||||
|
||||
``location`` is an entity type, ``B-`` indicates the beginning of an entity, and ``I-`` indicates consecutive positions of
|
||||
the same entity ("Empire State Building" is considered one entity). ``O`` indicates the token does not correspond to
|
||||
any entity.
|
||||
|
||||
Now that we've read the data in, let's create a train/validation split:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
from sklearn.model_selection import train_test_split
|
||||
train_texts, val_texts, train_tags, val_tags = train_test_split(texts, tags, test_size=.2)
|
||||
|
||||
Next, let's create encodings for our tokens and tags. For the tags, we can start by just create a simple mapping
|
||||
which we'll use in a moment:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
unique_tags = set(tag for doc in tags for tag in doc)
|
||||
tag2id = {tag: id for id, tag in enumerate(unique_tags)}
|
||||
id2tag = {id: tag for tag, id in tag2id.items()}
|
||||
|
||||
To encode the tokens, we'll use a pre-trained DistilBert tokenizer. We can tell the tokenizer that we're dealing
|
||||
with ready-split tokens rather than full sentence strings by passing ``is_pretokenized=True``. We'll also pass
|
||||
``padding=True`` and ``truncation=True`` to pad the sequences to be the same length. Lastly, we can tell the model
|
||||
to return information about the tokens which are split by the wordpiece tokenization process, which we will need in
|
||||
a moment.
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
from transformers import DistilBertTokenizerFast
|
||||
tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-cased')
|
||||
train_encodings = tokenizer(train_texts, is_pretokenized=True, return_offsets_mapping=True, padding=True, truncation=True)
|
||||
val_encodings = tokenizer(val_texts, is_pretokenized=True, return_offsets_mapping=True, padding=True, truncation=True)
|
||||
|
||||
Great, so now our tokens are nicely encoded in the format that they need to be in to feed them into our DistilBert
|
||||
model below.
|
||||
|
||||
Now we arrive at a common obstacle with using pre-trained models for token-level classification: many of the tokens
|
||||
in the W-NUT corpus are not in DistilBert's vocabulary. Bert and many models like it use a method called WordPiece
|
||||
Tokenization, meaning that single words are split into multiple tokens such that each token is likely to be in
|
||||
the vocabulary. For example, DistilBert's tokenizer would split the Twitter handle ``@huggingface`` into the tokens
|
||||
``['@', 'hugging', '##face']``. This is a problem for us because we have exactly one tag per token. If the tokenizer
|
||||
splits a token into multiple sub-tokens, then we will end up with a mismatch between our tokens and our labels.
|
||||
|
||||
One way to handle this is to only train on the tag labels for the first subtoken of a split token. We can do this in
|
||||
🤗 Transformers by setting the labels we wish to ignore to ``-100``. In the example above, if the label for
|
||||
``@HuggingFace`` is ``3`` (indexing ``B-corporation``), we would set the labels of ``['@', 'hugging', '##face']`` to
|
||||
``[3, -100, -100]``.
|
||||
|
||||
Let's write a function to do this. This is where we will use the ``offset_mapping`` from the tokenizer as mentioned
|
||||
above. For each sub-token returned by the tokenizer, the offset mapping gives us a tuple indicating the sub-token's
|
||||
start position and end position relative to the original token it was split from. That means that if the first
|
||||
position in the tuple is anything other than ``0``, we will set its corresponding label to ``-100``. While we're at
|
||||
it, we can also set labels to ``-100`` if the second position of the offset mapping is ``0``, since this means it must
|
||||
be a special token like ``[PAD]`` or ``[CLS]``.
|
||||
|
||||
.. note::
|
||||
|
||||
Due to a recently fixed bug, -1 must be used instead of -100 when using TensorFlow in 🤗 Transformers <= 3.02.
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
import numpy as np
|
||||
|
||||
def encode_tags(tags, encodings):
|
||||
labels = [[tag2id[tag] for tag in doc] for doc in tags]
|
||||
encoded_labels = []
|
||||
for doc_labels, doc_offset in zip(labels, encodings.offset_mapping):
|
||||
# create an empty array of -100
|
||||
doc_enc_labels = np.ones(len(doc_offset),dtype=int) * -100
|
||||
arr_offset = np.array(doc_offset)
|
||||
|
||||
# set labels whose first offset position is 0 and the second is not 0
|
||||
doc_enc_labels[(arr_offset[:,0] == 0) & (arr_offset[:,1] != 0)] = doc_labels
|
||||
encoded_labels.append(doc_enc_labels.tolist())
|
||||
|
||||
return encoded_labels
|
||||
|
||||
train_labels = encode_tags(train_tags, train_encodings)
|
||||
val_labels = encode_tags(val_tags, val_encodings)
|
||||
|
||||
The hard part is now done. Just as in the sequence classification example above, we can create a dataset object:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
## PYTORCH CODE
|
||||
import torch
|
||||
|
||||
class WNUTDataset(torch.utils.data.Dataset):
|
||||
def __init__(self, encodings, labels):
|
||||
self.encodings = encodings
|
||||
self.labels = labels
|
||||
|
||||
def __getitem__(self, idx):
|
||||
item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
|
||||
item['labels'] = torch.tensor(self.labels[idx])
|
||||
return item
|
||||
|
||||
def __len__(self):
|
||||
return len(self.labels)
|
||||
|
||||
train_encodings.pop("offset_mapping") # we don't want to pass this to the model
|
||||
val_encodings.pop("offset_mapping")
|
||||
train_dataset = WNUTDataset(train_encodings, train_labels)
|
||||
val_dataset = WNUTDataset(val_encodings, val_labels)
|
||||
## TENSORFLOW CODE
|
||||
import tensorflow as tf
|
||||
|
||||
train_encodings.pop("offset_mapping") # we don't want to pass this to the model
|
||||
val_encodings.pop("offset_mapping")
|
||||
|
||||
train_dataset = tf.data.Dataset.from_tensor_slices((
|
||||
dict(train_encodings),
|
||||
train_labels
|
||||
))
|
||||
val_dataset = tf.data.Dataset.from_tensor_slices((
|
||||
dict(val_encodings),
|
||||
val_labels
|
||||
))
|
||||
|
||||
Now load in a token classification model and specify the number of labels:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
## PYTORCH CODE
|
||||
from transformers import DistilBertForTokenClassification
|
||||
model = DistilBertForTokenClassification.from_pretrained('distilbert-base-cased', num_labels=len(unique_tags))
|
||||
## TENSORFLOW CODE
|
||||
from transformers import TFDistilBertForTokenClassification
|
||||
model = TFDistilBertForTokenClassification.from_pretrained('distilbert-base-cased', num_labels=len(unique_tags))
|
||||
|
||||
The data and model are both ready to go. You can train the model either with
|
||||
:class:`~transformers.Trainer`/:class:`~transformers.TFTrainer` or with native PyTorch/TensorFlow, exactly as in the
|
||||
sequence classification example above.
|
||||
|
||||
- :ref:`ft_trainer`
|
||||
- :ref:`ft_native`
|
||||
|
||||
.. _qa_squad:
|
||||
|
||||
Question Answering with SQuAD 2.0
|
||||
---------------------------------
|
||||
|
||||
.. note::
|
||||
|
||||
This dataset can be explored in the Hugging Face model hub (`SQuAD V2 <https://huggingface.co/datasets/squad_v2>`_), and can
|
||||
be alternatively downloaded with the 🤗 NLP library with ``load_dataset("squad_v2")``.
|
||||
|
||||
Question answering comes in many forms. In this example, we'll look at the particular type of extractive QA that
|
||||
involves answering a question about a passage by highlighting the segment of the passage that answers the question.
|
||||
This involves fine-tuning a model which predicts a start position and an end position in the passage. We will use the
|
||||
`Stanford Question Answering Dataset (SQuAD) 2.0 <https://rajpurkar.github.io/SQuAD-explorer/>`_.
|
||||
|
||||
We will start by downloading the data:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
mkdir squad
|
||||
wget https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v2.0.json -O squad/train-v2.0.json
|
||||
wget https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v2.0.json -O squad/dev-v2.0.json
|
||||
|
||||
Each split is in a structured json file with a number of questions and answers for each passage (or context). We'll
|
||||
take this apart into parallel lists of contexts, questions, and answers (note that the contexts here are repeated
|
||||
since there are multiple questions per context):
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
import json
|
||||
from pathlib import Path
|
||||
|
||||
def read_squad(path):
|
||||
path = Path(path)
|
||||
with open(path, 'rb') as f:
|
||||
squad_dict = json.load(f)
|
||||
|
||||
contexts = []
|
||||
questions = []
|
||||
answers = []
|
||||
for group in squad_dict['data']:
|
||||
for passage in group['paragraphs']:
|
||||
context = passage['context']
|
||||
for qa in passage['qas']:
|
||||
question = qa['question']
|
||||
for answer in qa['answers']:
|
||||
contexts.append(context)
|
||||
questions.append(question)
|
||||
answers.append(answer)
|
||||
|
||||
return contexts, questions, answers
|
||||
|
||||
train_contexts, train_questions, train_answers = read_squad('squad/train-v2.0.json')
|
||||
val_contexts, val_questions, val_answers = read_squad('squad/dev-v2.0.json')
|
||||
|
||||
The contexts and questions are just strings. The answers are dicts containing the subsequence of the passage with
|
||||
the correct answer as well as an integer indicating the character at which the answer begins. In order to train a
|
||||
model on this data we need (1) the tokenized context/question pairs, and (2) integers indicating at which *token*
|
||||
positions the answer begins and ends.
|
||||
|
||||
First, let's get the *character* position at which the answer ends in the passage (we are given the starting
|
||||
position). Sometimes SQuAD answers are off by one or two characters, so we will also adjust for that.
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
def add_end_idx(answers, contexts):
|
||||
for answer, context in zip(answers, contexts):
|
||||
gold_text = answer['text']
|
||||
start_idx = answer['answer_start']
|
||||
end_idx = start_idx + len(gold_text)
|
||||
|
||||
# sometimes squad answers are off by a character or two – fix this
|
||||
if context[start_idx:end_idx] == gold_text:
|
||||
answer['answer_end'] = end_idx
|
||||
elif context[start_idx-1:end_idx-1] == gold_text:
|
||||
answer['answer_start'] = start_idx - 1
|
||||
answer['answer_end'] = end_idx - 1 # When the gold label is off by one character
|
||||
elif context[start_idx-2:end_idx-2] == gold_text:
|
||||
answer['answer_start'] = start_idx - 2
|
||||
answer['answer_end'] = end_idx - 2 # When the gold label is off by two characters
|
||||
|
||||
add_end_idx(train_answers, train_contexts)
|
||||
add_end_idx(val_answers, val_contexts)
|
||||
|
||||
Now ``train_answers`` and ``val_answers`` include the character end positions and the corrected start positions.
|
||||
Next, let's tokenize our context/question pairs. 🤗 Tokenizers can accept parallel lists of sequences and encode
|
||||
them together as sequence pairs.
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
from transformers import DistilBertTokenizerFast
|
||||
tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-uncased')
|
||||
|
||||
train_encodings = tokenizer(train_contexts, train_questions, truncation=True, padding=True)
|
||||
val_encodings = tokenizer(val_contexts, val_questions, truncation=True, padding=True)
|
||||
|
||||
Next we need to convert our character start/end positions to token start/end positions. When using 🤗 Fast
|
||||
Tokenizers, we can use the built in :func:`~transformers.BatchEncoding.char_to_token` method.
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
def add_token_positions(encodings, answers):
|
||||
start_positions = []
|
||||
end_positions = []
|
||||
for i in range(len(answers)):
|
||||
start_positions.append(encodings.char_to_token(i, answers[i]['answer_start']))
|
||||
end_positions.append(encodings.char_to_token(i, answers[i]['answer_end'] - 1))
|
||||
# if None, the answer passage has been truncated
|
||||
if start_positions[-1] is None:
|
||||
start_positions[-1] = tokenizer.model_max_length
|
||||
if end_positions[-1] is None:
|
||||
end_positions[-1] = tokenizer.model_max_length
|
||||
encodings.update({'start_positions': start_positions, 'end_positions': end_positions})
|
||||
|
||||
add_token_positions(train_encodings, train_answers)
|
||||
add_token_positions(val_encodings, val_answers)
|
||||
|
||||
Our data is ready. Let's just put it in a PyTorch/TensorFlow dataset so that we can easily use it for
|
||||
training. In PyTorch, we define a custom ``Dataset`` class. In TensorFlow, we pass a tuple of
|
||||
``(inputs_dict, labels_dict)`` to the ``from_tensor_slices`` method.
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
## PYTORCH CODE
|
||||
import torch
|
||||
|
||||
class SquadDataset(torch.utils.data.Dataset):
|
||||
def __init__(self, encodings):
|
||||
self.encodings = encodings
|
||||
|
||||
def __getitem__(self, idx):
|
||||
return {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
|
||||
|
||||
def __len__(self):
|
||||
return len(self.encodings.input_ids)
|
||||
|
||||
train_dataset = SquadDataset(train_encodings)
|
||||
val_dataset = SquadDataset(val_encodings)
|
||||
## TENSORFLOW CODE
|
||||
import tensorflow as tf
|
||||
|
||||
train_dataset = tf.data.Dataset.from_tensor_slices((
|
||||
{key: train_encodings[key] for key in ['input_ids', 'attention_mask']},
|
||||
{key: train_encodings[key] for key in ['start_positions', 'end_positions']}
|
||||
))
|
||||
val_dataset = tf.data.Dataset.from_tensor_slices((
|
||||
{key: val_encodings[key] for key in ['input_ids', 'attention_mask']},
|
||||
{key: val_encodings[key] for key in ['start_positions', 'end_positions']}
|
||||
))
|
||||
|
||||
Now we can use a DistilBert model with a QA head for training:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
## PYTORCH CODE
|
||||
from transformers import DistilBertForQuestionAnswering
|
||||
model = DistilBertForQuestionAnswering.from_pretrained("distilbert-base-uncased")
|
||||
## TENSORFLOW CODE
|
||||
from transformers import TFDistilBertForQuestionAnswering
|
||||
model = TFDistilBertForQuestionAnswering.from_pretrained("distilbert-base-uncased")
|
||||
|
||||
|
||||
The data and model are both ready to go. You can train the model with
|
||||
:class:`~transformers.Trainer`/:class:`~transformers.TFTrainer` exactly as in the sequence classification example
|
||||
above. If using native PyTorch, replace ``labels`` with ``start_positions`` and ``end_positions`` in the training
|
||||
example. If using Keras's ``fit``, we need to make a minor modification to handle this example since it involves
|
||||
multiple model outputs.
|
||||
|
||||
- :ref:`ft_trainer`
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
## PYTORCH CODE
|
||||
from torch.utils.data import DataLoader
|
||||
from transformers import AdamW
|
||||
|
||||
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
|
||||
|
||||
model.to(device)
|
||||
model.train()
|
||||
|
||||
train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)
|
||||
|
||||
optim = AdamW(model.parameters(), lr=5e-5)
|
||||
|
||||
for epoch in range(3):
|
||||
for batch in train_loader:
|
||||
optim.zero_grad()
|
||||
input_ids = batch['input_ids'].to(device)
|
||||
attention_mask = batch['attention_mask'].to(device)
|
||||
start_positions = batch['start_positions'].to(device)
|
||||
end_positions = batch['end_positions'].to(device)
|
||||
outputs = model(input_ids, attention_mask=attention_mask, start_positions=start_positions, end_positions=end_positions)
|
||||
loss = outputs[0]
|
||||
loss.backward()
|
||||
optim.step()
|
||||
|
||||
model.eval()
|
||||
## TENSORFLOW CODE
|
||||
# Keras will expect a tuple when dealing with labels
|
||||
train_dataset = train_dataset.map(lambda x, y: (x, (y['start_positions'], y['end_positions'])))
|
||||
|
||||
# Keras will assign a separate loss for each output and add them together. So we'll just use the standard CE loss
|
||||
# instead of using the built-in model.compute_loss, which expects a dict of outputs and averages the two terms.
|
||||
# Note that this means the loss will be 2x of when using TFTrainer since we're adding instead of averaging them.
|
||||
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
|
||||
model.distilbert.return_dict = False # if using 🤗 Transformers >3.02, make sure outputs are tuples
|
||||
|
||||
optimizer = tf.keras.optimizers.Adam(learning_rate=5e-5)
|
||||
model.compile(optimizer=optimizer, loss=loss) # can also use any keras loss fn
|
||||
model.fit(train_dataset.shuffle(1000).batch(16), epochs=3, batch_size=16)
|
||||
|
||||
.. _resources:
|
||||
|
||||
Additional Resources
|
||||
--------------------
|
||||
|
||||
- `How to train a new language model from scratch using Transformers and Tokenizers
|
||||
<https://huggingface.co/blog/how-to-train>`_. Blog post showing the steps to load in Esperanto data and train a
|
||||
masked language model from scratch.
|
||||
- :doc:`Preprocessing <preprocessing>`. Docs page on data preprocessing.
|
||||
- :doc:`Training <training>`. Docs page on training and fine-tuning.
|
||||
|
||||
.. _nlplib:
|
||||
|
||||
Using the 🤗 NLP Datasets & Metrics library
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
This tutorial demonstrates how to read in datasets from various raw text formats and prepare them for training with
|
||||
🤗 Transformers so that you can do the same thing with your own custom datasets. However, we recommend users use the
|
||||
`🤗 NLP library <https://github.com/huggingface/nlp>`_ for working with the 150+ datasets included in the
|
||||
`hub <https://huggingface.co/datasets>`_, including the three datasets used in this tutorial. As a very brief overview,
|
||||
we will show how to use the NLP library to download and prepare the IMDb dataset from the first example,
|
||||
:ref:`seq_imdb`.
|
||||
|
||||
Start by downloading the dataset:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
from nlp import load_dataset
|
||||
train = load_dataset("imdb", split="train")
|
||||
|
||||
Each dataset has multiple columns corresponding to different features. Let's see what our columns are.
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
>>> print(train.column_names)
|
||||
['label', 'text']
|
||||
|
||||
Great. Now let's tokenize the text. We can do this using the ``map`` method. We'll also rename the ``label`` column
|
||||
to ``labels`` to match the model's input arguments.
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
train = train.map(lambda batch: tokenizer(batch["text"], truncation=True, padding=True), batched=True)
|
||||
train.rename_column_("label", "labels")
|
||||
|
||||
Lastly, we can use the ``set_format`` method to determine which columns and in what data format we want to access
|
||||
dataset elements.
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
## PYTORCH CODE
|
||||
>>> train.set_format("torch", columns=["input_ids", "attention_mask", "labels"])
|
||||
>>> {key: val.shape for key, val in train[0].items()})
|
||||
{'labels': torch.Size([]), 'input_ids': torch.Size([512]), 'attention_mask': torch.Size([512])}
|
||||
## TENSORFLOW CODE
|
||||
>>> train.set_format("tensorflow", columns=["input_ids", "attention_mask", "labels"])
|
||||
>>> {key: val.shape for key, val in train[0].items()})
|
||||
{'labels': TensorShape([]), 'input_ids': TensorShape([512]), 'attention_mask': TensorShape([512])}
|
||||
|
||||
We now have a fully-prepared dataset. Check out `the 🤗 NLP docs <https://huggingface.co/nlp/processing.html>`_ for
|
||||
a more thorough introduction.
|
||||
@@ -1 +0,0 @@
|
||||
../../examples/README.md
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 47 KiB |
@@ -1,241 +0,0 @@
|
||||
Glossary
|
||||
^^^^^^^^
|
||||
|
||||
General terms
|
||||
-------------
|
||||
|
||||
- autoencoding models: see MLM
|
||||
- autoregressive models: see CLM
|
||||
- CLM: causal language modeling, a pretraining task where the model reads the texts in order and has to predict the
|
||||
next word. It's usually done by reading the whole sentence but using a mask inside the model to hide the future
|
||||
tokens at a certain timestep.
|
||||
- MLM: masked language modeling, a pretraining task where the model sees a corrupted version of the texts, usually done
|
||||
by masking some tokens randomly, and has to predict the original text.
|
||||
- multimodal: a task that combines texts with another kind of inputs (for instance images).
|
||||
- NLG: natural language generation, all tasks related to generating text ( for instance talk with transformers,
|
||||
translation)
|
||||
- NLP: natural language processing, a generic way to say "deal with texts".
|
||||
- NLU: natural language understanding, all tasks related to understanding what is in a text (for instance classifying
|
||||
the whole text, individual words)
|
||||
- pretrained model: a model that has been pretrained on some data (for instance all of Wikipedia). Pretraining methods
|
||||
involve a self-supervised objective, which can be reading the text and trying to predict the next word (see CLM) or
|
||||
masking some words and trying to predict them (see MLM).
|
||||
- RNN: recurrent neural network, a type of model that uses a loop over a layer to process texts.
|
||||
- seq2seq or sequence-to-sequence: models that generate a new sequence from an input, like translation models, or
|
||||
summarization models (such as :doc:`Bart </model_doc/bart>` or :doc:`T5 </model_doc/t5>`).
|
||||
- token: a part of a sentence, usually a word, but can also be a subword (non-common words are often split in subwords)
|
||||
or a punctuation symbol.
|
||||
|
||||
Model inputs
|
||||
------------
|
||||
|
||||
Every model is different yet bears similarities with the others. Therefore most models use the same inputs, which are
|
||||
detailed here alongside usage examples.
|
||||
|
||||
.. _input-ids:
|
||||
|
||||
Input IDs
|
||||
~~~~~~~~~
|
||||
|
||||
The input ids are often the only required parameters to be passed to the model as input. *They are token indices,
|
||||
numerical representations of tokens building the sequences that will be used as input by the model*.
|
||||
|
||||
Each tokenizer works differently but the underlying mechanism remains the same. Here's an example using the BERT
|
||||
tokenizer, which is a `WordPiece <https://arxiv.org/pdf/1609.08144.pdf>`__ tokenizer:
|
||||
|
||||
::
|
||||
|
||||
>>> from transformers import BertTokenizer
|
||||
>>> tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
|
||||
|
||||
>>> sequence = "A Titan RTX has 24GB of VRAM"
|
||||
|
||||
The tokenizer takes care of splitting the sequence into tokens available in the tokenizer vocabulary.
|
||||
|
||||
::
|
||||
|
||||
>>> tokenized_sequence = tokenizer.tokenize(sequence)
|
||||
|
||||
The tokens are either words or subwords. Here for instance, "VRAM" wasn't in the model vocabulary, so it's been split
|
||||
in "V", "RA" and "M". To indicate those tokens are not separate words but parts of the same word, a double-hash prefix is
|
||||
added for "RA" and "M":
|
||||
|
||||
::
|
||||
|
||||
>>> print(tokenized_sequence)
|
||||
['A', 'Titan', 'R', '##T', '##X', 'has', '24', '##GB', 'of', 'V', '##RA', '##M']
|
||||
|
||||
These tokens can then be converted into IDs which are understandable by the model. This can be done by directly feeding
|
||||
the sentence to the tokenizer, which leverages the Rust implementation of
|
||||
`huggingface/tokenizers <https://github.com/huggingface/tokenizers>`__ for peak performance.
|
||||
|
||||
::
|
||||
|
||||
>>> inputs = tokenizer(sequence)
|
||||
|
||||
The tokenizer returns a dictionary with all the arguments necessary for its corresponding model to work properly. The
|
||||
token indices are under the key "input_ids":
|
||||
|
||||
::
|
||||
|
||||
>>> encoded_sequence = inputs["input_ids"]
|
||||
>>> print(encoded_sequence)
|
||||
[101, 138, 18696, 155, 1942, 3190, 1144, 1572, 13745, 1104, 159, 9664, 2107, 102]
|
||||
|
||||
Note that the tokenizer automatically adds "special tokens" (if the associated model relies on them) which are special
|
||||
IDs the model sometimes uses.
|
||||
|
||||
If we decode the previous sequence of ids,
|
||||
|
||||
::
|
||||
|
||||
>>> decoded_sequence = tokenizer.decode(encoded_sequence)
|
||||
|
||||
we will see
|
||||
|
||||
::
|
||||
|
||||
>>> print(decoded_sequence)
|
||||
[CLS] A Titan RTX has 24GB of VRAM [SEP]
|
||||
|
||||
because this is the way a :class:`~transformers.BertModel` is going to expect its inputs.
|
||||
|
||||
.. _attention-mask:
|
||||
|
||||
Attention mask
|
||||
~~~~~~~~~~~~~~
|
||||
|
||||
The attention mask is an optional argument used when batching sequences together. This argument indicates to the
|
||||
model which tokens should be attended to, and which should not.
|
||||
|
||||
For example, consider these two sequences:
|
||||
|
||||
::
|
||||
|
||||
>>> from transformers import BertTokenizer
|
||||
>>> tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
|
||||
|
||||
>>> sequence_a = "This is a short sequence."
|
||||
>>> sequence_b = "This is a rather long sequence. It is at least longer than the sequence A."
|
||||
|
||||
>>> encoded_sequence_a = tokenizer(sequence_a)["input_ids"]
|
||||
>>> encoded_sequence_b = tokenizer(sequence_b)["input_ids"]
|
||||
|
||||
The encoded versions have different lengths:
|
||||
|
||||
::
|
||||
|
||||
>>> len(encoded_sequence_a), len(encoded_sequence_b)
|
||||
(8, 19)
|
||||
|
||||
Therefore, we can't put them together in the same tensor as-is. The first sequence needs to be padded up to the length
|
||||
of the second one, or the second one needs to be truncated down to the length of the first one.
|
||||
|
||||
In the first case, the list of IDs will be extended by the padding indices. We can pass a list to the tokenizer and ask
|
||||
it to pad like this:
|
||||
|
||||
::
|
||||
|
||||
>>> padded_sequences = tokenizer([sequence_a, sequence_b], padding=True)
|
||||
|
||||
We can see that 0s have been added on the right of the first sentence to make it the same length as the second one:
|
||||
|
||||
::
|
||||
|
||||
>>> padded_sequences["input_ids"]
|
||||
[[101, 1188, 1110, 170, 1603, 4954, 119, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [101, 1188, 1110, 170, 1897, 1263, 4954, 119, 1135, 1110, 1120, 1655, 2039, 1190, 1103, 4954, 138, 119, 102]]
|
||||
|
||||
This can then be converted into a tensor in PyTorch or TensorFlow. The attention mask is a binary tensor indicating
|
||||
the position of the padded indices so that the model does not attend to them. For the
|
||||
:class:`~transformers.BertTokenizer`, :obj:`1` indicates a value that should be attended to, while :obj:`0` indicates
|
||||
a padded value. This attention mask is in the dictionary returned by the tokenizer under the key "attention_mask":
|
||||
|
||||
::
|
||||
|
||||
>>> padded_sequences["attention_mask"]
|
||||
[[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
|
||||
|
||||
.. _token-type-ids:
|
||||
|
||||
Token Type IDs
|
||||
~~~~~~~~~~~~~~
|
||||
|
||||
Some models' purpose is to do sequence classification or question answering. These require two different sequences to
|
||||
be joined in a single "input_ids" entry, which usually is performed with the help of special tokens, such as the classifier (``[CLS]``) and separator (``[SEP]``)
|
||||
tokens. For example, the BERT model builds its two sequence input as such:
|
||||
|
||||
::
|
||||
|
||||
>>> # [CLS] SEQUENCE_A [SEP] SEQUENCE_B [SEP]
|
||||
|
||||
We can use our tokenizer to automatically generate such a sentence by passing the two sequences to ``tokenizer`` as two arguments (and
|
||||
not a list, like before) like this:
|
||||
|
||||
::
|
||||
|
||||
>>> from transformers import BertTokenizer
|
||||
>>> tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
|
||||
>>> sequence_a = "HuggingFace is based in NYC"
|
||||
>>> sequence_b = "Where is HuggingFace based?"
|
||||
|
||||
>>> encoded_dict = tokenizer(sequence_a, sequence_b)
|
||||
>>> decoded = tokenizer.decode(encoded_dict["input_ids"])
|
||||
|
||||
which will return:
|
||||
|
||||
::
|
||||
|
||||
>>> print(decoded)
|
||||
[CLS] HuggingFace is based in NYC [SEP] Where is HuggingFace based? [SEP]
|
||||
|
||||
This is enough for some models to understand where one sequence ends and where another begins. However, other models,
|
||||
such as BERT, also deploy token type IDs (also called segment IDs). They are represented as a binary
|
||||
mask identifying the two types of sequence in the model.
|
||||
|
||||
The tokenizer returns this mask as the "token_type_ids" entry:
|
||||
|
||||
::
|
||||
|
||||
>>> encoded_dict['token_type_ids']
|
||||
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]
|
||||
|
||||
The first sequence, the "context" used for the question, has all its tokens represented by a :obj:`0`, whereas the
|
||||
second sequence, corresponding to the "question", has all its tokens represented by a :obj:`1`.
|
||||
|
||||
Some models, like :class:`~transformers.XLNetModel` use an additional token represented by a :obj:`2`.
|
||||
|
||||
.. _position-ids:
|
||||
|
||||
Position IDs
|
||||
~~~~~~~~~~~~
|
||||
|
||||
Contrary to RNNs that have the position of each token embedded within them,
|
||||
transformers are unaware of the position of each token. Therefore, the position IDs (``position_ids``) are used by the model to identify each token's position in the list of tokens.
|
||||
|
||||
They are an optional parameter. If no ``position_ids`` is passed to the model, the IDs are automatically created as absolute
|
||||
positional embeddings.
|
||||
|
||||
Absolute positional embeddings are selected in the range ``[0, config.max_position_embeddings - 1]``. Some models
|
||||
use other types of positional embeddings, such as sinusoidal position embeddings or relative position embeddings.
|
||||
|
||||
.. _feed-forward-chunking:
|
||||
|
||||
Feed Forward Chunking
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
In each residual attention block in transformers the self-attention layer is usually followed by 2 feed forward layers.
|
||||
The intermediate embedding size of the feed forward layers is often bigger than the hidden size of the model (e.g.,
|
||||
for ``bert-base-uncased``).
|
||||
|
||||
For an input of size ``[batch_size, sequence_length]``, the memory required to store the intermediate feed forward
|
||||
embeddings ``[batch_size, sequence_length, config.intermediate_size]`` can account for a large fraction of the memory
|
||||
use. The authors of `Reformer: The Efficient Transformer <https://arxiv.org/abs/2001.04451>`_ noticed that since the
|
||||
computation is independent of the ``sequence_length`` dimension, it is mathematically equivalent to compute the output
|
||||
embeddings of both feed forward layers ``[batch_size, config.hidden_size]_0, ..., [batch_size, config.hidden_size]_n``
|
||||
individually and concat them afterward to ``[batch_size, sequence_length, config.hidden_size]`` with
|
||||
``n = sequence_length``, which trades increased computation time against reduced memory use, but yields a
|
||||
mathematically **equivalent** result.
|
||||
|
||||
For models employing the function :func:`~.transformers.apply_chunking_to_forward`, the ``chunk_size`` defines the
|
||||
number of output embeddings that are computed in parallel and thus defines the trade-off between memory and time
|
||||
complexity. If ``chunk_size`` is set to 0, no feed forward chunking is done.
|
||||
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@@ -1,230 +0,0 @@
|
||||
Transformers
|
||||
================================================================================================================================================
|
||||
|
||||
State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0.
|
||||
|
||||
🤗 Transformers (formerly known as `pytorch-transformers` and `pytorch-pretrained-bert`) provides general-purpose
|
||||
architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural Language Understanding (NLU) and Natural
|
||||
Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between
|
||||
TensorFlow 2.0 and PyTorch.
|
||||
|
||||
This is the documentation of our repository `transformers <https://github.com/huggingface/transformers>`_.
|
||||
|
||||
Features
|
||||
---------------------------------------------------
|
||||
|
||||
- High performance on NLU and NLG tasks
|
||||
- Low barrier to entry for educators and practitioners
|
||||
|
||||
State-of-the-art NLP for everyone:
|
||||
|
||||
- Deep learning researchers
|
||||
- Hands-on practitioners
|
||||
- AI/ML/NLP teachers and educators
|
||||
|
||||
Lower compute costs, smaller carbon footprint:
|
||||
|
||||
- Researchers can share trained models instead of always retraining
|
||||
- Practitioners can reduce compute time and production costs
|
||||
- 8 architectures with over 30 pretrained models, some in more than 100 languages
|
||||
|
||||
Choose the right framework for every part of a model's lifetime:
|
||||
|
||||
- Train state-of-the-art models in 3 lines of code
|
||||
- Deep interoperability between TensorFlow 2.0 and PyTorch models
|
||||
- Move a single model between TF2.0/PyTorch frameworks at will
|
||||
- Seamlessly pick the right framework for training, evaluation, production
|
||||
|
||||
Contents
|
||||
---------------------------------
|
||||
|
||||
The documentation is organized in five parts:
|
||||
|
||||
- **GET STARTED** contains a quick tour, the installation instructions and some useful information about our philosophy
|
||||
and a glossary.
|
||||
- **USING 🤗 TRANSFORMERS** contains general tutorials on how to use the library.
|
||||
- **ADVANCED GUIDES** contains more advanced guides that are more specific to a given script or part of the library.
|
||||
- **RESEARCH** focuses on tutorials that have less to do with how to use the library but more about general resarch in
|
||||
transformers model
|
||||
- **PACKAGE REFERENCE** contains the documentation of each public class and function.
|
||||
|
||||
The library currently contains PyTorch and Tensorflow implementations, pre-trained model weights, usage scripts and
|
||||
conversion utilities for the following models:
|
||||
|
||||
1. `BERT <https://github.com/google-research/bert>`_ (from Google) released with the paper `BERT: Pre-training of Deep
|
||||
Bidirectional Transformers for Language Understanding <https://arxiv.org/abs/1810.04805>`_ by Jacob Devlin, Ming-Wei
|
||||
Chang, Kenton Lee, and Kristina Toutanova.
|
||||
2. `GPT <https://github.com/openai/finetune-transformer-lm>`_ (from OpenAI) released with the paper `Improving Language
|
||||
Understanding by Generative Pre-Training <https://blog.openai.com/language-unsupervised>`_ by Alec Radford, Karthik
|
||||
Narasimhan, Tim Salimans, and Ilya Sutskever.
|
||||
3. `GPT-2 <https://blog.openai.com/better-language-models>`_ (from OpenAI) released with the paper `Language Models are
|
||||
Unsupervised Multitask Learners <https://blog.openai.com/better-language-models>`_ by Alec Radford, Jeffrey Wu,
|
||||
Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever.
|
||||
4. `Transformer-XL <https://github.com/kimiyoung/transformer-xl>`_ (from Google/CMU) released with the paper
|
||||
`Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context <https://arxiv.org/abs/1901.02860>`_ by
|
||||
Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V. Le, and Ruslan Salakhutdinov.
|
||||
5. `XLNet <https://github.com/zihangdai/xlnet>`_ (from Google/CMU) released with the paper `XLNet: Generalized
|
||||
Autoregressive Pretraining for Language Understanding <https://arxiv.org/abs/1906.08237>`_ by Zhilin Yang, Zihang
|
||||
Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, and Quoc V. Le.
|
||||
6. `XLM <https://github.com/facebookresearch/XLM>`_ (from Facebook) released together with the paper `Cross-lingual
|
||||
Language Model Pretraining <https://arxiv.org/abs/1901.07291>`_ by Guillaume Lample and Alexis Conneau.
|
||||
7. `RoBERTa <https://github.com/pytorch/fairseq/tree/master/examples/roberta>`_ (from Facebook), released together with
|
||||
the paper a `Robustly Optimized BERT Pretraining Approach <https://arxiv.org/abs/1907.11692>`_ by Yinhan Liu, Myle
|
||||
Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin
|
||||
Stoyanov.
|
||||
8. `DistilBERT <https://huggingface.co/transformers/model_doc/distilbert.html>`_ (from HuggingFace) released together
|
||||
with the paper `DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
|
||||
<https://arxiv.org/abs/1910.01108>`_ by Victor Sanh, Lysandre Debut, and Thomas Wolf. The same method has been
|
||||
applied to compress GPT2 into
|
||||
`DistilGPT2 <https://github.com/huggingface/transformers/tree/master/examples/distillation>`_.
|
||||
9. `CTRL <https://github.com/pytorch/fairseq/tree/master/examples/ctrl>`_ (from Salesforce), released together with the
|
||||
paper `CTRL: A Conditional Transformer Language Model for Controllable Generation
|
||||
<https://www.github.com/salesforce/ctrl>`_ by Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, Caiming Xiong,
|
||||
and Richard Socher.
|
||||
10. `CamemBERT <https://huggingface.co/transformers/model_doc/camembert.html>`_ (from FAIR, Inria, Sorbonne Université)
|
||||
released together with the paper `CamemBERT: a Tasty French Language Model <https://arxiv.org/abs/1911.03894>`_ by
|
||||
Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suarez, Yoann Dupont, Laurent Romary, Eric Villemonte de la
|
||||
Clergerie, Djame Seddah, and Benoît Sagot.
|
||||
11. `ALBERT <https://github.com/google-research/ALBERT>`_ (from Google Research), released together with the paper
|
||||
`ALBERT: A Lite BERT for Self-supervised Learning of Language Representations <https://arxiv.org/abs/1909.11942>`_
|
||||
by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut.
|
||||
12. `T5 <https://github.com/google-research/text-to-text-transfer-transformer>`_ (from Google) released with the paper
|
||||
`Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
|
||||
<https://arxiv.org/abs/1910.10683>`_ by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang,
|
||||
Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu.
|
||||
13. `XLM-RoBERTa <https://github.com/pytorch/fairseq/tree/master/examples/xlmr>`_ (from Facebook AI), released together
|
||||
with the paper `Unsupervised Cross-lingual Representation Learning at Scale <https://arxiv.org/abs/1911.02116>`_ by
|
||||
Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard
|
||||
Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov.
|
||||
14. `MMBT <https://github.com/facebookresearch/mmbt/>`_ (from Facebook), released together with the paper a `Supervised
|
||||
Multimodal Bitransformers for Classifying Images and Text <https://arxiv.org/pdf/1909.02950.pdf>`_ by Douwe Kiela,
|
||||
Suvrat Bhooshan, Hamed Firooz, and Davide Testuggine.
|
||||
15. `FlauBERT <https://github.com/getalp/Flaubert>`_ (from CNRS) released with the paper `FlauBERT: Unsupervised
|
||||
Language Model Pre-training for French <https://arxiv.org/abs/1912.05372>`_ by Hang Le, Loïc Vial, Jibril Frej,
|
||||
Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, and
|
||||
Didier Schwab.
|
||||
16. `BART <https://github.com/pytorch/fairseq/tree/master/examples/bart>`_ (from Facebook) released with the paper
|
||||
`BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
|
||||
<https://arxiv.org/pdf/1910.13461.pdf>`_ by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman
|
||||
Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer.
|
||||
17. `ELECTRA <https://github.com/google-research/electra>`_ (from Google Research/Stanford University) released with
|
||||
the paper `ELECTRA: Pre-training text encoders as discriminators rather than generators
|
||||
<https://arxiv.org/abs/2003.10555>`_ by Kevin Clark, Minh-Thang Luong, Quoc V. Le, and Christopher D. Manning.
|
||||
18. `DialoGPT <https://github.com/microsoft/DialoGPT>`_ (from Microsoft Research) released with the paper `DialoGPT:
|
||||
Large-Scale Generative Pre-training for Conversational Response Generation <https://arxiv.org/abs/1911.00536>`_ by
|
||||
Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu,
|
||||
and Bill Dolan.
|
||||
19. `Reformer <https://github.com/google/trax/tree/master/trax/models/reformer>`_ (from Google Research) released with
|
||||
the paper `Reformer: The Efficient Transformer <https://arxiv.org/abs/2001.04451>`_ by Nikita Kitaev, Łukasz
|
||||
Kaiser, and Anselm Levskaya.
|
||||
20. `MarianMT <https://marian-nmt.github.io/>`_ (developed by the Microsoft Translator Team) machine translation models
|
||||
trained using `OPUS <http://opus.nlpl.eu/>`_ pretrained_models data by Jörg Tiedemann.
|
||||
21. `Longformer <https://github.com/allenai/longformer>`_ (from AllenAI) released with the paper `Longformer: The
|
||||
Long-Document Transformer <https://arxiv.org/abs/2004.05150>`_ by Iz Beltagy, Matthew E. Peters, and Arman Cohan.
|
||||
22. `DPR <https://github.com/facebookresearch/DPR>`_ (from Facebook) released with the paper `Dense Passage Retrieval
|
||||
for Open-Domain Question Answering <https://arxiv.org/abs/2004.04906>`_ by Vladimir Karpukhin, Barlas Oğuz, Sewon
|
||||
Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
|
||||
23. `Pegasus <https://github.com/google-research/pegasus>`_ (from Google) released with the paper `PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
|
||||
<https://arxiv.org/abs/1912.08777>`_ by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu.
|
||||
24. `MBart <https://github.com/pytorch/fairseq/tree/master/examples/mbart>`_ (from Facebook) released with the paper `Multilingual Denoising Pre-training for Neural Machine Translation <https://arxiv.org/abs/2001.08210>`_ by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov,
|
||||
Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
|
||||
25. `LXMERT <https://github.com/airsplay/lxmert>`_ (from UNC Chapel Hill) released with the paper `LXMERT: Learning
|
||||
Cross-Modality Encoder Representations from Transformers for Open-Domain Question
|
||||
Answering <https://arxiv.org/abs/1908.07490>`_ by Hao Tan and Mohit Bansal.
|
||||
26. `Funnel Transformer <https://github.com/laiguokun/Funnel-Transformer>`_ (from CMU/Google Brain) released with the paper
|
||||
`Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing
|
||||
<https://arxiv.org/abs/2006.03236>`_ by Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le.
|
||||
27. `Bert For Sequence Generation <https://tfhub.dev/s?module-type=text-generation&subtype=module,placeholder>`_ (from Google) released with the paper
|
||||
`Leveraging Pre-trained Checkpoints for Sequence Generation Tasks
|
||||
<https://arxiv.org/abs/1907.12461>`_ by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
|
||||
28. `Other community models <https://huggingface.co/models>`_, contributed by the `community
|
||||
<https://huggingface.co/users>`_.
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
:caption: Get started
|
||||
|
||||
quicktour
|
||||
installation
|
||||
philosophy
|
||||
glossary
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
:caption: Using 🤗 Transformers
|
||||
|
||||
task_summary
|
||||
model_summary
|
||||
preprocessing
|
||||
training
|
||||
model_sharing
|
||||
tokenizer_summary
|
||||
multilingual
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
:caption: Advanced guides
|
||||
|
||||
pretrained_models
|
||||
examples
|
||||
custom_datasets
|
||||
notebooks
|
||||
converting_tensorflow_models
|
||||
migration
|
||||
contributing
|
||||
serialization
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
:caption: Research
|
||||
|
||||
bertology
|
||||
perplexity
|
||||
benchmarks
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
:caption: Package Reference
|
||||
|
||||
main_classes/configuration
|
||||
main_classes/output
|
||||
main_classes/model
|
||||
main_classes/tokenizer
|
||||
main_classes/pipelines
|
||||
main_classes/trainer
|
||||
main_classes/optimizer_schedules
|
||||
main_classes/processors
|
||||
main_classes/logging
|
||||
model_doc/auto
|
||||
model_doc/encoderdecoder
|
||||
model_doc/bert
|
||||
model_doc/gpt
|
||||
model_doc/transformerxl
|
||||
model_doc/gpt2
|
||||
model_doc/xlm
|
||||
model_doc/xlnet
|
||||
model_doc/roberta
|
||||
model_doc/distilbert
|
||||
model_doc/ctrl
|
||||
model_doc/camembert
|
||||
model_doc/albert
|
||||
model_doc/xlmroberta
|
||||
model_doc/flaubert
|
||||
model_doc/bart
|
||||
model_doc/t5
|
||||
model_doc/electra
|
||||
model_doc/dialogpt
|
||||
model_doc/reformer
|
||||
model_doc/marian
|
||||
model_doc/longformer
|
||||
model_doc/retribert
|
||||
model_doc/mobilebert
|
||||
model_doc/dpr
|
||||
model_doc/pegasus
|
||||
model_doc/mbart
|
||||
model_doc/funnel
|
||||
model_doc/lxmert
|
||||
model_doc/bertgeneration
|
||||
internal/modeling_utils
|
||||
internal/tokenization_utils
|
||||
internal/pipelines_utils
|
||||
@@ -1,102 +0,0 @@
|
||||
# Installation
|
||||
|
||||
🤗 Transformers is tested on Python 3.6+, and PyTorch 1.1.0+ or TensorFlow 2.0+.
|
||||
|
||||
You should install 🤗 Transformers in a [virtual environment](https://docs.python.org/3/library/venv.html). If you're
|
||||
unfamiliar with Python virtual environments, check out the [user guide](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/). Create a virtual environment with the version of Python you're going
|
||||
to use and activate it.
|
||||
|
||||
Now, if you want to use 🤗 Transformers, you can install it with pip. If you'd like to play with the examples, you
|
||||
must install it from source.
|
||||
|
||||
## Installation with pip
|
||||
|
||||
First you need to install one of, or both, TensorFlow 2.0 and PyTorch.
|
||||
Please refer to [TensorFlow installation page](https://www.tensorflow.org/install/pip#tensorflow-2.0-rc-is-available)
|
||||
and/or [PyTorch installation page](https://pytorch.org/get-started/locally/#start-locally) regarding the specific
|
||||
install command for your platform.
|
||||
|
||||
When TensorFlow 2.0 and/or PyTorch has been installed, 🤗 Transformers can be installed using pip as follows:
|
||||
|
||||
```bash
|
||||
pip install transformers
|
||||
```
|
||||
|
||||
Alternatively, for CPU-support only, you can install 🤗 Transformers and PyTorch in one line with:
|
||||
|
||||
```bash
|
||||
pip install transformers[torch]
|
||||
```
|
||||
|
||||
or 🤗 Transformers and TensorFlow 2.0 in one line with:
|
||||
|
||||
```bash
|
||||
pip install transformers[tf-cpu]
|
||||
```
|
||||
|
||||
To check 🤗 Transformers is properly installed, run the following command:
|
||||
|
||||
```bash
|
||||
python -c "from transformers import pipeline; print(pipeline('sentiment-analysis')('I hate you'))"
|
||||
```
|
||||
|
||||
It should download a pretrained model then print something like
|
||||
|
||||
```bash
|
||||
[{'label': 'NEGATIVE', 'score': 0.9991129040718079}]
|
||||
```
|
||||
|
||||
(Note that TensorFlow will print additional stuff before that last statement.)
|
||||
|
||||
## Installing from source
|
||||
|
||||
To install from source, clone the repository and install with the following commands:
|
||||
|
||||
``` bash
|
||||
git clone https://github.com/huggingface/transformers.git
|
||||
cd transformers
|
||||
pip install -e .
|
||||
```
|
||||
|
||||
Again, you can run
|
||||
|
||||
```bash
|
||||
python -c "from transformers import pipeline; print(pipeline('sentiment-analysis')('I hate you'))"
|
||||
```
|
||||
|
||||
to check 🤗 Transformers is properly installed.
|
||||
|
||||
## Caching models
|
||||
|
||||
This library provides pretrained models that will be downloaded and cached locally. Unless you specify a location with
|
||||
`cache_dir=...` when you use methods like `from_pretrained`, these models will automatically be downloaded in the
|
||||
folder given by the shell environment variable ``TRANSFORMERS_CACHE``. The default value for it will be the PyTorch
|
||||
cache home followed by ``/transformers/`` (even if you don't have PyTorch installed). This is (by order of priority):
|
||||
|
||||
* shell environment variable ``TORCH_HOME``
|
||||
* shell environment variable ``XDG_CACHE_HOME`` + ``/torch/``
|
||||
* default: ``~/.cache/torch/``
|
||||
|
||||
So if you don't have any specific environment variable set, the cache directory will be at
|
||||
``~/.cache/torch/transformers/``.
|
||||
|
||||
**Note:** If you have set a shell enviromnent variable for one of the predecessors of this library
|
||||
(``PYTORCH_TRANSFORMERS_CACHE`` or ``PYTORCH_PRETRAINED_BERT_CACHE``), those will be used if there is no shell
|
||||
enviromnent variable for ``TRANSFORMERS_CACHE``.
|
||||
|
||||
### Note on model downloads (Continuous Integration or large-scale deployments)
|
||||
|
||||
If you expect to be downloading large volumes of models (more than 1,000) from our hosted bucket (for instance through
|
||||
your CI setup, or a large-scale production deployment), please cache the model files on your end. It will be way
|
||||
faster, and cheaper. Feel free to contact us privately if you need any help.
|
||||
|
||||
## Do you want to run a Transformer model on a mobile device?
|
||||
|
||||
You should check out our [swift-coreml-transformers](https://github.com/huggingface/swift-coreml-transformers) repo.
|
||||
|
||||
It contains a set of tools to convert PyTorch or TensorFlow 2.0 trained Transformer models (currently contains `GPT-2`,
|
||||
`DistilGPT-2`, `BERT`, and `DistilBERT`) to CoreML models that run on iOS devices.
|
||||
|
||||
At some point in the future, you'll be able to seamlessly move from pre-training or fine-tuning models in PyTorch or
|
||||
TensorFlow 2.0 to productizing them in CoreML, or prototype a model or an app in CoreML then research its
|
||||
hyperparameters or architecture from PyTorch or TensorFlow 2.0. Super exciting!
|
||||
@@ -1,88 +0,0 @@
|
||||
Custom Layers and Utilities
|
||||
---------------------------
|
||||
|
||||
This page lists all the custom layers used by the library, as well as the utility functions it provides for modeling.
|
||||
|
||||
Most of those are only useful if you are studying the code of the models in the library.
|
||||
|
||||
|
||||
``Pytorch custom modules``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.modeling_utils.Conv1D
|
||||
|
||||
.. autoclass:: transformers.modeling_utils.PoolerStartLogits
|
||||
:members: forward
|
||||
|
||||
.. autoclass:: transformers.modeling_utils.PoolerEndLogits
|
||||
:members: forward
|
||||
|
||||
.. autoclass:: transformers.modeling_utils.PoolerAnswerClass
|
||||
:members: forward
|
||||
|
||||
.. autoclass:: transformers.modeling_utils.SquadHeadOutput
|
||||
|
||||
.. autoclass:: transformers.modeling_utils.SQuADHead
|
||||
:members: forward
|
||||
|
||||
.. autoclass:: transformers.modeling_utils.SequenceSummary
|
||||
:members: forward
|
||||
|
||||
|
||||
``PyTorch Helper Functions``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autofunction:: transformers.apply_chunking_to_forward
|
||||
|
||||
.. autofunction:: transformers.modeling_utils.find_pruneable_heads_and_indices
|
||||
|
||||
.. autofunction:: transformers.modeling_utils.prune_layer
|
||||
|
||||
.. autofunction:: transformers.modeling_utils.prune_conv1d_layer
|
||||
|
||||
.. autofunction:: transformers.modeling_utils.prune_linear_layer
|
||||
|
||||
``TensorFlow custom layers``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.modeling_tf_utils.TFConv1D
|
||||
|
||||
.. autoclass:: transformers.modeling_tf_utils.TFSharedEmbeddings
|
||||
:members: call
|
||||
|
||||
.. autoclass:: transformers.modeling_tf_utils.TFSequenceSummary
|
||||
:members: call
|
||||
|
||||
|
||||
``TensorFlow loss functions``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.modeling_tf_utils.TFCausalLanguageModelingLoss
|
||||
:members:
|
||||
|
||||
.. autoclass:: transformers.modeling_tf_utils.TFMaskedLanguageModelingLoss
|
||||
:members:
|
||||
|
||||
.. autoclass:: transformers.modeling_tf_utils.TFMultipleChoiceLoss
|
||||
:members:
|
||||
|
||||
.. autoclass:: transformers.modeling_tf_utils.TFQuestionAnsweringLoss
|
||||
:members:
|
||||
|
||||
.. autoclass:: transformers.modeling_tf_utils.TFSequenceClassificationLoss
|
||||
:members:
|
||||
|
||||
.. autoclass:: transformers.modeling_tf_utils.TFTokenClassificationLoss
|
||||
:members:
|
||||
|
||||
|
||||
``TensorFlow Helper Functions``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autofunction:: transformers.modeling_tf_utils.cast_bool_to_primitive
|
||||
|
||||
.. autofunction:: transformers.modeling_tf_utils.get_initializer
|
||||
|
||||
.. autofunction:: transformers.modeling_tf_utils.keras_serializable
|
||||
|
||||
.. autofunction:: transformers.modeling_tf_utils.shape_list
|
||||
@@ -1,40 +0,0 @@
|
||||
Utilities for pipelines
|
||||
-----------------------
|
||||
|
||||
This page lists all the utility functions the library provides for pipelines.
|
||||
|
||||
Most of those are only useful if you are studying the code of the models in the library.
|
||||
|
||||
|
||||
Argument handling
|
||||
~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.pipelines.ArgumentHandler
|
||||
|
||||
.. autoclass:: transformers.pipelines.ZeroShotClassificationArgumentHandler
|
||||
|
||||
.. autoclass:: transformers.pipelines.QuestionAnsweringArgumentHandler
|
||||
|
||||
|
||||
Data format
|
||||
~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.pipelines.PipelineDataFormat
|
||||
:members:
|
||||
|
||||
.. autoclass:: transformers.pipelines.CsvPipelineDataFormat
|
||||
:members:
|
||||
|
||||
.. autoclass:: transformers.pipelines.JsonPipelineDataFormat
|
||||
:members:
|
||||
|
||||
.. autoclass:: transformers.pipelines.PipedPipelineDataFormat
|
||||
:members:
|
||||
|
||||
|
||||
Utilities
|
||||
~~~~~~~~~
|
||||
|
||||
.. autofunction:: transformers.pipelines.get_framework
|
||||
|
||||
.. autoclass:: transformers.pipelines.PipelineException
|
||||
@@ -1,38 +0,0 @@
|
||||
Utilities for Tokenizers
|
||||
------------------------
|
||||
|
||||
This page lists all the utility functions used by the tokenizers, mainly the class
|
||||
:class:`~transformers.tokenization_utils_base.PreTrainedTokenizerBase` that implements the common methods between
|
||||
:class:`~transformers.PreTrainedTokenizer` and :class:`~transformers.PreTrainedTokenizerFast` and the mixin
|
||||
:class:`~transformers.tokenization_utils_base.SpecialTokensMixin`.
|
||||
|
||||
Most of those are only useful if you are studying the code of the tokenizers in the library.
|
||||
|
||||
``PreTrainedTokenizerBase``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.tokenization_utils_base.PreTrainedTokenizerBase
|
||||
:special-members: __call__
|
||||
:members:
|
||||
|
||||
|
||||
``SpecialTokensMixin``
|
||||
~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.tokenization_utils_base.SpecialTokensMixin
|
||||
:members:
|
||||
|
||||
|
||||
Enums and namedtuples
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
.. autoclass:: transformers.tokenization_utils_base.ExplicitEnum
|
||||
|
||||
.. autoclass:: transformers.tokenization_utils_base.PaddingStrategy
|
||||
|
||||
.. autoclass:: transformers.tokenization_utils_base.TensorType
|
||||
|
||||
.. autoclass:: transformers.tokenization_utils_base.TruncationStrategy
|
||||
|
||||
.. autoclass:: transformers.tokenization_utils_base.CharSpan
|
||||
|
||||
.. autoclass:: transformers.tokenization_utils_base.TokenSpan
|
||||
@@ -1,13 +0,0 @@
|
||||
Configuration
|
||||
----------------------------------------------------
|
||||
|
||||
The base class :class:`~transformers.PretrainedConfig` implements the common methods for loading/saving a configuration
|
||||
either from a local file or directory, or from a pretrained model configuration provided by the library (downloaded
|
||||
from HuggingFace's AWS S3 repository).
|
||||
|
||||
|
||||
PretrainedConfig
|
||||
~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.PretrainedConfig
|
||||
:members:
|
||||
@@ -1,52 +0,0 @@
|
||||
Logging
|
||||
-------
|
||||
|
||||
🤗 Transformers has a centralized logging system, so that you can setup the verbosity of the library easily.
|
||||
|
||||
Currently the default verbosity of the library is ``WARNING``.
|
||||
|
||||
To change the level of verbosity, just use one of the direct setters. For instance, here is how to change the verbosity to the INFO level.
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
import transformers
|
||||
transformers.logging.set_verbosity_info()
|
||||
|
||||
You can also use the environment variable ``TRANSFORMERS_VERBOSITY`` to override the default verbosity. You can set it to one of the following: ``debug``, ``info``, ``warning``, ``error``, ``critical``. For example:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
TRANSFORMERS_VERBOSITY=error ./myprogram.py
|
||||
|
||||
All the methods of this logging module are documented below, the main ones are
|
||||
:func:`transformers.logging.get_verbosity` to get the current level of verbosity in the logger and
|
||||
:func:`transformers.logging.set_verbosity` to set the verbosity to the level of your choice. In order (from the least
|
||||
verbose to the most verbose), those levels (with their corresponding int values in parenthesis) are:
|
||||
|
||||
- :obj:`transformers.logging.CRITICAL` or :obj:`transformers.logging.FATAL` (int value, 50): only report the most
|
||||
critical errors.
|
||||
- :obj:`transformers.logging.ERROR` (int value, 40): only report errors.
|
||||
- :obj:`transformers.logging.WARNING` or :obj:`transformers.logging.WARN` (int value, 30): only reports error and
|
||||
warnings. This the default level used by the library.
|
||||
- :obj:`transformers.logging.INFO` (int value, 20): reports error, warnings and basic information.
|
||||
- :obj:`transformers.logging.DEBUG` (int value, 10): report all information.
|
||||
|
||||
Base setters
|
||||
~~~~~~~~~~~~
|
||||
|
||||
.. autofunction:: transformers.logging.set_verbosity_error
|
||||
|
||||
.. autofunction:: transformers.logging.set_verbosity_warning
|
||||
|
||||
.. autofunction:: transformers.logging.set_verbosity_info
|
||||
|
||||
.. autofunction:: transformers.logging.set_verbosity_debug
|
||||
|
||||
Other functions
|
||||
~~~~~~~~~~~~~~~
|
||||
|
||||
.. autofunction:: transformers.logging.get_verbosity
|
||||
|
||||
.. autofunction:: transformers.logging.set_verbosity
|
||||
|
||||
.. autofunction:: transformers.logging.get_logger
|
||||
@@ -1,55 +0,0 @@
|
||||
Models
|
||||
----------------------------------------------------
|
||||
|
||||
The base classes :class:`~transformers.PreTrainedModel` and :class:`~transformers.TFPreTrainedModel` implement the
|
||||
common methods for loading/saving a model either from a local file or directory, or from a pretrained model
|
||||
configuration provided by the library (downloaded from HuggingFace's AWS S3 repository).
|
||||
|
||||
:class:`~transformers.PreTrainedModel` and :class:`~transformers.TFPreTrainedModel` also implement a few methods which
|
||||
are common among all the models to:
|
||||
|
||||
- resize the input token embeddings when new tokens are added to the vocabulary
|
||||
- prune the attention heads of the model.
|
||||
|
||||
The other methods that are common to each model are defined in :class:`~transformers.modeling_utils.ModuleUtilsMixin`
|
||||
(for the PyTorch models) and :class:`~transformers.modeling_tf_utils.TFModuleUtilsMixin` (for the TensorFlow models) or
|
||||
for text generation, :class:`~transformers.generation_utils.GenerationMixin` (for the PyTorch models) and
|
||||
:class:`~transformers.generation_tf_utils.TFGenerationMixin` (for the TensorFlow models)
|
||||
|
||||
|
||||
``PreTrainedModel``
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.PreTrainedModel
|
||||
:members:
|
||||
|
||||
|
||||
``ModuleUtilsMixin``
|
||||
~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.modeling_utils.ModuleUtilsMixin
|
||||
:members:
|
||||
|
||||
|
||||
``TFPreTrainedModel``
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFPreTrainedModel
|
||||
:members:
|
||||
|
||||
|
||||
``TFModelUtilsMixin``
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.modeling_tf_utils.TFModelUtilsMixin
|
||||
:members:
|
||||
|
||||
|
||||
Generative models
|
||||
~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.generation_utils.GenerationMixin
|
||||
:members:
|
||||
|
||||
.. autoclass:: transformers.generation_tf_utils.TFGenerationMixin
|
||||
:members:
|
||||
@@ -1,77 +0,0 @@
|
||||
Optimization
|
||||
----------------------------------------------------
|
||||
|
||||
The ``.optimization`` module provides:
|
||||
|
||||
- an optimizer with weight decay fixed that can be used to fine-tuned models, and
|
||||
- several schedules in the form of schedule objects that inherit from ``_LRSchedule``:
|
||||
- a gradient accumulation class to accumulate the gradients of multiple batches
|
||||
|
||||
``AdamW`` (PyTorch)
|
||||
~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.AdamW
|
||||
:members:
|
||||
|
||||
``AdaFactor`` (PyTorch)
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.Adafactor
|
||||
|
||||
``AdamWeightDecay`` (TensorFlow)
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.AdamWeightDecay
|
||||
|
||||
.. autofunction:: transformers.create_optimizer
|
||||
|
||||
Schedules
|
||||
~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
Learning Rate Schedules (Pytorch)
|
||||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||||
|
||||
.. autofunction:: transformers.get_constant_schedule
|
||||
|
||||
|
||||
.. autofunction:: transformers.get_constant_schedule_with_warmup
|
||||
|
||||
.. image:: /imgs/warmup_constant_schedule.png
|
||||
:target: /imgs/warmup_constant_schedule.png
|
||||
:alt:
|
||||
|
||||
|
||||
.. autofunction:: transformers.get_cosine_schedule_with_warmup
|
||||
|
||||
.. image:: /imgs/warmup_cosine_schedule.png
|
||||
:target: /imgs/warmup_cosine_schedule.png
|
||||
:alt:
|
||||
|
||||
|
||||
.. autofunction:: transformers.get_cosine_with_hard_restarts_schedule_with_warmup
|
||||
|
||||
.. image:: /imgs/warmup_cosine_hard_restarts_schedule.png
|
||||
:target: /imgs/warmup_cosine_hard_restarts_schedule.png
|
||||
:alt:
|
||||
|
||||
|
||||
|
||||
.. autofunction:: transformers.get_linear_schedule_with_warmup
|
||||
|
||||
.. image:: /imgs/warmup_linear_schedule.png
|
||||
:target: /imgs/warmup_linear_schedule.png
|
||||
:alt:
|
||||
|
||||
``Warmup`` (TensorFlow)
|
||||
^^^^^^^^^^^^^^^^^^^^^^^
|
||||
|
||||
.. autoclass:: transformers.WarmUp
|
||||
:members:
|
||||
|
||||
Gradient Strategies
|
||||
~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
``GradientAccumulator`` (TensorFlow)
|
||||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||||
|
||||
.. autoclass:: transformers.GradientAccumulator
|
||||
@@ -1,141 +0,0 @@
|
||||
Model outputs
|
||||
-------------
|
||||
|
||||
PyTorch models have outputs that are instances of subclasses of :class:`~transformers.file_utils.ModelOutput`. Those
|
||||
are data structures containing all the information returned by the model, but that can also be used as tuples or
|
||||
dictionaries.
|
||||
|
||||
Let's see of this looks on an example:
|
||||
|
||||
.. code-block::
|
||||
|
||||
from transformers import BertTokenizer, BertForSequenceClassification
|
||||
import torch
|
||||
|
||||
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
|
||||
model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
|
||||
|
||||
inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
|
||||
labels = torch.tensor([1]).unsqueeze(0) # Batch size 1
|
||||
outputs = model(**inputs, labels=labels)
|
||||
|
||||
The ``outputs`` object is a :class:`~transformers.modeling_outputs.SequenceClassifierOutput`, as we can see in the
|
||||
documentation of that class below, it means it has an optional ``loss``, a ``logits`` an optional ``hidden_states`` and
|
||||
an optional ``attentions`` attribute. Here we have the ``loss`` since we passed along ``labels``, but we don't have
|
||||
``hidden_states`` and ``attentions`` because we didn't pass ``output_hidden_states=True`` or
|
||||
``output_attentions=True``.
|
||||
|
||||
You can access each attribute as you would usually do, and if that attribute has not been returned by the model, you
|
||||
will get ``None``. Here for instance ``outputs.loss`` is the loss computed by the model, and ``outputs.attentions`` is
|
||||
``None``.
|
||||
|
||||
When considering our ``outputs`` object as tuple, it only considers the attributes that don't have ``None`` values.
|
||||
Here for instance, it has two elements, ``loss`` then ``logits``, so
|
||||
|
||||
.. code-block::
|
||||
|
||||
outputs[:2]
|
||||
|
||||
will return the tuple ``(outputs.loss, outputs.logits)`` for instance.
|
||||
|
||||
When considering our ``outputs`` object as dictionary, it only considers the attributes that don't have ``None``
|
||||
values. Here for instance, it has two keys that are ``loss`` and ``logits``.
|
||||
|
||||
We document here the generic model outputs that are used by more than one model type. Specific output types are
|
||||
documented on their corresponding model page.
|
||||
|
||||
``ModelOutput``
|
||||
~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.file_utils.ModelOutput
|
||||
:members:
|
||||
|
||||
``BaseModelOutput``
|
||||
~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.modeling_outputs.BaseModelOutput
|
||||
:members:
|
||||
|
||||
``BaseModelOutputWithPooling``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.modeling_outputs.BaseModelOutputWithPooling
|
||||
:members:
|
||||
|
||||
``BaseModelOutputWithPast``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.modeling_outputs.BaseModelOutputWithPast
|
||||
:members:
|
||||
|
||||
``Seq2SeqModelOutput``
|
||||
~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.modeling_outputs.Seq2SeqModelOutput
|
||||
:members:
|
||||
|
||||
``CausalLMOutput``
|
||||
~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.modeling_outputs.CausalLMOutput
|
||||
:members:
|
||||
|
||||
``CausalLMOutputWithPast``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.modeling_outputs.CausalLMOutputWithPast
|
||||
:members:
|
||||
|
||||
``MaskedLMOutput``
|
||||
~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.modeling_outputs.MaskedLMOutput
|
||||
:members:
|
||||
|
||||
``Seq2SeqLMOutput``
|
||||
~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.modeling_outputs.Seq2SeqLMOutput
|
||||
:members:
|
||||
|
||||
``NextSentencePredictorOutput``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.modeling_outputs.NextSentencePredictorOutput
|
||||
:members:
|
||||
|
||||
``SequenceClassifierOutput``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.modeling_outputs.SequenceClassifierOutput
|
||||
:members:
|
||||
|
||||
``Seq2SeqSequenceClassifierOutput``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput
|
||||
:members:
|
||||
|
||||
``MultipleChoiceModelOutput``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.modeling_outputs.MultipleChoiceModelOutput
|
||||
:members:
|
||||
|
||||
``TokenClassifierOutput``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.modeling_outputs.TokenClassifierOutput
|
||||
:members:
|
||||
|
||||
``QuestionAnsweringModelOutput``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.modeling_outputs.QuestionAnsweringModelOutput
|
||||
:members:
|
||||
|
||||
``Seq2SeqQuestionAnsweringModelOutput``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput
|
||||
:members:
|
||||
@@ -1,120 +0,0 @@
|
||||
Pipelines
|
||||
----------------------------------------------------
|
||||
|
||||
The pipelines are a great and easy way to use models for inference. These pipelines are objects that abstract most
|
||||
of the complex code from the library, offering a simple API dedicated to several tasks, including Named Entity
|
||||
Recognition, Masked Language Modeling, Sentiment Analysis, Feature Extraction and Question Answering. See the
|
||||
:doc:`task summary <../task_summary>` for examples of use.
|
||||
|
||||
There are two categories of pipeline abstractions to be aware about:
|
||||
|
||||
- The :func:`~transformers.pipeline` which is the most powerful object encapsulating all other pipelines.
|
||||
- The other task-specific pipelines:
|
||||
|
||||
- :class:`~transformers.ConversationalPipeline`
|
||||
- :class:`~transformers.FeatureExtractionPipeline`
|
||||
- :class:`~transformers.FillMaskPipeline`
|
||||
- :class:`~transformers.QuestionAnsweringPipeline`
|
||||
- :class:`~transformers.SummarizationPipeline`
|
||||
- :class:`~transformers.TextClassificationPipeline`
|
||||
- :class:`~transformers.TextGenerationPipeline`
|
||||
- :class:`~transformers.TokenClassificationPipeline`
|
||||
- :class:`~transformers.TranslationPipeline`
|
||||
- :class:`~transformers.ZeroShotClassificationPipeline`
|
||||
- :class:`~transformers.Text2TextGenerationPipeline`
|
||||
|
||||
The pipeline abstraction
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
The `pipeline` abstraction is a wrapper around all the other available pipelines. It is instantiated as any
|
||||
other pipeline but requires an additional argument which is the `task`.
|
||||
|
||||
.. autofunction:: transformers.pipeline
|
||||
|
||||
|
||||
The task specific pipelines
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
ConversationalPipeline
|
||||
==========================================
|
||||
|
||||
.. autoclass:: transformers.Conversation
|
||||
|
||||
.. autoclass:: transformers.ConversationalPipeline
|
||||
:special-members: __call__
|
||||
:members:
|
||||
|
||||
FeatureExtractionPipeline
|
||||
==========================================
|
||||
|
||||
.. autoclass:: transformers.FeatureExtractionPipeline
|
||||
:special-members: __call__
|
||||
:members:
|
||||
|
||||
FillMaskPipeline
|
||||
==========================================
|
||||
|
||||
.. autoclass:: transformers.FillMaskPipeline
|
||||
:special-members: __call__
|
||||
:members:
|
||||
|
||||
NerPipeline
|
||||
==========================================
|
||||
|
||||
This class is an alias of the :class:`~transformers.TokenClassificationPipeline` defined below. Please refer to that
|
||||
pipeline for documentation and usage examples.
|
||||
|
||||
QuestionAnsweringPipeline
|
||||
==========================================
|
||||
|
||||
.. autoclass:: transformers.QuestionAnsweringPipeline
|
||||
:special-members: __call__
|
||||
:members:
|
||||
|
||||
SummarizationPipeline
|
||||
==========================================
|
||||
|
||||
.. autoclass:: transformers.SummarizationPipeline
|
||||
:special-members: __call__
|
||||
:members:
|
||||
|
||||
TextClassificationPipeline
|
||||
==========================================
|
||||
|
||||
.. autoclass:: transformers.TextClassificationPipeline
|
||||
:special-members: __call__
|
||||
:members:
|
||||
|
||||
TextGenerationPipeline
|
||||
==========================================
|
||||
|
||||
.. autoclass:: transformers.TextGenerationPipeline
|
||||
:special-members: __call__
|
||||
:members:
|
||||
|
||||
Text2TextGenerationPipeline
|
||||
==========================================
|
||||
|
||||
.. autoclass:: transformers.Text2TextGenerationPipeline
|
||||
:special-members: __call__
|
||||
:members:
|
||||
|
||||
TokenClassificationPipeline
|
||||
==========================================
|
||||
|
||||
.. autoclass:: transformers.TokenClassificationPipeline
|
||||
:special-members: __call__
|
||||
:members:
|
||||
|
||||
ZeroShotClassificationPipeline
|
||||
==========================================
|
||||
|
||||
.. autoclass:: transformers.ZeroShotClassificationPipeline
|
||||
:special-members: __call__
|
||||
:members:
|
||||
|
||||
Parent class: :obj:`Pipeline`
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.Pipeline
|
||||
:members:
|
||||
@@ -1,153 +0,0 @@
|
||||
Processors
|
||||
----------------------------------------------------
|
||||
|
||||
This library includes processors for several traditional tasks. These processors can be used to process a dataset into
|
||||
examples that can be fed to a model.
|
||||
|
||||
Processors
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
All processors follow the same architecture which is that of the
|
||||
:class:`~transformers.data.processors.utils.DataProcessor`. The processor returns a list
|
||||
of :class:`~transformers.data.processors.utils.InputExample`. These
|
||||
:class:`~transformers.data.processors.utils.InputExample` can be converted to
|
||||
:class:`~transformers.data.processors.utils.InputFeatures` in order to be fed to the model.
|
||||
|
||||
.. autoclass:: transformers.data.processors.utils.DataProcessor
|
||||
:members:
|
||||
|
||||
|
||||
.. autoclass:: transformers.data.processors.utils.InputExample
|
||||
:members:
|
||||
|
||||
|
||||
.. autoclass:: transformers.data.processors.utils.InputFeatures
|
||||
:members:
|
||||
|
||||
|
||||
GLUE
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
`General Language Understanding Evaluation (GLUE) <https://gluebenchmark.com/>`__ is a benchmark that evaluates
|
||||
the performance of models across a diverse set of existing NLU tasks. It was released together with the paper
|
||||
`GLUE: A multi-task benchmark and analysis platform for natural language understanding <https://openreview.net/pdf?id=rJ4km2R5t7>`__
|
||||
|
||||
This library hosts a total of 10 processors for the following tasks: MRPC, MNLI, MNLI (mismatched),
|
||||
CoLA, SST2, STSB, QQP, QNLI, RTE and WNLI.
|
||||
|
||||
Those processors are:
|
||||
- :class:`~transformers.data.processors.utils.MrpcProcessor`
|
||||
- :class:`~transformers.data.processors.utils.MnliProcessor`
|
||||
- :class:`~transformers.data.processors.utils.MnliMismatchedProcessor`
|
||||
- :class:`~transformers.data.processors.utils.Sst2Processor`
|
||||
- :class:`~transformers.data.processors.utils.StsbProcessor`
|
||||
- :class:`~transformers.data.processors.utils.QqpProcessor`
|
||||
- :class:`~transformers.data.processors.utils.QnliProcessor`
|
||||
- :class:`~transformers.data.processors.utils.RteProcessor`
|
||||
- :class:`~transformers.data.processors.utils.WnliProcessor`
|
||||
|
||||
Additionally, the following method can be used to load values from a data file and convert them to a list of
|
||||
:class:`~transformers.data.processors.utils.InputExample`.
|
||||
|
||||
.. automethod:: transformers.data.processors.glue.glue_convert_examples_to_features
|
||||
|
||||
Example usage
|
||||
^^^^^^^^^^^^^^^^^^^^^^^^^
|
||||
|
||||
An example using these processors is given in the `run_glue.py <https://github.com/huggingface/pytorch-transformers/blob/master/examples/text-classification/run_glue.py>`__ script.
|
||||
|
||||
|
||||
XNLI
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
`The Cross-Lingual NLI Corpus (XNLI) <https://www.nyu.edu/projects/bowman/xnli/>`__ is a benchmark that evaluates
|
||||
the quality of cross-lingual text representations.
|
||||
XNLI is crowd-sourced dataset based on `MultiNLI <http://www.nyu.edu/projects/bowman/multinli/>`: pairs of text are labeled with textual entailment
|
||||
annotations for 15 different languages (including both high-resource language such as English and low-resource languages such as Swahili).
|
||||
|
||||
It was released together with the paper
|
||||
`XNLI: Evaluating Cross-lingual Sentence Representations <https://arxiv.org/abs/1809.05053>`__
|
||||
|
||||
This library hosts the processor to load the XNLI data:
|
||||
- :class:`~transformers.data.processors.utils.XnliProcessor`
|
||||
|
||||
Please note that since the gold labels are available on the test set, evaluation is performed on the test set.
|
||||
|
||||
An example using these processors is given in the
|
||||
`run_xnli.py <https://github.com/huggingface/pytorch-transformers/blob/master/examples/text-classification/run_xnli.py>`__ script.
|
||||
|
||||
|
||||
SQuAD
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
`The Stanford Question Answering Dataset (SQuAD) <https://rajpurkar.github.io/SQuAD-explorer//>`__ is a benchmark that evaluates
|
||||
the performance of models on question answering. Two versions are available, v1.1 and v2.0. The first version (v1.1) was released together with the paper
|
||||
`SQuAD: 100,000+ Questions for Machine Comprehension of Text <https://arxiv.org/abs/1606.05250>`__. The second version (v2.0) was released alongside
|
||||
the paper `Know What You Don't Know: Unanswerable Questions for SQuAD <https://arxiv.org/abs/1806.03822>`__.
|
||||
|
||||
This library hosts a processor for each of the two versions:
|
||||
|
||||
Processors
|
||||
^^^^^^^^^^^^^^^^^^^^^^^^^
|
||||
|
||||
Those processors are:
|
||||
- :class:`~transformers.data.processors.utils.SquadV1Processor`
|
||||
- :class:`~transformers.data.processors.utils.SquadV2Processor`
|
||||
|
||||
They both inherit from the abstract class :class:`~transformers.data.processors.utils.SquadProcessor`
|
||||
|
||||
.. autoclass:: transformers.data.processors.squad.SquadProcessor
|
||||
:members:
|
||||
|
||||
Additionally, the following method can be used to convert SQuAD examples into :class:`~transformers.data.processors.utils.SquadFeatures`
|
||||
that can be used as model inputs.
|
||||
|
||||
.. automethod:: transformers.data.processors.squad.squad_convert_examples_to_features
|
||||
|
||||
These processors as well as the aforementionned method can be used with files containing the data as well as with the `tensorflow_datasets` package.
|
||||
Examples are given below.
|
||||
|
||||
|
||||
Example usage
|
||||
^^^^^^^^^^^^^^^^^^^^^^^^^
|
||||
Here is an example using the processors as well as the conversion method using data files:
|
||||
|
||||
Example::
|
||||
|
||||
# Loading a V2 processor
|
||||
processor = SquadV2Processor()
|
||||
examples = processor.get_dev_examples(squad_v2_data_dir)
|
||||
|
||||
# Loading a V1 processor
|
||||
processor = SquadV1Processor()
|
||||
examples = processor.get_dev_examples(squad_v1_data_dir)
|
||||
|
||||
features = squad_convert_examples_to_features(
|
||||
examples=examples,
|
||||
tokenizer=tokenizer,
|
||||
max_seq_length=max_seq_length,
|
||||
doc_stride=args.doc_stride,
|
||||
max_query_length=max_query_length,
|
||||
is_training=not evaluate,
|
||||
)
|
||||
|
||||
Using `tensorflow_datasets` is as easy as using a data file:
|
||||
|
||||
Example::
|
||||
|
||||
# tensorflow_datasets only handle Squad V1.
|
||||
tfds_examples = tfds.load("squad")
|
||||
examples = SquadV1Processor().get_examples_from_dataset(tfds_examples, evaluate=evaluate)
|
||||
|
||||
features = squad_convert_examples_to_features(
|
||||
examples=examples,
|
||||
tokenizer=tokenizer,
|
||||
max_seq_length=max_seq_length,
|
||||
doc_stride=args.doc_stride,
|
||||
max_query_length=max_query_length,
|
||||
is_training=not evaluate,
|
||||
)
|
||||
|
||||
|
||||
Another example using these processors is given in the
|
||||
`run_squad.py <https://github.com/huggingface/transformers/blob/master/examples/question-answering/run_squad.py>`__ script.
|
||||
@@ -1,59 +0,0 @@
|
||||
Tokenizer
|
||||
----------------------------------------------------
|
||||
|
||||
A tokenizer is in charge of preparing the inputs for a model. The library contains tokenizers for all the models. Most
|
||||
of the tokenizers are available in two flavors: a full python implementation and a "Fast" implementation based on the
|
||||
Rust library `tokenizers <https://github.com/huggingface/tokenizers>`__. The "Fast" implementations allows:
|
||||
|
||||
1. a significant speed-up in particular when doing batched tokenization and
|
||||
2. additional methods to map between the original string (character and words) and the token space (e.g. getting the
|
||||
index of the token comprising a given character or the span of characters corresponding to a given token). Currently
|
||||
no "Fast" implementation is available for the SentencePiece-based tokenizers (for T5, ALBERT, CamemBERT, XLMRoBERTa
|
||||
and XLNet models).
|
||||
|
||||
The base classes :class:`~transformers.PreTrainedTokenizer` and :class:`~transformers.PreTrainedTokenizerFast`
|
||||
implement the common methods for encoding string inputs in model inputs (see below) and instantiating/saving python and
|
||||
"Fast" tokenizers either from a local file or directory or from a pretrained tokenizer provided by the library
|
||||
(downloaded from HuggingFace's AWS S3 repository). They both rely on
|
||||
:class:`~transformers.tokenization_utils_base.PreTrainedTokenizerBase` that contains the common methods, and
|
||||
:class:`~transformers.tokenization_utils_base.SpecialTokensMixin`.
|
||||
|
||||
:class:`~transformers.PreTrainedTokenizer` and :class:`~transformers.PreTrainedTokenizerFast` thus implement the main
|
||||
methods for using all the tokenizers:
|
||||
|
||||
- Tokenizing (splitting strings in sub-word token strings), converting tokens strings to ids and back, and
|
||||
encoding/decoding (i.e., tokenizing and converting to integers).
|
||||
- Adding new tokens to the vocabulary in a way that is independent of the underlying structure (BPE, SentencePiece...).
|
||||
- Managing special tokens (like mask, beginning-of-sentence, etc.): adding them, assigning them to attributes in the
|
||||
tokenizer for easy access and making sure they are not split during tokenization.
|
||||
|
||||
:class:`~transformers.BatchEncoding` holds the output of the tokenizer's encoding methods (``__call__``,
|
||||
``encode_plus`` and ``batch_encode_plus``) and is derived from a Python dictionary. When the tokenizer is a pure python
|
||||
tokenizer, this class behaves just like a standard python dictionary and holds the various model inputs computed by these
|
||||
methods (``input_ids``, ``attention_mask``...). When the tokenizer is a "Fast" tokenizer (i.e., backed by HuggingFace
|
||||
`tokenizers library <https://github.com/huggingface/tokenizers>`__), this class provides in addition several advanced
|
||||
alignment methods which can be used to map between the original string (character and words) and the token space (e.g.,
|
||||
getting the index of the token comprising a given character or the span of characters corresponding to a given token).
|
||||
|
||||
|
||||
``PreTrainedTokenizer``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.PreTrainedTokenizer
|
||||
:special-members: __call__
|
||||
:members:
|
||||
|
||||
|
||||
``PreTrainedTokenizerFast``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.PreTrainedTokenizerFast
|
||||
:special-members: __call__
|
||||
:members:
|
||||
|
||||
|
||||
``BatchEncoding``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.BatchEncoding
|
||||
:members:
|
||||
@@ -1,75 +0,0 @@
|
||||
Trainer
|
||||
----------
|
||||
|
||||
The :class:`~transformers.Trainer` and :class:`~transformers.TFTrainer` classes provide an API for feature-complete
|
||||
training in most standard use cases. It's used in most of the :doc:`example scripts <../examples>`.
|
||||
|
||||
Before instantiating your :class:`~transformers.Trainer`/:class:`~transformers.TFTrainer`, create a
|
||||
:class:`~transformers.TrainingArguments`/:class:`~transformers.TFTrainingArguments` to access all the points of
|
||||
customization during training.
|
||||
|
||||
The API supports distributed training on multiple GPUs/TPUs, mixed precision through `NVIDIA Apex
|
||||
<https://github.com/NVIDIA/apex>`__ for PyTorch and :obj:`tf.keras.mixed_precision` for TensorFlow.
|
||||
|
||||
Both :class:`~transformers.Trainer` and :class:`~transformers.TFTrainer` contain the basic training loop supporting the
|
||||
previous features. To inject custom behavior you can subclass them and override the following methods:
|
||||
|
||||
- **get_train_dataloader**/**get_train_tfdataset** -- Creates the training DataLoader (PyTorch) or TF Dataset.
|
||||
- **get_eval_dataloader**/**get_eval_tfdataset** -- Creates the evaulation DataLoader (PyTorch) or TF Dataset.
|
||||
- **get_test_dataloader**/**get_test_tfdataset** -- Creates the test DataLoader (PyTorch) or TF Dataset.
|
||||
- **log** -- Logs information on the various objects watching training.
|
||||
- **setup_wandb** -- Setups wandb (see `here <https://docs.wandb.com/huggingface>`__ for more information).
|
||||
- **create_optimizer_and_scheduler** -- Setups the optimizer and learning rate scheduler if they were not passed at
|
||||
init.
|
||||
- **compute_loss** - Computes the loss on a batch of training inputs.
|
||||
- **training_step** -- Performs a training step.
|
||||
- **prediction_step** -- Performs an evaluation/test step.
|
||||
- **run_model** (TensorFlow only) -- Basic pass through the model.
|
||||
- **evaluate** -- Runs an evaluation loop and returns metrics.
|
||||
- **predict** -- Returns predictions (with metrics if labels are available) on a test set.
|
||||
|
||||
Here is an example of how to customize :class:`~transformers.Trainer` using a custom loss function:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
from transformers import Trainer
|
||||
class MyTrainer(Trainer):
|
||||
def compute_loss(self, model, inputs):
|
||||
labels = inputs.pop("labels")
|
||||
outputs = models(**inputs)
|
||||
logits = outputs[0]
|
||||
return my_custom_loss(logits, labels)
|
||||
|
||||
|
||||
``Trainer``
|
||||
~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.Trainer
|
||||
:members:
|
||||
|
||||
``TFTrainer``
|
||||
~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFTrainer
|
||||
:members:
|
||||
|
||||
``TrainingArguments``
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TrainingArguments
|
||||
:members:
|
||||
|
||||
``TFTrainingArguments``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFTrainingArguments
|
||||
:members:
|
||||
|
||||
Utilities
|
||||
~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.EvalPrediction
|
||||
|
||||
.. autofunction:: transformers.set_seed
|
||||
|
||||
.. autofunction:: transformers.torch_distributed_zero_first
|
||||
@@ -1,122 +0,0 @@
|
||||
# Migrating from previous packages
|
||||
|
||||
## Migrating from pytorch-transformers to 🤗 Transformers
|
||||
|
||||
Here is a quick summary of what you should take care of when migrating from `pytorch-transformers` to 🤗 Transformers.
|
||||
|
||||
### Positional order of some models' keywords inputs (`attention_mask`, `token_type_ids`...) changed
|
||||
|
||||
To be able to use Torchscript (see #1010, #1204 and #1195) the specific order of some models **keywords inputs** (`attention_mask`, `token_type_ids`...) has been changed.
|
||||
|
||||
If you used to call the models with keyword names for keyword arguments, e.g. `model(inputs_ids, attention_mask=attention_mask, token_type_ids=token_type_ids)`, this should not cause any change.
|
||||
|
||||
If you used to call the models with positional inputs for keyword arguments, e.g. `model(inputs_ids, attention_mask, token_type_ids)`, you may have to double check the exact order of input arguments.
|
||||
|
||||
## Migrating from pytorch-pretrained-bert
|
||||
|
||||
Here is a quick summary of what you should take care of when migrating from `pytorch-pretrained-bert` to 🤗 Transformers
|
||||
|
||||
### Models always output `tuples`
|
||||
|
||||
The main breaking change when migrating from `pytorch-pretrained-bert` to 🤗 Transformers is that the models forward method always outputs a `tuple` with various elements depending on the model and the configuration parameters.
|
||||
|
||||
The exact content of the tuples for each model are detailled in the models' docstrings and the [documentation](https://huggingface.co/transformers/).
|
||||
|
||||
In pretty much every case, you will be fine by taking the first element of the output as the output you previously used in `pytorch-pretrained-bert`.
|
||||
|
||||
Here is a `pytorch-pretrained-bert` to 🤗 Transformers conversion example for a `BertForSequenceClassification` classification model:
|
||||
|
||||
```python
|
||||
# Let's load our model
|
||||
model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
|
||||
|
||||
# If you used to have this line in pytorch-pretrained-bert:
|
||||
loss = model(input_ids, labels=labels)
|
||||
|
||||
# Now just use this line in 🤗 Transformers to extract the loss from the output tuple:
|
||||
outputs = model(input_ids, labels=labels)
|
||||
loss = outputs[0]
|
||||
|
||||
# In 🤗 Transformers you can also have access to the logits:
|
||||
loss, logits = outputs[:2]
|
||||
|
||||
# And even the attention weights if you configure the model to output them (and other outputs too, see the docstrings and documentation)
|
||||
model = BertForSequenceClassification.from_pretrained('bert-base-uncased', output_attentions=True)
|
||||
outputs = model(input_ids, labels=labels)
|
||||
loss, logits, attentions = outputs
|
||||
```
|
||||
|
||||
### Serialization
|
||||
|
||||
Breaking change in the `from_pretrained()`method:
|
||||
|
||||
1. Models are now set in evaluation mode by default when instantiated with the `from_pretrained()` method. To train them don't forget to set them back in training mode (`model.train()`) to activate the dropout modules.
|
||||
|
||||
2. The additional `*inputs` and `**kwargs` arguments supplied to the `from_pretrained()` method used to be directly passed to the underlying model's class `__init__()` method. They are now used to update the model configuration attribute first which can break derived model classes build based on the previous `BertForSequenceClassification` examples. More precisely, the positional arguments `*inputs` provided to `from_pretrained()` are directly forwarded the model `__init__()` method while the keyword arguments `**kwargs` (i) which match configuration class attributes are used to update said attributes (ii) which don't match any configuration class attributes are forwarded to the model `__init__()` method.
|
||||
|
||||
Also, while not a breaking change, the serialization methods have been standardized and you probably should switch to the new method `save_pretrained(save_directory)` if you were using any other serialization method before.
|
||||
|
||||
Here is an example:
|
||||
|
||||
```python
|
||||
### Let's load a model and tokenizer
|
||||
model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
|
||||
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
|
||||
|
||||
### Do some stuff to our model and tokenizer
|
||||
# Ex: add new tokens to the vocabulary and embeddings of our model
|
||||
tokenizer.add_tokens(['[SPECIAL_TOKEN_1]', '[SPECIAL_TOKEN_2]'])
|
||||
model.resize_token_embeddings(len(tokenizer))
|
||||
# Train our model
|
||||
train(model)
|
||||
|
||||
### Now let's save our model and tokenizer to a directory
|
||||
model.save_pretrained('./my_saved_model_directory/')
|
||||
tokenizer.save_pretrained('./my_saved_model_directory/')
|
||||
|
||||
### Reload the model and the tokenizer
|
||||
model = BertForSequenceClassification.from_pretrained('./my_saved_model_directory/')
|
||||
tokenizer = BertTokenizer.from_pretrained('./my_saved_model_directory/')
|
||||
```
|
||||
|
||||
### Optimizers: BertAdam & OpenAIAdam are now AdamW, schedules are standard PyTorch schedules
|
||||
|
||||
The two optimizers previously included, `BertAdam` and `OpenAIAdam`, have been replaced by a single `AdamW` optimizer which has a few differences:
|
||||
|
||||
- it only implements weights decay correction,
|
||||
- schedules are now externals (see below),
|
||||
- gradient clipping is now also external (see below).
|
||||
|
||||
The new optimizer `AdamW` matches PyTorch `Adam` optimizer API and let you use standard PyTorch or apex methods for the schedule and clipping.
|
||||
|
||||
The schedules are now standard [PyTorch learning rate schedulers](https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate) and not part of the optimizer anymore.
|
||||
|
||||
Here is a conversion examples from `BertAdam` with a linear warmup and decay schedule to `AdamW` and the same schedule:
|
||||
|
||||
```python
|
||||
# Parameters:
|
||||
lr = 1e-3
|
||||
max_grad_norm = 1.0
|
||||
num_training_steps = 1000
|
||||
num_warmup_steps = 100
|
||||
warmup_proportion = float(num_warmup_steps) / float(num_training_steps) # 0.1
|
||||
|
||||
### Previously BertAdam optimizer was instantiated like this:
|
||||
optimizer = BertAdam(model.parameters(), lr=lr, schedule='warmup_linear', warmup=warmup_proportion, num_training_steps=num_training_steps)
|
||||
### and used like this:
|
||||
for batch in train_data:
|
||||
loss = model(batch)
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
### In 🤗 Transformers, optimizer and schedules are splitted and instantiated like this:
|
||||
optimizer = AdamW(model.parameters(), lr=lr, correct_bias=False) # To reproduce BertAdam specific behavior set correct_bias=False
|
||||
scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps) # PyTorch scheduler
|
||||
### and used like this:
|
||||
for batch in train_data:
|
||||
loss = model(batch)
|
||||
loss.backward()
|
||||
torch.nn.utils.clip_grad_norm_(model.parameters(), max_grad_norm) # Gradient clipping is not in AdamW anymore (so you can use amp without issue)
|
||||
optimizer.step()
|
||||
scheduler.step()
|
||||
```
|
||||
@@ -1,155 +0,0 @@
|
||||
ALBERT
|
||||
----------------------------------------------------
|
||||
|
||||
Overview
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
The ALBERT model was proposed in `ALBERT: A Lite BERT for Self-supervised Learning of Language Representations <https://arxiv.org/abs/1909.11942>`_
|
||||
by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut. It presents
|
||||
two parameter-reduction techniques to lower memory consumption and increase the training speed of BERT:
|
||||
|
||||
- Splitting the embedding matrix into two smaller matrices
|
||||
- Using repeating layers split among groups
|
||||
|
||||
The abstract from the paper is the following:
|
||||
|
||||
*Increasing model size when pretraining natural language representations often results in improved performance on
|
||||
downstream tasks. However, at some point further model increases become harder due to GPU/TPU memory limitations,
|
||||
longer training times, and unexpected model degradation. To address these problems, we present two parameter-reduction
|
||||
techniques to lower memory consumption and increase the training speed of BERT. Comprehensive empirical evidence shows
|
||||
that our proposed methods lead to models that scale much better compared to the original BERT. We also use a
|
||||
self-supervised loss that focuses on modeling inter-sentence coherence, and show it consistently helps downstream
|
||||
tasks with multi-sentence inputs. As a result, our best model establishes new state-of-the-art results on the GLUE,
|
||||
RACE, and SQuAD benchmarks while having fewer parameters compared to BERT-large.*
|
||||
|
||||
Tips:
|
||||
|
||||
- ALBERT is a model with absolute position embeddings so it's usually advised to pad the inputs on
|
||||
the right rather than the left.
|
||||
- ALBERT uses repeating layers which results in a small memory footprint, however the computational cost remains
|
||||
similar to a BERT-like architecture with the same number of hidden layers as it has to iterate through the same
|
||||
number of (repeating) layers.
|
||||
|
||||
The original code can be found `here <https://github.com/google-research/ALBERT>`_.
|
||||
|
||||
AlbertConfig
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.AlbertConfig
|
||||
:members:
|
||||
|
||||
|
||||
AlbertTokenizer
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.AlbertTokenizer
|
||||
:members: build_inputs_with_special_tokens, get_special_tokens_mask,
|
||||
create_token_type_ids_from_sequences, save_vocabulary
|
||||
|
||||
|
||||
Albert specific outputs
|
||||
~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.modeling_albert.AlbertForPreTrainingOutput
|
||||
:members:
|
||||
|
||||
.. autoclass:: transformers.modeling_tf_albert.TFAlbertForPreTrainingOutput
|
||||
:members:
|
||||
|
||||
|
||||
AlbertModel
|
||||
~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.AlbertModel
|
||||
:members:
|
||||
|
||||
|
||||
AlbertForPreTraining
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.AlbertForPreTraining
|
||||
:members:
|
||||
|
||||
|
||||
AlbertForMaskedLM
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.AlbertForMaskedLM
|
||||
:members:
|
||||
|
||||
|
||||
AlbertForSequenceClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.AlbertForSequenceClassification
|
||||
:members:
|
||||
|
||||
|
||||
AlbertForMultipleChoice
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.AlbertForMultipleChoice
|
||||
:members:
|
||||
|
||||
|
||||
AlbertForTokenClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.AlbertForTokenClassification
|
||||
:members:
|
||||
|
||||
|
||||
AlbertForQuestionAnswering
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.AlbertForQuestionAnswering
|
||||
:members:
|
||||
|
||||
|
||||
TFAlbertModel
|
||||
~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFAlbertModel
|
||||
:members:
|
||||
|
||||
|
||||
TFAlbertForPreTraining
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFAlbertForPreTraining
|
||||
:members:
|
||||
|
||||
|
||||
TFAlbertForMaskedLM
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFAlbertForMaskedLM
|
||||
:members:
|
||||
|
||||
|
||||
TFAlbertForSequenceClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFAlbertForSequenceClassification
|
||||
:members:
|
||||
|
||||
|
||||
TFAlbertForMultipleChoice
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFAlbertForMultipleChoice
|
||||
:members:
|
||||
|
||||
|
||||
TFAlbertForTokenClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFAlbertForTokenClassification
|
||||
:members:
|
||||
|
||||
|
||||
TFAlbertForQuestionAnswering
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFAlbertForQuestionAnswering
|
||||
:members:
|
||||
@@ -1,109 +0,0 @@
|
||||
AutoModels
|
||||
-----------
|
||||
|
||||
In many cases, the architecture you want to use can be guessed from the name or the path of the pretrained model you
|
||||
are supplying to the ``from_pretrained`` method.
|
||||
|
||||
AutoClasses are here to do this job for you so that you automatically retrieve the relevant model given the name/path
|
||||
to the pretrained weights/config/vocabulary:
|
||||
|
||||
Instantiating one of ``AutoModel``, ``AutoConfig`` and ``AutoTokenizer`` will directly create a class of the relevant
|
||||
architecture (ex: ``model = AutoModel.from_pretrained('bert-base-cased')`` will create a instance of
|
||||
:class:`~transformers.BertModel`).
|
||||
|
||||
|
||||
``AutoConfig``
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.AutoConfig
|
||||
:members:
|
||||
|
||||
|
||||
``AutoTokenizer``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.AutoTokenizer
|
||||
:members:
|
||||
|
||||
|
||||
``AutoModel``
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.AutoModel
|
||||
:members:
|
||||
|
||||
|
||||
``AutoModelForPreTraining``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.AutoModelForPreTraining
|
||||
:members:
|
||||
|
||||
|
||||
``AutoModelWithLMHead``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.AutoModelWithLMHead
|
||||
:members:
|
||||
|
||||
|
||||
``AutoModelForSequenceClassification``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.AutoModelForSequenceClassification
|
||||
:members:
|
||||
|
||||
|
||||
``AutoModelForQuestionAnswering``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.AutoModelForQuestionAnswering
|
||||
:members:
|
||||
|
||||
|
||||
``AutoModelForTokenClassification``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.AutoModelForTokenClassification
|
||||
:members:
|
||||
|
||||
``TFAutoModel``
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFAutoModel
|
||||
:members:
|
||||
|
||||
|
||||
``TFAutoModelForPreTraining``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFAutoModelForPreTraining
|
||||
:members:
|
||||
|
||||
|
||||
``TFAutoModelWithLMHead``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFAutoModelWithLMHead
|
||||
:members:
|
||||
|
||||
|
||||
``TFAutoModelForSequenceClassification``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFAutoModelForSequenceClassification
|
||||
:members:
|
||||
|
||||
|
||||
``TFAutoModelForQuestionAnswering``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFAutoModelForQuestionAnswering
|
||||
:members:
|
||||
|
||||
|
||||
``TFAutoModelForTokenClassification``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFAutoModelForTokenClassification
|
||||
:members:
|
||||
@@ -1,75 +0,0 @@
|
||||
Bart
|
||||
----------------------------------------------------
|
||||
**DISCLAIMER:** If you see something strange,
|
||||
file a `Github Issue <https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title>`__ and assign
|
||||
@sshleifer
|
||||
|
||||
Overview
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
The Bart model was `proposed <https://arxiv.org/abs/1910.13461>`_ by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer on 29 Oct, 2019.
|
||||
According to the abstract,
|
||||
|
||||
- Bart uses a standard seq2seq/machine translation architecture with a bidirectional encoder (like BERT) and a left-to-right decoder (like GPT).
|
||||
- The pretraining task involves randomly shuffling the order of the original sentences and a novel in-filling scheme, where spans of text are replaced with a single mask token.
|
||||
- BART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. It matches the performance of RoBERTa with comparable training resources on GLUE and SQuAD, achieves new state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains of up to 6 ROUGE.
|
||||
|
||||
The Authors' code can be found `here <https://github.com/pytorch/fairseq/tree/master/examples/bart>`_
|
||||
|
||||
|
||||
Implementation Notes
|
||||
~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
- Bart doesn't use :obj:`token_type_ids` for sequence classification. Use BartTokenizer.encode to get the proper splitting.
|
||||
- The forward pass of ``BartModel`` will create decoder inputs (using the helper function ``transformers.modeling_bart._prepare_bart_decoder_inputs``) if they are not passed. This is different than some other modeling APIs.
|
||||
- Model predictions are intended to be identical to the original implementation. This only works, however, if the string you pass to ``fairseq.encode`` starts with a space.
|
||||
- ``BartForConditionalGeneration.generate`` should be used for conditional generation tasks like summarization, see the example in that docstrings
|
||||
- Models that load the ``"facebook/bart-large-cnn"`` weights will not have a ``mask_token_id``, or be able to perform mask filling tasks.
|
||||
- for training/forward passes that don't involve beam search, pass ``use_cache=False``
|
||||
|
||||
|
||||
BartForConditionalGeneration
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.BartForConditionalGeneration
|
||||
:members: forward
|
||||
|
||||
|
||||
BartConfig
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.BartConfig
|
||||
:members:
|
||||
|
||||
|
||||
BartTokenizer
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.BartTokenizer
|
||||
:members:
|
||||
|
||||
|
||||
|
||||
BartModel
|
||||
~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.BartModel
|
||||
:members: forward
|
||||
|
||||
.. autofunction:: transformers.modeling_bart._prepare_bart_decoder_inputs
|
||||
|
||||
|
||||
BartForSequenceClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.BartForSequenceClassification
|
||||
:members: forward
|
||||
|
||||
|
||||
BartForQuestionAnswering
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.BartForQuestionAnswering
|
||||
:members: forward
|
||||
|
||||
|
||||
@@ -1,191 +0,0 @@
|
||||
BERT
|
||||
----------------------------------------------------
|
||||
|
||||
Overview
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
The BERT model was proposed in `BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding <https://arxiv.org/abs/1810.04805>`__
|
||||
by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova. It's a bidirectional transformer
|
||||
pre-trained using a combination of masked language modeling objective and next sentence prediction
|
||||
on a large corpus comprising the Toronto Book Corpus and Wikipedia.
|
||||
|
||||
The abstract from the paper is the following:
|
||||
|
||||
*We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations
|
||||
from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional
|
||||
representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result,
|
||||
the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models
|
||||
for a wide range of tasks, such as question answering and language inference, without substantial task-specific
|
||||
architecture modifications.*
|
||||
|
||||
*BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural
|
||||
language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI
|
||||
accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute
|
||||
improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).*
|
||||
|
||||
Tips:
|
||||
|
||||
- BERT is a model with absolute position embeddings so it's usually advised to pad the inputs on
|
||||
the right rather than the left.
|
||||
- BERT was trained with the masked language modeling (MLM) and next sentence prediction (NSP) objectives. It is efficient at predicting masked
|
||||
tokens and at NLU in general, but is not optimal for text generation.
|
||||
|
||||
The original code can be found `here <https://github.com/google-research/bert>`_.
|
||||
|
||||
BertConfig
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.BertConfig
|
||||
:members:
|
||||
|
||||
|
||||
BertTokenizer
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.BertTokenizer
|
||||
:members: build_inputs_with_special_tokens, get_special_tokens_mask,
|
||||
create_token_type_ids_from_sequences, save_vocabulary
|
||||
|
||||
|
||||
BertTokenizerFast
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.BertTokenizerFast
|
||||
:members:
|
||||
|
||||
|
||||
Bert specific outputs
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.modeling_bert.BertForPreTrainingOutput
|
||||
:members:
|
||||
|
||||
.. autoclass:: transformers.modeling_tf_bert.TFBertForPreTrainingOutput
|
||||
:members:
|
||||
|
||||
|
||||
BertModel
|
||||
~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.BertModel
|
||||
:members:
|
||||
|
||||
|
||||
BertForPreTraining
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.BertForPreTraining
|
||||
:members:
|
||||
|
||||
|
||||
BertModelLMHeadModel
|
||||
~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.BertLMHeadModel
|
||||
:members:
|
||||
|
||||
|
||||
BertForMaskedLM
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.BertForMaskedLM
|
||||
:members:
|
||||
|
||||
|
||||
BertForNextSentencePrediction
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.BertForNextSentencePrediction
|
||||
:members:
|
||||
|
||||
|
||||
BertForSequenceClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.BertForSequenceClassification
|
||||
:members:
|
||||
|
||||
|
||||
BertForMultipleChoice
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.BertForMultipleChoice
|
||||
:members:
|
||||
|
||||
|
||||
BertForTokenClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.BertForTokenClassification
|
||||
:members:
|
||||
|
||||
|
||||
BertForQuestionAnswering
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.BertForQuestionAnswering
|
||||
:members:
|
||||
|
||||
|
||||
TFBertModel
|
||||
~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFBertModel
|
||||
:members:
|
||||
|
||||
|
||||
TFBertForPreTraining
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFBertForPreTraining
|
||||
:members:
|
||||
|
||||
|
||||
TFBertModelLMHeadModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFBertLMHeadModel
|
||||
:members:
|
||||
|
||||
|
||||
TFBertForMaskedLM
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFBertForMaskedLM
|
||||
:members:
|
||||
|
||||
|
||||
TFBertForNextSentencePrediction
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFBertForNextSentencePrediction
|
||||
:members:
|
||||
|
||||
|
||||
TFBertForSequenceClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFBertForSequenceClassification
|
||||
:members:
|
||||
|
||||
|
||||
TFBertForMultipleChoice
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFBertForMultipleChoice
|
||||
:members:
|
||||
|
||||
|
||||
TFBertForTokenClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFBertForTokenClassification
|
||||
:members:
|
||||
|
||||
|
||||
TFBertForQuestionAnswering
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFBertForQuestionAnswering
|
||||
:members:
|
||||
|
||||
@@ -1,82 +0,0 @@
|
||||
BertGeneration
|
||||
----------------------------------------------------
|
||||
|
||||
Overview
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
The BertGeneration model is a BERT model that can be leveraged for sequence-to-sequence tasks using :class:`~transformers.EncoderDecoderModel` as proposed in `Leveraging Pre-trained Checkpoints for Sequence Generation Tasks <https://arxiv.org/abs/1907.12461>`__ by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
|
||||
|
||||
The abstract from the paper is the following:
|
||||
|
||||
*Unsupervised pre-training of large neural models has recently revolutionized Natural Language Processing. By warm-starting from the publicly released checkpoints, NLP practitioners have pushed the state-of-the-art on multiple benchmarks while saving significant amounts of compute time. So far the focus has been mainly on the Natural Language Understanding tasks. In this paper, we demonstrate the efficacy of pre-trained checkpoints for Sequence Generation. We developed a Transformer-based sequence-to-sequence model that is compatible with publicly available pre-trained BERT, GPT-2 and RoBERTa checkpoints and conducted an extensive empirical study on the utility of initializing our model, both encoder and decoder, with these checkpoints. Our models result in new state-of-the-art results on Machine Translation, Text Summarization, Sentence Splitting, and Sentence Fusion.*
|
||||
|
||||
Usage:
|
||||
|
||||
- The model can be used in combination with the :class:`~transformers.EncoderDecoderModel` to leverage two bert pretrained bert checkpoints for subsequent fine-tuning.
|
||||
|
||||
::
|
||||
|
||||
# leverage checkpoints for Bert2Bert model...
|
||||
encoder = BertGenerationEncoder.from_pretrained("bert-large-uncased", bos_token_id=101, eos_token_id=102) # use BERT's cls token as BOS token and sep token as EOS token
|
||||
decoder = BertGenerationDecoder.from_pretrained("bert-large-uncased", add_cross_attention=True, is_decoder=True, bos_token_id=101, eos_token_id=102) # add cross attention layers and use BERT's cls token as BOS token and sep token as EOS token
|
||||
bert2bert = EncoderDecoderModel(encoder=encoder, decoder=decoder)
|
||||
|
||||
# create tokenizer...
|
||||
tokenizer = BertTokenizer.from_pretrained("bert-large-uncased")
|
||||
|
||||
input_ids = tokenizer('This is a long article to summarize', add_special_tokens=False, return_tensors="pt").input_ids
|
||||
labels = tokenizer('This is a short summary', return_tensors="pt").input_ids
|
||||
|
||||
# train...
|
||||
loss = bert2bert(input_ids=input_ids, decoder_input_ids=labels, labels=labels, return_dict=True).loss
|
||||
loss.backward()
|
||||
|
||||
|
||||
- Pretrained :class:`~transformers.EncoderDecoderModel` are also directly available in the model hub, *e.g.*:
|
||||
|
||||
|
||||
::
|
||||
|
||||
# instantiate sentence fusion model
|
||||
sentence_fuser = EncoderDecoderModel.from_pretrained("google/roberta2roberta_L-24_discofuse")
|
||||
tokenizer = AutoTokenizer.from_pretrained("google/roberta2roberta_L-24_discofuse")
|
||||
|
||||
input_ids = tokenizer('This is the first sentence. This is the second sentence.', add_special_tokens=False, return_tensors="pt").input_ids
|
||||
|
||||
outputs = sentence_fuser.generate(input_ids)
|
||||
|
||||
print(tokenizer.decode(outputs[0]))
|
||||
|
||||
|
||||
Tips:
|
||||
|
||||
- :class:`~transformers.BertGenerationEncoder` and :class:`~transformers.BertGenerationDecoder` should be used in combination with :class:`~transformers.EncoderDecoder`.
|
||||
- For summarization, sentence splitting, sentence fusion and translation, no special tokens are required for the input. Therefore, no EOS token should be added to the end of the input.
|
||||
|
||||
The original code can be found `here <https://tfhub.dev/s?module-type=text-generation&subtype=module,placeholder>`__.
|
||||
|
||||
BertGenerationConfig
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.BertGenerationConfig
|
||||
:members:
|
||||
|
||||
|
||||
BertGenerationTokenizer
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.BertGenerationTokenizer
|
||||
:members:
|
||||
|
||||
BertGenerationEncoder
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.BertGenerationEncoder
|
||||
:members:
|
||||
|
||||
|
||||
BertGenerationDecoder
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.BertGenerationDecoder
|
||||
:members:
|
||||
@@ -1,133 +0,0 @@
|
||||
CamemBERT
|
||||
----------------------------------------------------
|
||||
|
||||
Overview
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
The CamemBERT model was proposed in `CamemBERT: a Tasty French Language Model <https://arxiv.org/abs/1911.03894>`__
|
||||
by Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suárez, Yoann Dupont, Laurent Romary, Éric Villemonte de la
|
||||
Clergerie, Djamé Seddah, and Benoît Sagot. It is based on Facebook's RoBERTa model released in 2019. It is a model
|
||||
trained on 138GB of French text.
|
||||
|
||||
The abstract from the paper is the following:
|
||||
|
||||
*Pretrained language models are now ubiquitous in Natural Language Processing. Despite their success,
|
||||
most available models have either been trained on English data or on the concatenation of data in multiple
|
||||
languages. This makes practical use of such models --in all languages except English-- very limited. Aiming
|
||||
to address this issue for French, we release CamemBERT, a French version of the Bi-directional Encoders for
|
||||
Transformers (BERT). We measure the performance of CamemBERT compared to multilingual models in multiple
|
||||
downstream tasks, namely part-of-speech tagging, dependency parsing, named-entity recognition, and natural
|
||||
language inference. CamemBERT improves the state of the art for most of the tasks considered. We release the
|
||||
pretrained model for CamemBERT hoping to foster research and downstream applications for French NLP.*
|
||||
|
||||
Tips:
|
||||
|
||||
- This implementation is the same as RoBERTa. Refer to the `documentation of RoBERTa <./roberta.html>`__ for usage
|
||||
examples as well as the information relative to the inputs and outputs.
|
||||
|
||||
The original code can be found `here <https://camembert-model.fr/>`_.
|
||||
|
||||
CamembertConfig
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.CamembertConfig
|
||||
:members:
|
||||
|
||||
|
||||
CamembertTokenizer
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.CamembertTokenizer
|
||||
:members: build_inputs_with_special_tokens, get_special_tokens_mask,
|
||||
create_token_type_ids_from_sequences, save_vocabulary
|
||||
|
||||
|
||||
CamembertModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.CamembertModel
|
||||
:members:
|
||||
|
||||
|
||||
CamembertForCausalLM
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.CamembertForCausalLM
|
||||
:members:
|
||||
|
||||
|
||||
CamembertForMaskedLM
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.CamembertForMaskedLM
|
||||
:members:
|
||||
|
||||
|
||||
CamembertForSequenceClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.CamembertForSequenceClassification
|
||||
:members:
|
||||
|
||||
|
||||
CamembertForMultipleChoice
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.CamembertForMultipleChoice
|
||||
:members:
|
||||
|
||||
|
||||
CamembertForTokenClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.CamembertForTokenClassification
|
||||
:members:
|
||||
|
||||
|
||||
CamembertForQuestionAnswering
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.CamembertForQuestionAnswering
|
||||
:members:
|
||||
|
||||
|
||||
TFCamembertModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFCamembertModel
|
||||
:members:
|
||||
|
||||
|
||||
TFCamembertForMaskedLM
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFCamembertForMaskedLM
|
||||
:members:
|
||||
|
||||
|
||||
TFCamembertForSequenceClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFCamembertForSequenceClassification
|
||||
:members:
|
||||
|
||||
|
||||
TFCamembertForMultipleChoice
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFCamembertForMultipleChoice
|
||||
:members:
|
||||
|
||||
|
||||
TFCamembertForTokenClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFCamembertForTokenClassification
|
||||
:members:
|
||||
|
||||
|
||||
TFCamembertForQuestionAnswering
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFCamembertForQuestionAnswering
|
||||
:members:
|
||||
@@ -1,80 +0,0 @@
|
||||
CTRL
|
||||
----------------------------------------------------
|
||||
|
||||
Overview
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
CTRL model was proposed in `CTRL: A Conditional Transformer Language Model for Controllable Generation <https://arxiv.org/abs/1909.05858>`_
|
||||
by Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and Richard Socher.
|
||||
It's a causal (unidirectional) transformer pre-trained using language modeling on a very large
|
||||
corpus of ~140 GB of text data with the first token reserved as a control code (such as Links, Books, Wikipedia etc.).
|
||||
|
||||
The abstract from the paper is the following:
|
||||
|
||||
*Large-scale language models show promising text generation capabilities, but users cannot easily control particular
|
||||
aspects of the generated text. We release CTRL, a 1.63 billion-parameter conditional transformer language model,
|
||||
trained to condition on control codes that govern style, content, and task-specific behavior. Control codes were
|
||||
derived from structure that naturally co-occurs with raw text, preserving the advantages of unsupervised learning
|
||||
while providing more explicit control over text generation. These codes also allow CTRL to predict which parts of
|
||||
the training data are most likely given a sequence. This provides a potential method for analyzing large amounts
|
||||
of data via model-based source attribution.*
|
||||
|
||||
Tips:
|
||||
|
||||
- CTRL makes use of control codes to generate text: it requires generations to be started by certain words, sentences
|
||||
or links to generate coherent text. Refer to the `original implementation <https://github.com/salesforce/ctrl>`__
|
||||
for more information.
|
||||
- CTRL is a model with absolute position embeddings so it's usually advised to pad the inputs on
|
||||
the right rather than the left.
|
||||
- CTRL was trained with a causal language modeling (CLM) objective and is therefore powerful at predicting the next
|
||||
token in a sequence. Leveraging this feature allows CTRL to generate syntactically coherent text as
|
||||
it can be observed in the `run_generation.py` example script.
|
||||
- The PyTorch models can take the `past` as input, which is the previously computed key/value attention pairs. Using
|
||||
this `past` value prevents the model from re-computing pre-computed values in the context of text generation.
|
||||
See `reusing the past in generative models <../quickstart.html#using-the-past>`_ for more information on the usage
|
||||
of this argument.
|
||||
|
||||
The original code can be found `here <https://github.com/salesforce/ctrl>`_.
|
||||
|
||||
|
||||
CTRLConfig
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.CTRLConfig
|
||||
:members:
|
||||
|
||||
|
||||
CTRLTokenizer
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.CTRLTokenizer
|
||||
:members: save_vocabulary
|
||||
|
||||
|
||||
CTRLModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.CTRLModel
|
||||
:members:
|
||||
|
||||
|
||||
CTRLLMHeadModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.CTRLLMHeadModel
|
||||
:members:
|
||||
|
||||
|
||||
TFCTRLModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFCTRLModel
|
||||
:members:
|
||||
|
||||
|
||||
TFCTRLLMHeadModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFCTRLLMHeadModel
|
||||
:members:
|
||||
|
||||
@@ -1,39 +0,0 @@
|
||||
DialoGPT
|
||||
----------------------------------------------------
|
||||
|
||||
Overview
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
DialoGPT was proposed in
|
||||
`DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation <https://arxiv.org/abs/1911.00536>`_
|
||||
by Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan.
|
||||
It's a GPT2 Model trained on 147M conversation-like exchanges extracted from Reddit.
|
||||
|
||||
The abstract from the paper is the following:
|
||||
|
||||
*We present a large, tunable neural conversational response generation model, DialoGPT (dialogue generative pre-trained transformer).
|
||||
Trained on 147M conversation-like exchanges extracted from Reddit comment chains over a period spanning from 2005 through 2017, DialoGPT extends the Hugging Face PyTorch transformer to attain a performance close to human both in terms of automatic and human evaluation in single-turn dialogue settings.
|
||||
We show that conversational systems that leverage DialoGPT generate more relevant, contentful and context-consistent responses than strong baseline systems.
|
||||
The pre-trained model and training pipeline are publicly released to facilitate research into neural response generation and the development of more intelligent open-domain dialogue systems.*
|
||||
|
||||
Tips:
|
||||
|
||||
- DialoGPT is a model with absolute position embeddings so it's usually advised to pad the inputs on
|
||||
the right rather than the left.
|
||||
- DialoGPT was trained with a causal language modeling (CLM) objective on conversational data and is therefore powerful at response generation in open-domain dialogue systems.
|
||||
- DialoGPT enables the user to create a chat bot in just 10 lines of code as shown on `DialoGPT's model card <https://huggingface.co/microsoft/DialoGPT-medium>`_.
|
||||
|
||||
Training:
|
||||
|
||||
In order to train or fine-tune DialoGPT, one can use causal language modeling training.
|
||||
To cite the official paper:
|
||||
*We follow the OpenAI GPT-2 to model a multiturn dialogue session
|
||||
as a long text and frame the generation task as language modeling. We first
|
||||
concatenate all dialog turns within a dialogue session into a long text
|
||||
x_1,..., x_N (N is the sequence length), ended by the end-of-text token.*
|
||||
For more information please confer to the original paper.
|
||||
|
||||
|
||||
DialoGPT's architecture is based on the GPT2 model, so one can refer to GPT2's `docstring <https://huggingface.co/transformers/model_doc/gpt2.html>`_.
|
||||
|
||||
The original code can be found `here <https://github.com/microsoft/DialoGPT>`_.
|
||||
@@ -1,139 +0,0 @@
|
||||
DistilBERT
|
||||
----------------------------------------------------
|
||||
|
||||
Overview
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
The DistilBERT model was proposed in the blog post
|
||||
`Smaller, faster, cheaper, lighter: Introducing DistilBERT, a distilled version of BERT <https://medium.com/huggingface/distilbert-8cf3380435b5>`__,
|
||||
and the paper `DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter <https://arxiv.org/abs/1910.01108>`__.
|
||||
DistilBERT is a small, fast, cheap and light Transformer model trained by distilling Bert base. It has 40% less
|
||||
parameters than `bert-base-uncased`, runs 60% faster while preserving over 95% of Bert's performances as measured on
|
||||
the GLUE language understanding benchmark.
|
||||
|
||||
The abstract from the paper is the following:
|
||||
|
||||
*As Transfer Learning from large-scale pre-trained models becomes more prevalent in Natural Language Processing (NLP),
|
||||
operating these large models in on-the-edge and/or under constrained computational training or inference budgets
|
||||
remains challenging. In this work, we propose a method to pre-train a smaller general-purpose language representation
|
||||
model, called DistilBERT, which can then be fine-tuned with good performances on a wide range of tasks like its larger
|
||||
counterparts. While most prior work investigated the use of distillation for building task-specific models, we
|
||||
leverage knowledge distillation during the pre-training phase and show that it is possible to reduce the size of a
|
||||
BERT model by 40%, while retaining 97% of its language understanding capabilities and being 60% faster. To leverage
|
||||
the inductive biases learned by larger models during pre-training, we introduce a triple loss combining language
|
||||
modeling, distillation and cosine-distance losses. Our smaller, faster and lighter model is cheaper to pre-train
|
||||
and we demonstrate its capabilities for on-device computations in a proof-of-concept experiment and a comparative
|
||||
on-device study.*
|
||||
|
||||
Tips:
|
||||
|
||||
- DistilBert doesn't have `token_type_ids`, you don't need to indicate which token belongs to which segment. Just separate your segments with the separation token `tokenizer.sep_token` (or `[SEP]`)
|
||||
- DistilBert doesn't have options to select the input positions (`position_ids` input). This could be added if necessary though, just let's us know if you need this option.
|
||||
|
||||
The original code can be found `here <https://github.com/huggingface/transformers/tree/master/examples/distillation>`_.
|
||||
|
||||
|
||||
DistilBertConfig
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.DistilBertConfig
|
||||
:members:
|
||||
|
||||
|
||||
DistilBertTokenizer
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.DistilBertTokenizer
|
||||
:members:
|
||||
|
||||
|
||||
DistilBertTokenizerFast
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.DistilBertTokenizerFast
|
||||
:members:
|
||||
|
||||
|
||||
DistilBertModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.DistilBertModel
|
||||
:members:
|
||||
|
||||
|
||||
DistilBertForMaskedLM
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.DistilBertForMaskedLM
|
||||
:members:
|
||||
|
||||
|
||||
DistilBertForSequenceClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.DistilBertForSequenceClassification
|
||||
:members:
|
||||
|
||||
|
||||
DistilBertForMultipleChoice
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.DistilBertForMultipleChoice
|
||||
:members:
|
||||
|
||||
|
||||
DistilBertForTokenClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.DistilBertForTokenClassification
|
||||
:members:
|
||||
|
||||
|
||||
DistilBertForQuestionAnswering
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.DistilBertForQuestionAnswering
|
||||
:members:
|
||||
|
||||
TFDistilBertModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFDistilBertModel
|
||||
:members:
|
||||
|
||||
|
||||
TFDistilBertForMaskedLM
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFDistilBertForMaskedLM
|
||||
:members:
|
||||
|
||||
|
||||
TFDistilBertForSequenceClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFDistilBertForSequenceClassification
|
||||
:members:
|
||||
|
||||
|
||||
|
||||
TFDistilBertForMultipleChoice
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFDistilBertForMultipleChoice
|
||||
:members:
|
||||
|
||||
|
||||
|
||||
TFDistilBertForTokenClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFDistilBertForTokenClassification
|
||||
:members:
|
||||
|
||||
|
||||
TFDistilBertForQuestionAnswering
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFDistilBertForQuestionAnswering
|
||||
:members:
|
||||
@@ -1,102 +0,0 @@
|
||||
DPR
|
||||
----------------------------------------------------
|
||||
|
||||
Overview
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
Dense Passage Retrieval (DPR) - is a set of tools and models for state-of-the-art open-domain Q&A research.
|
||||
It is based on the following paper:
|
||||
|
||||
Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih, Dense Passage Retrieval for Open-Domain Question Answering.
|
||||
|
||||
The abstract from the paper is the following:
|
||||
|
||||
*Open-domain question answering relies on efficient passage retrieval to select candidate contexts, where traditional
|
||||
sparse vector space models, such as TF-IDF or BM25, are the de facto method. In this work, we show that retrieval can
|
||||
be practically implemented using dense representations alone, where embeddings are learned from a small number of
|
||||
questions and passages by a simple dual-encoder framework. When evaluated on a wide range of open-domain QA datasets,
|
||||
our dense retriever outperforms a strong Lucene-BM25 system largely by 9%-19% absolute in terms of top-20 passage
|
||||
retrieval accuracy, and helps our end-to-end QA system establish new state-of-the-art on multiple open-domain QA
|
||||
benchmarks.*
|
||||
|
||||
The original code can be found `here <https://github.com/facebookresearch/DPR>`_.
|
||||
|
||||
|
||||
DPRConfig
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.DPRConfig
|
||||
:members:
|
||||
|
||||
|
||||
DPRContextEncoderTokenizer
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.DPRContextEncoderTokenizer
|
||||
:members:
|
||||
|
||||
|
||||
DPRContextEncoderTokenizerFast
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.DPRContextEncoderTokenizerFast
|
||||
:members:
|
||||
|
||||
DPRQuestionEncoderTokenizer
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.DPRQuestionEncoderTokenizer
|
||||
:members:
|
||||
|
||||
|
||||
DPRQuestionEncoderTokenizerFast
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.DPRQuestionEncoderTokenizerFast
|
||||
:members:
|
||||
|
||||
DPRReaderTokenizer
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.DPRReaderTokenizer
|
||||
:members:
|
||||
|
||||
|
||||
DPRReaderTokenizerFast
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.DPRReaderTokenizerFast
|
||||
:members:
|
||||
|
||||
|
||||
DPR specific outputs
|
||||
~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.modeling_dpr.DPRContextEncoderOutput
|
||||
:members:
|
||||
|
||||
.. autoclass:: transformers.modeling_dpr.DPRQuestionEncoderOutput
|
||||
:members:
|
||||
|
||||
.. autoclass:: transformers.modeling_dpr.DPRReaderOutput
|
||||
:members:
|
||||
|
||||
|
||||
DPRContextEncoder
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.DPRContextEncoder
|
||||
:members:
|
||||
|
||||
DPRQuestionEncoder
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.DPRQuestionEncoder
|
||||
:members:
|
||||
|
||||
|
||||
DPRReader
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.DPRReader
|
||||
:members:
|
||||
@@ -1,179 +0,0 @@
|
||||
ELECTRA
|
||||
----------------------------------------------------
|
||||
|
||||
Overview
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
The ELECTRA model was proposed in the paper.
|
||||
`ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators <https://openreview.net/pdf?id=r1xMH1BtvB>`__.
|
||||
ELECTRA is a new pre-training approach which trains two transformer models: the generator and the discriminator. The
|
||||
generator's role is to replace tokens in a sequence, and is therefore trained as a masked language model. The discriminator,
|
||||
which is the model we're interested in, tries to identify which tokens were replaced by the generator in the sequence.
|
||||
|
||||
The abstract from the paper is the following:
|
||||
|
||||
*Masked language modeling (MLM) pre-training methods such as BERT corrupt
|
||||
the input by replacing some tokens with [MASK] and then train a model to
|
||||
reconstruct the original tokens. While they produce good results when transferred
|
||||
to downstream NLP tasks, they generally require large amounts of compute to be
|
||||
effective. As an alternative, we propose a more sample-efficient pre-training task
|
||||
called replaced token detection. Instead of masking the input, our approach
|
||||
corrupts it by replacing some tokens with plausible alternatives sampled from a small
|
||||
generator network. Then, instead of training a model that predicts the original
|
||||
identities of the corrupted tokens, we train a discriminative model that predicts
|
||||
whether each token in the corrupted input was replaced by a generator sample
|
||||
or not. Thorough experiments demonstrate this new pre-training task is more
|
||||
efficient than MLM because the task is defined over all input tokens rather than
|
||||
just the small subset that was masked out. As a result, the contextual representations
|
||||
learned by our approach substantially outperform the ones learned by BERT
|
||||
given the same model size, data, and compute. The gains are particularly strong
|
||||
for small models; for example, we train a model on one GPU for 4 days that
|
||||
outperforms GPT (trained using 30x more compute) on the GLUE natural language
|
||||
understanding benchmark. Our approach also works well at scale, where it
|
||||
performs comparably to RoBERTa and XLNet while using less than 1/4 of their
|
||||
compute and outperforms them when using the same amount of compute.*
|
||||
|
||||
Tips:
|
||||
|
||||
- ELECTRA is the pre-training approach, therefore there is nearly no changes done to the underlying model: BERT. The
|
||||
only change is the separation of the embedding size and the hidden size -> The embedding size is generally smaller,
|
||||
while the hidden size is larger. An additional projection layer (linear) is used to project the embeddings from
|
||||
their embedding size to the hidden size. In the case where the embedding size is the same as the hidden size, no
|
||||
projection layer is used.
|
||||
- The ELECTRA checkpoints saved using `Google Research's implementation <https://github.com/google-research/electra>`__
|
||||
contain both the generator and discriminator. The conversion script requires the user to name which model to export
|
||||
into the correct architecture. Once converted to the HuggingFace format, these checkpoints may be loaded into all
|
||||
available ELECTRA models, however. This means that the discriminator may be loaded in the `ElectraForMaskedLM` model,
|
||||
and the generator may be loaded in the `ElectraForPreTraining` model (the classification head will be randomly
|
||||
initialized as it doesn't exist in the generator).
|
||||
|
||||
The original code can be found `here <https://github.com/google-research/electra>`_.
|
||||
|
||||
|
||||
ElectraConfig
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.ElectraConfig
|
||||
:members:
|
||||
|
||||
|
||||
ElectraTokenizer
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.ElectraTokenizer
|
||||
:members:
|
||||
|
||||
|
||||
ElectraTokenizerFast
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.ElectraTokenizerFast
|
||||
:members:
|
||||
|
||||
|
||||
Electra specific outputs
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.modeling_electra.ElectraForPreTrainingOutput
|
||||
:members:
|
||||
|
||||
.. autoclass:: transformers.modeling_tf_electra.TFElectraForPreTrainingOutput
|
||||
:members:
|
||||
|
||||
|
||||
ElectraModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.ElectraModel
|
||||
:members:
|
||||
|
||||
|
||||
ElectraForPreTraining
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.ElectraForPreTraining
|
||||
:members:
|
||||
|
||||
|
||||
ElectraForMaskedLM
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.ElectraForMaskedLM
|
||||
:members:
|
||||
|
||||
|
||||
ElectraForSequenceClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.ElectraForSequenceClassification
|
||||
:members:
|
||||
|
||||
|
||||
ElectraForMultipleChoice
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.ElectraForMultipleChoice
|
||||
:members:
|
||||
|
||||
|
||||
ElectraForTokenClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.ElectraForTokenClassification
|
||||
:members:
|
||||
|
||||
|
||||
ElectraForQuestionAnswering
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.ElectraForQuestionAnswering
|
||||
:members:
|
||||
|
||||
|
||||
TFElectraModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFElectraModel
|
||||
:members:
|
||||
|
||||
|
||||
TFElectraForPreTraining
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFElectraForPreTraining
|
||||
:members:
|
||||
|
||||
|
||||
TFElectraForMaskedLM
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFElectraForMaskedLM
|
||||
:members:
|
||||
|
||||
|
||||
TFElectraForSequenceClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFElectraForSequenceClassification
|
||||
:members:
|
||||
|
||||
|
||||
TFElectraForMultipleChoice
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFElectraForMultipleChoice
|
||||
:members:
|
||||
|
||||
|
||||
TFElectraForTokenClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFElectraForTokenClassification
|
||||
:members:
|
||||
|
||||
|
||||
TFElectraForQuestionAnswering
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFElectraForQuestionAnswering
|
||||
:members:
|
||||
@@ -1,24 +0,0 @@
|
||||
Encoder Decoder Models
|
||||
------------------------
|
||||
|
||||
The :class:`~transformers.EncoderDecoderModel` can be used to initialize a sequence-to-sequence model with any pre-trained autoencoding model as the encoder and any pre-trained autoregressive model as the decoder.
|
||||
|
||||
The effectiveness of initializing sequence-to-sequence models with pre-trained checkpoints for sequence generation tasks was shown in `Leveraging Pre-trained Checkpoints for Sequence Generation Tasks <https://arxiv.org/abs/1907.12461>`__ by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
|
||||
|
||||
After such an :class:`~transformers.EncoderDecoderModel` has been trained / fine-tuned, it can be saved / loaded just like any other models (see Examples for more information).
|
||||
|
||||
An application of this architecture could be to leverage two pre-trained :obj:`transformers.BertModel` models as the encoder and decoder for a summarization model as was shown in: `Text Summarization with Pretrained Encoders <https://arxiv.org/abs/1910.13461>`_ by Yang Liu and Mirella Lapata.
|
||||
|
||||
|
||||
``EncoderDecoderConfig``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.EncoderDecoderConfig
|
||||
:members:
|
||||
|
||||
|
||||
``EncoderDecoderModel``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.EncoderDecoderModel
|
||||
:members:
|
||||
@@ -1,131 +0,0 @@
|
||||
FlauBERT
|
||||
----------------------------------------------------
|
||||
|
||||
Overview
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
The FlauBERT model was proposed in the paper
|
||||
`FlauBERT: Unsupervised Language Model Pre-training for French <https://arxiv.org/abs/1912.05372>`__ by Hang Le et al.
|
||||
It's a transformer pre-trained using a masked language modeling (MLM) objective (BERT-like).
|
||||
|
||||
The abstract from the paper is the following:
|
||||
|
||||
*Language models have become a key step to achieve state-of-the art results in many different Natural Language
|
||||
Processing (NLP) tasks. Leveraging the huge amount of unlabeled texts nowadays available, they provide an efficient
|
||||
way to pre-train continuous word representations that can be fine-tuned for a downstream task, along with their
|
||||
contextualization at the sentence level. This has been widely demonstrated for English using contextualized
|
||||
representations (Dai and Le, 2015; Peters et al., 2018; Howard and Ruder, 2018; Radford et al., 2018; Devlin et
|
||||
al., 2019; Yang et al., 2019b). In this paper, we introduce and share FlauBERT, a model learned on a very large
|
||||
and heterogeneous French corpus. Models of different sizes are trained using the new CNRS (French National Centre
|
||||
for Scientific Research) Jean Zay supercomputer. We apply our French language models to diverse NLP tasks (text
|
||||
classification, paraphrasing, natural language inference, parsing, word sense disambiguation) and show that most
|
||||
of the time they outperform other pre-training approaches. Different versions of FlauBERT as well as a unified
|
||||
evaluation protocol for the downstream tasks, called FLUE (French Language Understanding Evaluation), are shared
|
||||
to the research community for further reproducible experiments in French NLP.*
|
||||
|
||||
The original code can be found `here <https://github.com/getalp/Flaubert>`_.
|
||||
|
||||
|
||||
FlaubertConfig
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.FlaubertConfig
|
||||
:members:
|
||||
|
||||
|
||||
FlaubertTokenizer
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.FlaubertTokenizer
|
||||
:members:
|
||||
|
||||
|
||||
FlaubertModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.FlaubertModel
|
||||
:members:
|
||||
|
||||
|
||||
FlaubertWithLMHeadModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.FlaubertWithLMHeadModel
|
||||
:members:
|
||||
|
||||
|
||||
FlaubertForSequenceClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.FlaubertForSequenceClassification
|
||||
:members:
|
||||
|
||||
|
||||
FlaubertForMultipleChoice
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.FlaubertForMultipleChoice
|
||||
:members:
|
||||
|
||||
|
||||
FlaubertForTokenClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.FlaubertForTokenClassification
|
||||
:members:
|
||||
|
||||
|
||||
FlaubertForQuestionAnsweringSimple
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.FlaubertForQuestionAnsweringSimple
|
||||
:members:
|
||||
|
||||
|
||||
FlaubertForQuestionAnswering
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.FlaubertForQuestionAnswering
|
||||
:members:
|
||||
|
||||
|
||||
TFFlaubertModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFFlaubertModel
|
||||
:members:
|
||||
|
||||
|
||||
TFFlaubertWithLMHeadModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFFlaubertWithLMHeadModel
|
||||
:members:
|
||||
|
||||
|
||||
TFFlaubertForSequenceClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFFlaubertForSequenceClassification
|
||||
:members:
|
||||
|
||||
|
||||
TFFlaubertForMultipleChoice
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFFlaubertForMultipleChoice
|
||||
:members:
|
||||
|
||||
|
||||
TFFlaubertForTokenClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFFlaubertForTokenClassification
|
||||
:members:
|
||||
|
||||
|
||||
TFFlaubertForQuestionAnsweringSimple
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFFlaubertForQuestionAnsweringSimple
|
||||
:members:
|
||||
@@ -1,185 +0,0 @@
|
||||
Funnel Transformer
|
||||
------------------
|
||||
|
||||
Overview
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
The Funnel Transformer model was proposed in the paper
|
||||
`Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing
|
||||
<https://arxiv.org/abs/2006.03236>`__.
|
||||
It is a bidirectional transformer model, like BERT, but with a pooling operation after each block of layers, a bit
|
||||
like in traditional convolutional neural networks (CNN) in computer vision.
|
||||
|
||||
The abstract from the paper is the following:
|
||||
|
||||
*With the success of language pretraining, it is highly desirable to develop more efficient architectures of good
|
||||
scalability that can exploit the abundant unlabeled data at a lower cost. To improve the efficiency, we examine the
|
||||
much-overlooked redundancy in maintaining a full-length token-level presentation, especially for tasks that only
|
||||
require a single-vector presentation of the sequence. With this intuition, we propose Funnel-Transformer which
|
||||
gradually compresses the sequence of hidden states to a shorter one and hence reduces the computation cost. More
|
||||
importantly, by re-investing the saved FLOPs from length reduction in constructing a deeper or wider model, we further
|
||||
improve the model capacity. In addition, to perform token-level predictions as required by common pretraining
|
||||
objectives, Funnel-Transformer is able to recover a deep representation for each token from the reduced hidden sequence
|
||||
via a decoder. Empirically, with comparable or fewer FLOPs, Funnel-Transformer outperforms the standard Transformer on
|
||||
a wide variety of sequence-level prediction tasks, including text classification, language understanding, and reading
|
||||
comprehension.*
|
||||
|
||||
Tips:
|
||||
|
||||
- Since Funnel Transformer uses pooling, the sequence length of the hidden states changes after each block of layers.
|
||||
The base model therefore has a final sequence length that is a quarter of the original one. This model can be used
|
||||
directly for tasks that just require a sentence summary (like sequence classification or multiple choice). For other
|
||||
tasks, the full model is used; this full model has a decoder that upsamples the final hidden states to the same
|
||||
sequence length as the input.
|
||||
- The Funnel Transformer checkpoints are all available with a full version and a base version. The first ones should
|
||||
be used for :class:`~transformers.FunnelModel`, :class:`~transformers.FunnelForPreTraining`,
|
||||
:class:`~transformers.FunnelForMaskedLM`, :class:`~transformers.FunnelForTokenClassification` and
|
||||
class:`~transformers.FunnelForQuestionAnswering`. The second ones should be used for
|
||||
:class:`~transformers.FunnelBaseModel`, :class:`~transformers.FunnelForSequenceClassification` and
|
||||
:class:`~transformers.FunnelForMultipleChoice`.
|
||||
|
||||
The original code can be found `here <https://github.com/laiguokun/Funnel-Transformer>`_.
|
||||
|
||||
|
||||
FunnelConfig
|
||||
~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.FunnelConfig
|
||||
:members:
|
||||
|
||||
|
||||
FunnelTokenizer
|
||||
~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.FunnelTokenizer
|
||||
:members: build_inputs_with_special_tokens, get_special_tokens_mask,
|
||||
create_token_type_ids_from_sequences, save_vocabulary
|
||||
|
||||
|
||||
FunnelTokenizerFast
|
||||
~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.FunnelTokenizerFast
|
||||
:members:
|
||||
|
||||
|
||||
Funnel specific outputs
|
||||
~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.modeling_funnel.FunnelForPreTrainingOutput
|
||||
:members:
|
||||
|
||||
.. autoclass:: transformers.modeling_tf_funnel.TFFunnelForPreTrainingOutput
|
||||
:members:
|
||||
|
||||
|
||||
FunnelBaseModel
|
||||
~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.FunnelBaseModel
|
||||
:members:
|
||||
|
||||
|
||||
FunnelModel
|
||||
~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.FunnelModel
|
||||
:members:
|
||||
|
||||
|
||||
FunnelModelForPreTraining
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.FunnelForPreTraining
|
||||
:members:
|
||||
|
||||
|
||||
FunnelForMaskedLM
|
||||
~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.FunnelForMaskedLM
|
||||
:members:
|
||||
|
||||
|
||||
FunnelForSequenceClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.FunnelForSequenceClassification
|
||||
:members:
|
||||
|
||||
|
||||
FunnelForMultipleChoice
|
||||
~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.FunnelForMultipleChoice
|
||||
:members:
|
||||
|
||||
|
||||
FunnelForTokenClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.FunnelForTokenClassification
|
||||
:members:
|
||||
|
||||
|
||||
FunnelForQuestionAnswering
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.FunnelForQuestionAnswering
|
||||
:members:
|
||||
|
||||
|
||||
TFFunnelBaseModel
|
||||
~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFFunnelBaseModel
|
||||
:members:
|
||||
|
||||
|
||||
TFFunnelModel
|
||||
~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFFunnelModel
|
||||
:members:
|
||||
|
||||
|
||||
TFFunnelModelForPreTraining
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFFunnelForPreTraining
|
||||
:members:
|
||||
|
||||
|
||||
TFFunnelForMaskedLM
|
||||
~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFFunnelForMaskedLM
|
||||
:members:
|
||||
|
||||
|
||||
TFFunnelForSequenceClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFFunnelForSequenceClassification
|
||||
:members:
|
||||
|
||||
|
||||
TFFunnelForMultipleChoice
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFFunnelForMultipleChoice
|
||||
:members:
|
||||
|
||||
|
||||
TFFunnelForTokenClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFFunnelForTokenClassification
|
||||
:members:
|
||||
|
||||
|
||||
TFFunnelForQuestionAnswering
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFFunnelForQuestionAnswering
|
||||
:members:
|
||||
@@ -1,123 +0,0 @@
|
||||
OpenAI GPT
|
||||
----------------------------------------------------
|
||||
|
||||
Overview
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
OpenAI GPT model was proposed in `Improving Language Understanding by Generative Pre-Training <https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf>`__
|
||||
by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever. It's a causal (unidirectional)
|
||||
transformer pre-trained using language modeling on a large corpus will long range dependencies, the Toronto Book Corpus.
|
||||
|
||||
The abstract from the paper is the following:
|
||||
|
||||
*Natural language understanding comprises a wide range of diverse tasks such
|
||||
as textual entailment, question answering, semantic similarity assessment, and
|
||||
document classification. Although large unlabeled text corpora are abundant,
|
||||
labeled data for learning these specific tasks is scarce, making it challenging for
|
||||
discriminatively trained models to perform adequately. We demonstrate that large
|
||||
gains on these tasks can be realized by generative pre-training of a language model
|
||||
on a diverse corpus of unlabeled text, followed by discriminative fine-tuning on each
|
||||
specific task. In contrast to previous approaches, we make use of task-aware input
|
||||
transformations during fine-tuning to achieve effective transfer while requiring
|
||||
minimal changes to the model architecture. We demonstrate the effectiveness of
|
||||
our approach on a wide range of benchmarks for natural language understanding.
|
||||
Our general task-agnostic model outperforms discriminatively trained models that
|
||||
use architectures specifically crafted for each task, significantly improving upon the
|
||||
state of the art in 9 out of the 12 tasks studied.*
|
||||
|
||||
Tips:
|
||||
|
||||
- GPT is a model with absolute position embeddings so it's usually advised to pad the inputs on
|
||||
the right rather than the left.
|
||||
- GPT was trained with a causal language modeling (CLM) objective and is therefore powerful at predicting the next
|
||||
token in a sequence. Leveraging this feature allows GPT-2 to generate syntactically coherent text as
|
||||
it can be observed in the `run_generation.py` example script.
|
||||
|
||||
`Write With Transformer <https://transformer.huggingface.co/doc/gpt>`__ is a webapp created and hosted by
|
||||
Hugging Face showcasing the generative capabilities of several models. GPT is one of them.
|
||||
|
||||
The original code can be found `here <https://github.com/openai/finetune-transformer-lm>`_.
|
||||
|
||||
Note:
|
||||
|
||||
If you want to reproduce the original tokenization process of the `OpenAI GPT` paper, you will need to install
|
||||
``ftfy`` and ``SpaCy``::
|
||||
|
||||
pip install spacy ftfy==4.4.3
|
||||
python -m spacy download en
|
||||
|
||||
If you don't install ``ftfy`` and ``SpaCy``, the :class:`transformers.OpenAIGPTTokenizer` will default to tokenize using
|
||||
BERT's :obj:`BasicTokenizer` followed by Byte-Pair Encoding (which should be fine for most usage, don't
|
||||
worry).
|
||||
|
||||
OpenAIGPTConfig
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.OpenAIGPTConfig
|
||||
:members:
|
||||
|
||||
|
||||
OpenAIGPTTokenizer
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.OpenAIGPTTokenizer
|
||||
:members: save_vocabulary
|
||||
|
||||
|
||||
OpenAIGPTTokenizerFast
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.OpenAIGPTTokenizerFast
|
||||
:members:
|
||||
|
||||
|
||||
OpenAI specific outputs
|
||||
~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.modeling_openai.OpenAIGPTDoubleHeadsModelOutput
|
||||
:members:
|
||||
|
||||
.. autoclass:: transformers.modeling_tf_openai.TFOpenAIGPTDoubleHeadsModelOutput
|
||||
:members:
|
||||
|
||||
|
||||
OpenAIGPTModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.OpenAIGPTModel
|
||||
:members:
|
||||
|
||||
|
||||
OpenAIGPTLMHeadModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.OpenAIGPTLMHeadModel
|
||||
:members:
|
||||
|
||||
|
||||
OpenAIGPTDoubleHeadsModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.OpenAIGPTDoubleHeadsModel
|
||||
:members:
|
||||
|
||||
|
||||
TFOpenAIGPTModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFOpenAIGPTModel
|
||||
:members:
|
||||
|
||||
|
||||
TFOpenAIGPTLMHeadModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFOpenAIGPTLMHeadModel
|
||||
:members:
|
||||
|
||||
|
||||
TFOpenAIGPTDoubleHeadsModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFOpenAIGPTDoubleHeadsModel
|
||||
:members:
|
||||
@@ -1,110 +0,0 @@
|
||||
OpenAI GPT2
|
||||
----------------------------------------------------
|
||||
|
||||
Overview
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
OpenAI GPT-2 model was proposed in
|
||||
`Language Models are Unsupervised Multitask Learners <https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf>`_
|
||||
by Alec Radford*, Jeffrey Wu*, Rewon Child, David Luan, Dario Amodei** and Ilya Sutskever**.
|
||||
It's a causal (unidirectional) transformer pre-trained using language modeling on a very large
|
||||
corpus of ~40 GB of text data.
|
||||
|
||||
The abstract from the paper is the following:
|
||||
|
||||
*GPT-2 is a large transformer-based language model with 1.5 billion parameters, trained on a dataset[1]
|
||||
of 8 million web pages. GPT-2 is trained with a simple objective: predict the next word, given all of the previous
|
||||
words within some text. The diversity of the dataset causes this simple goal to contain naturally occurring
|
||||
demonstrations of many tasks across diverse domains. GPT-2 is a direct scale-up of GPT, with more than 10X
|
||||
the parameters and trained on more than 10X the amount of data.*
|
||||
|
||||
Tips:
|
||||
|
||||
- GPT-2 is a model with absolute position embeddings so it's usually advised to pad the inputs on
|
||||
the right rather than the left.
|
||||
- GPT-2 was trained with a causal language modeling (CLM) objective and is therefore powerful at predicting the next
|
||||
token in a sequence. Leveraging this feature allows GPT-2 to generate syntactically coherent text as
|
||||
it can be observed in the `run_generation.py` example script.
|
||||
- The PyTorch models can take the `past` as input, which is the previously computed key/value attention pairs. Using
|
||||
this `past` value prevents the model from re-computing pre-computed values in the context of text generation.
|
||||
See `reusing the past in generative models <../quickstart.html#using-the-past>`_ for more information on the usage
|
||||
of this argument.
|
||||
|
||||
`Write With Transformer <https://transformer.huggingface.co/doc/gpt2-large>`__ is a webapp created and hosted by
|
||||
Hugging Face showcasing the generative capabilities of several models. GPT-2 is one of them and is available in five
|
||||
different sizes: small, medium, large, xl and a distilled version of the small checkpoint: distilgpt-2.
|
||||
|
||||
The original code can be found `here <https://openai.com/blog/better-language-models/>`_.
|
||||
|
||||
|
||||
GPT2Config
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.GPT2Config
|
||||
:members:
|
||||
|
||||
|
||||
GPT2Tokenizer
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.GPT2Tokenizer
|
||||
:members: save_vocabulary
|
||||
|
||||
|
||||
GPT2TokenizerFast
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.GPT2TokenizerFast
|
||||
:members:
|
||||
|
||||
|
||||
GPT2 specific outputs
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.modeling_gpt2.GPT2DoubleHeadsModelOutput
|
||||
:members:
|
||||
|
||||
.. autoclass:: transformers.modeling_tf_gpt2.TFGPT2DoubleHeadsModelOutput
|
||||
:members:
|
||||
|
||||
|
||||
GPT2Model
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.GPT2Model
|
||||
:members:
|
||||
|
||||
|
||||
GPT2LMHeadModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.GPT2LMHeadModel
|
||||
:members:
|
||||
|
||||
|
||||
GPT2DoubleHeadsModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.GPT2DoubleHeadsModel
|
||||
:members:
|
||||
|
||||
|
||||
TFGPT2Model
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFGPT2Model
|
||||
:members:
|
||||
|
||||
|
||||
TFGPT2LMHeadModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFGPT2LMHeadModel
|
||||
:members:
|
||||
|
||||
|
||||
TFGPT2DoubleHeadsModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFGPT2DoubleHeadsModel
|
||||
:members:
|
||||
@@ -1,126 +0,0 @@
|
||||
Longformer
|
||||
----------------------------------------------------
|
||||
**DISCLAIMER:** This model is still a work in progress, if you see something strange,
|
||||
file a `Github Issue <https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title>`_
|
||||
|
||||
Overview
|
||||
~~~~~~~~~
|
||||
The Longformer model was presented in `Longformer: The Long-Document Transformer <https://arxiv.org/pdf/2004.05150.pdf>`_ by Iz Beltagy, Matthew E. Peters, Arman Cohan.
|
||||
Here the abstract:
|
||||
|
||||
*Transformer-based models are unable to process long sequences due to their self-attention operation, which scales quadratically with the sequence length. To address this limitation, we introduce the Longformer with an attention mechanism that scales linearly with sequence length, making it easy to process documents of thousands of tokens or longer. Longformer's attention mechanism is a drop-in replacement for the standard self-attention and combines a local windowed attention with a task motivated global attention. Following prior work on long-sequence transformers, we evaluate Longformer on character-level language modeling and achieve state-of-the-art results on text8 and enwik8. In contrast to most prior work, we also pretrain Longformer and finetune it on a variety of downstream tasks. Our pretrained Longformer consistently outperforms RoBERTa on long document tasks and sets new state-of-the-art results on WikiHop and TriviaQA.*
|
||||
|
||||
The Authors' code can be found `here <https://github.com/allenai/longformer>`_ .
|
||||
|
||||
Longformer Self Attention
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
Longformer self attention employs self attention on both a "local" context and a "global" context.
|
||||
Most tokens only attend "locally" to each other meaning that each token attends to its :math:`\frac{1}{2} w` previous tokens and :math:`\frac{1}{2} w` succeding tokens with :math:`w` being the window length as defined in `config.attention_window`. Note that `config.attention_window` can be of type ``list`` to define a different :math:`w` for each layer.
|
||||
A selected few tokens attend "globally" to all other tokens, as it is conventionally done for all tokens in *e.g.* `BertSelfAttention`.
|
||||
|
||||
Note that "locally" and "globally" attending tokens are projected by different query, key and value matrices.
|
||||
Also note that every "locally" attending token not only attends to tokens within its window :math:`w`, but also to all "globally" attending tokens so that global attention is *symmetric*.
|
||||
|
||||
The user can define which tokens attend "locally" and which tokens attend "globally" by setting the tensor `global_attention_mask` at run-time appropriately. `Longformer` employs the following logic for `global_attention_mask`: `0` - the token attends "locally", `1` - token attends "globally". For more information please also refer to :func:`~transformers.LongformerModel.forward` method.
|
||||
|
||||
Using Longformer self attention, the memory and time complexity of the query-key matmul operation, which usually represents the memory and time bottleneck, can be reduced from :math:`\mathcal{O}(n_s \times n_s)` to :math:`\mathcal{O}(n_s \times w)`, with :math:`n_s` being the sequence length and :math:`w` being the average window size. It is assumed that the number of "globally" attending tokens is insignificant as compared to the number of "locally" attending tokens.
|
||||
|
||||
For more information, please refer to the official `paper <https://arxiv.org/pdf/2004.05150.pdf>`_ .
|
||||
|
||||
|
||||
Training
|
||||
~~~~~~~~~~~~~~~~~~~~
|
||||
``LongformerForMaskedLM`` is trained the exact same way, ``RobertaForMaskedLM`` is trained and
|
||||
should be used as follows:
|
||||
|
||||
::
|
||||
|
||||
input_ids = tokenizer.encode('This is a sentence from [MASK] training data', return_tensors='pt')
|
||||
mlm_labels = tokenizer.encode('This is a sentence from the training data', return_tensors='pt')
|
||||
|
||||
loss = model(input_ids, labels=input_ids, masked_lm_labels=mlm_labels)[0]
|
||||
|
||||
|
||||
LongformerConfig
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.LongformerConfig
|
||||
:members:
|
||||
|
||||
|
||||
LongformerTokenizer
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.LongformerTokenizer
|
||||
:members:
|
||||
|
||||
|
||||
LongformerTokenizerFast
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.LongformerTokenizerFast
|
||||
:members:
|
||||
|
||||
|
||||
LongformerModel
|
||||
~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.LongformerModel
|
||||
:members:
|
||||
|
||||
|
||||
LongformerForMaskedLM
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.LongformerForMaskedLM
|
||||
:members:
|
||||
|
||||
|
||||
LongformerForSequenceClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.LongformerForSequenceClassification
|
||||
:members:
|
||||
|
||||
|
||||
LongformerForMultipleChoice
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.LongformerForMultipleChoice
|
||||
:members:
|
||||
|
||||
|
||||
LongformerForTokenClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.LongformerForTokenClassification
|
||||
:members:
|
||||
|
||||
|
||||
LongformerForQuestionAnswering
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.LongformerForQuestionAnswering
|
||||
:members:
|
||||
|
||||
|
||||
TFLongformerModel
|
||||
~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFLongformerModel
|
||||
:members:
|
||||
|
||||
|
||||
TFLongformerForMaskedLM
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFLongformerForMaskedLM
|
||||
:members:
|
||||
|
||||
|
||||
TFLongformerForQuestionAnswering
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFLongformerForQuestionAnswering
|
||||
:members:
|
||||
|
||||
@@ -1,109 +0,0 @@
|
||||
LXMERT
|
||||
----------------------------------------------------
|
||||
|
||||
Overview
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
The LXMERT model was proposed in `LXMERT: Learning Cross-Modality Encoder Representations from Transformers <https://arxiv.org/abs/1908.07490>`__
|
||||
by Hao Tan & Mohit Bansal. It is a series of bidirectional transformer encoders (one for the vision modality, one for the language modality, and then one to fuse both modalities)
|
||||
pre-trained using a combination of masked language modeling, visual-language text alignment, ROI-feature regression, masked visual-attribute modeling, masked visual-object modeling, and visual-question answering objectives.
|
||||
The pretraining consists of multiple multi-modal datasets: MSCOCO, Visual-Genome + Visual-Genome Question Answering, VQA 2.0, and GQA.
|
||||
|
||||
The abstract from the paper is the following:
|
||||
|
||||
*Vision-and-language reasoning requires an understanding of visual concepts, language semantics, and, most importantly, the alignment and relationships between these two
|
||||
modalities. We thus propose the LXMERT
|
||||
(Learning Cross-Modality Encoder Representations from Transformers) framework to learn
|
||||
these vision-and-language connections. In
|
||||
LXMERT, we build a large-scale Transformer
|
||||
model that consists of three encoders: an object relationship encoder, a language encoder,
|
||||
and a cross-modality encoder. Next, to endow our model with the capability of connecting vision and language semantics, we
|
||||
pre-train the model with large amounts of
|
||||
image-and-sentence pairs, via five diverse representative pre-training tasks: masked language modeling, masked object prediction
|
||||
(feature regression and label classification),
|
||||
cross-modality matching, and image question answering. These tasks help in learning both intra-modality and cross-modality relationships. After fine-tuning from our pretrained parameters, our model achieves the
|
||||
state-of-the-art results on two visual question answering datasets (i.e., VQA and GQA).
|
||||
We also show the generalizability of our pretrained cross-modality model by adapting it to
|
||||
a challenging visual-reasoning task, NLVR
|
||||
,
|
||||
and improve the previous best result by 22%
|
||||
absolute (54% to 76%). Lastly, we demonstrate detailed ablation studies to prove that
|
||||
both our novel model components and pretraining strategies significantly contribute to
|
||||
our strong results; and also present several
|
||||
attention visualizations for the different encoders*
|
||||
|
||||
Tips:
|
||||
|
||||
- Bounding boxes are not necessary to be used in the visual feature embeddings, any kind of visual-spacial features will work.
|
||||
- Both the language hidden states and the visual hidden states that LXMERT outputs are passed through the cross-modality layer, so they
|
||||
contain information from both modalities. To access a modality that only attends to itself, select the vision/language hidden states from the first input in the tuple.
|
||||
- The bi-directional cross-modality encoder attention only returns attention values when the language modality is used as the input and the vision modality is used as the context vector. Further,
|
||||
while the cross-modality encoder contains self-attention for each respective modality and cross-attention, only the cross attention is returned and both self attention outputs are disregarded.
|
||||
|
||||
The code can be found `here <https://github.com/airsplay/lxmert>`__
|
||||
|
||||
|
||||
LxmertConfig
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.LxmertConfig
|
||||
:members:
|
||||
|
||||
|
||||
LxmertTokenizer
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.LxmertTokenizer
|
||||
:members: build_inputs_with_special_tokens, get_special_tokens_mask,
|
||||
create_token_type_ids_from_sequences, save_vocabulary
|
||||
|
||||
|
||||
Lxmert specific outputs
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.modeling_lxmert.LxmertModelOutput
|
||||
:members:
|
||||
|
||||
.. autoclass:: transformers.modeling_lxmert.LxmertForPreTrainingOutput
|
||||
:members:
|
||||
|
||||
.. autoclass:: transformers.modeling_lxmert.LxmertForQuestionAnsweringOutput
|
||||
:members:
|
||||
|
||||
.. autoclass:: transformers.modeling_tf_lxmert.TFLxmertModelOutput
|
||||
:members:
|
||||
|
||||
.. autoclass:: transformers.modeling_tf_lxmert.TFLxmertForPreTrainingOutput
|
||||
:members:
|
||||
|
||||
|
||||
LxmertModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.LxmertModel
|
||||
:members:
|
||||
|
||||
LxmertForPreTraining
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.LxmertForPreTraining
|
||||
:members:
|
||||
|
||||
LxmertForQuestionAnswering
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.LxmertForQuestionAnswering
|
||||
:members:
|
||||
|
||||
|
||||
TFLxmertModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFLxmertModel
|
||||
:members:
|
||||
|
||||
TFLxmertForPreTraining
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFLxmertForPreTraining
|
||||
:members:
|
||||
@@ -1,111 +0,0 @@
|
||||
MarianMT
|
||||
----------------------------------------------------
|
||||
**Bugs:** If you see something strange,
|
||||
file a `Github Issue <https://github.com/huggingface/transformers/issues/new?assignees=sshleifer&labels=&template=bug-report.md&title>`__ and assign
|
||||
@sshleifer. Translations should be similar, but not identical to, output in the test set linked to in each model card.
|
||||
|
||||
Implementation Notes
|
||||
~~~~~~~~~~~~~~~~~~~~
|
||||
- Each model is about 298 MB on disk, there are 1,000+ models.
|
||||
- The list of supported language pairs can be found `here <https://huggingface.co/Helsinki-NLP>`__.
|
||||
- models were originally trained by `Jörg Tiedemann <https://researchportal.helsinki.fi/en/persons/j%C3%B6rg-tiedemann>`__ using the `Marian <https://marian-nmt.github.io/>`_ C++ library, which supports fast training and translation.
|
||||
- All models are transformer encoder-decoders with 6 layers in each component. Each model's performance is documented in a model card.
|
||||
- The 80 opus models that require BPE preprocessing are not supported.
|
||||
- The modeling code is the same as ``BartForConditionalGeneration`` with a few minor modifications:
|
||||
- static (sinusoid) positional embeddings (``MarianConfig.static_position_embeddings=True``)
|
||||
- a new final_logits_bias (``MarianConfig.add_bias_logits=True``)
|
||||
- no layernorm_embedding (``MarianConfig.normalize_embedding=False``)
|
||||
- the model starts generating with pad_token_id (which has 0 token_embedding) as the prefix. (Bart uses <s/>)
|
||||
- Code to bulk convert models can be found in ``convert_marian_to_pytorch.py``
|
||||
|
||||
Naming
|
||||
~~~~~~
|
||||
- All model names use the following format: ``Helsinki-NLP/opus-mt-{src}-{tgt}``
|
||||
- The language codes used to name models are inconsistent. Two digit codes can usually be found `here <https://developers.google.com/admin-sdk/directory/v1/languages>`_, three digit codes require googling "language code {code}".
|
||||
- Codes formatted like ``es_AR`` are usually ``code_{region}``. That one is spanish documents from Argentina.
|
||||
|
||||
|
||||
Multilingual Models
|
||||
~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
All model names use the following format: ``Helsinki-NLP/opus-mt-{src}-{tgt}``:
|
||||
- if ``src`` is in all caps, the model supports multiple input languages, you can figure out which ones by looking at the model card, or the Group Members `mapping <https://gist.github.com/sshleifer/6d20e7761931b08e73c3219027b97b8a>`_ .
|
||||
- if ``tgt`` is in all caps, the model can output multiple languages, and you should specify a language code by prepending the desired output language to the src_text
|
||||
- You can see a tokenizer's supported language codes in ``tokenizer.supported_language_codes``
|
||||
|
||||
Example of translating english to many romance languages, using language codes:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
from transformers import MarianMTModel, MarianTokenizer
|
||||
src_text = [
|
||||
'>>fr<< this is a sentence in english that we want to translate to french',
|
||||
'>>pt<< This should go to portuguese',
|
||||
'>>es<< And this to Spanish'
|
||||
]
|
||||
|
||||
model_name = 'Helsinki-NLP/opus-mt-en-ROMANCE'
|
||||
tokenizer = MarianTokenizer.from_pretrained(model_name)
|
||||
print(tokenizer.supported_language_codes)
|
||||
model = MarianMTModel.from_pretrained(model_name)
|
||||
translated = model.generate(**tokenizer.prepare_seq2seq_batch(src_text))
|
||||
tgt_text = [tokenizer.decode(t, skip_special_tokens=True) for t in translated]
|
||||
# ["c'est une phrase en anglais que nous voulons traduire en français",
|
||||
# 'Isto deve ir para o português.',
|
||||
# 'Y esto al español']
|
||||
|
||||
Sometimes, models were trained on collections of languages that do not resolve to a group. In this case, _ is used as a separator for src or tgt, as in ``'Helsinki-NLP/opus-mt-en_el_es_fi-en_el_es_fi'``. These still require language codes.
|
||||
There are many supported regional language codes, like ``>>es_ES<<`` (Spain) and ``>>es_AR<<`` (Argentina), that do not seem to change translations. I have not found these to provide different results than just using ``>>es<<``.
|
||||
|
||||
For Example:
|
||||
- ``Helsinki-NLP/opus-mt-NORTH_EU-NORTH_EU``: translates from all NORTH_EU languages (see `mapping <https://gist.github.com/sshleifer/6d20e7761931b08e73c3219027b97b8a>`_) to all NORTH_EU languages. Use a special language code like ``>>de<<`` to specify output language.
|
||||
- ``Helsinki-NLP/opus-mt-ROMANCE-en``: translates from many romance languages to english, no codes needed since there is only 1 tgt language.
|
||||
|
||||
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
GROUP_MEMBERS = {
|
||||
'ZH': ['cmn', 'cn', 'yue', 'ze_zh', 'zh_cn', 'zh_CN', 'zh_HK', 'zh_tw', 'zh_TW', 'zh_yue', 'zhs', 'zht', 'zh'],
|
||||
'ROMANCE': ['fr', 'fr_BE', 'fr_CA', 'fr_FR', 'wa', 'frp', 'oc', 'ca', 'rm', 'lld', 'fur', 'lij', 'lmo', 'es', 'es_AR', 'es_CL', 'es_CO', 'es_CR', 'es_DO', 'es_EC', 'es_ES', 'es_GT', 'es_HN', 'es_MX', 'es_NI', 'es_PA', 'es_PE', 'es_PR', 'es_SV', 'es_UY', 'es_VE', 'pt', 'pt_br', 'pt_BR', 'pt_PT', 'gl', 'lad', 'an', 'mwl', 'it', 'it_IT', 'co', 'nap', 'scn', 'vec', 'sc', 'ro', 'la'],
|
||||
'NORTH_EU': ['de', 'nl', 'fy', 'af', 'da', 'fo', 'is', 'no', 'nb', 'nn', 'sv'],
|
||||
'SCANDINAVIA': ['da', 'fo', 'is', 'no', 'nb', 'nn', 'sv'],
|
||||
'SAMI': ['se', 'sma', 'smj', 'smn', 'sms'],
|
||||
'NORWAY': ['nb_NO', 'nb', 'nn_NO', 'nn', 'nog', 'no_nb', 'no'],
|
||||
'CELTIC': ['ga', 'cy', 'br', 'gd', 'kw', 'gv']
|
||||
}
|
||||
|
||||
Code to see available pretrained models:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
from transformers.hf_api import HfApi
|
||||
model_list = HfApi().model_list()
|
||||
org = "Helsinki-NLP"
|
||||
model_ids = [x.modelId for x in model_list if x.modelId.startswith(org)]
|
||||
suffix = [x.split('/')[1] for x in model_ids]
|
||||
multi_models = [f'{org}/{s}' for s in suffix if s != s.lower()]
|
||||
|
||||
MarianMTModel
|
||||
~~~~~~~~~~~~~
|
||||
|
||||
Pytorch version of marian-nmt's transformer.h (c++). Designed for the OPUS-NMT translation checkpoints.
|
||||
Model API is identical to BartForConditionalGeneration.
|
||||
Available models are listed at `Model List <https://huggingface.co/models?search=Helsinki-NLP>`__
|
||||
This class inherits nearly all functionality from ``BartForConditionalGeneration``, see that page for method signatures.
|
||||
|
||||
MarianConfig
|
||||
~~~~~~~~~~~~~~~~~~~
|
||||
.. autoclass:: transformers.MarianConfig
|
||||
:members:
|
||||
|
||||
|
||||
MarianTokenizer
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.MarianTokenizer
|
||||
:members: prepare_seq2seq_batch
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -1,76 +0,0 @@
|
||||
MBart
|
||||
----------------------------------------------------
|
||||
**DISCLAIMER:** If you see something strange,
|
||||
file a `Github Issue <https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title>`__ and assign
|
||||
@sshleifer
|
||||
|
||||
Overview
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
The MBart model was presented in `Multilingual Denoising Pre-training for Neural Machine Translation <https://arxiv.org/abs/2001.08210>`_ by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov
|
||||
Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer. According to the abstract,
|
||||
|
||||
MBART is a sequence-to-sequence denoising auto-encoder pre-trained on large-scale monolingual corpora in many languages using the BART objective. mBART is one of the first methods for pre-training a complete sequence-to-sequence model by denoising full texts in multiple languages, while previous approaches have focused only on the encoder, decoder, or reconstructing parts of the text.
|
||||
|
||||
The Authors' code can be found `here <https://github.com/pytorch/fairseq/tree/master/examples/mbart>`__
|
||||
|
||||
|
||||
Training
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
MBart is a multilingual encoder-decoder (seq-to-seq) model primarily intended for translation task.
|
||||
As the model is multilingual it expects the sequences in a different format. A special language id token
|
||||
is added in both the source and target text. The source text format is ``X [eos, src_lang_code]``
|
||||
where ``X`` is the source text. The target text format is ```[tgt_lang_code] X [eos]```. ```bos``` is never used.
|
||||
The ```MBartTokenizer.prepare_seq2seq_batch``` handles this automatically and should be used to encode
|
||||
the sequences for seq-2-seq fine-tuning.
|
||||
|
||||
- Supervised training
|
||||
|
||||
::
|
||||
|
||||
example_english_phrase = "UN Chief Says There Is No Military Solution in Syria"
|
||||
expected_translation_romanian = "Şeful ONU declară că nu există o soluţie militară în Siria"
|
||||
batch = tokenizer.prepare_seq2seq_batch(example_english_phrase, src_lang="en_XX", tgt_lang="ro_RO", tgt_texts=expected_translation_romanian)
|
||||
input_ids = batch["input_ids"]
|
||||
target_ids = batch["decoder_input_ids"]
|
||||
decoder_input_ids = target_ids[:, :-1].contiguous()
|
||||
labels = target_ids[:, 1:].clone()
|
||||
model(input_ids=input_ids, decoder_input_ids=decoder_input_ids, labels=labels) #forward
|
||||
|
||||
- Generation
|
||||
|
||||
While generating the target text set the `decoder_start_token_id` to the target language id.
|
||||
The following example shows how to translate English to Romanian using the ```facebook/mbart-large-en-ro``` model.
|
||||
|
||||
::
|
||||
|
||||
from transformers import MBartForConditionalGeneration, MBartTokenizer
|
||||
model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-en-ro")
|
||||
tokenizer = MBartTokenizer.from_pretrained("facebook/mbart-large-en-ro")
|
||||
article = "UN Chief Says There Is No Military Solution in Syria"
|
||||
batch = tokenizer.prepare_seq2seq_batch(src_texts=[article], src_lang="en_XX")
|
||||
translated_tokens = model.generate(**batch, decoder_start_token_id=tokenizer.lang_code_to_id["ro_RO"])
|
||||
translation = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
|
||||
assert translation == "Şeful ONU declară că nu există o soluţie militară în Siria"
|
||||
|
||||
|
||||
MBartConfig
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.MBartConfig
|
||||
:members:
|
||||
|
||||
|
||||
MBartTokenizer
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.MBartTokenizer
|
||||
:members: build_inputs_with_special_tokens, prepare_seq2seq_batch
|
||||
|
||||
|
||||
MBartForConditionalGeneration
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.MBartForConditionalGeneration
|
||||
:members: generate, forward
|
||||
|
||||
|
||||
@@ -1,179 +0,0 @@
|
||||
MobileBERT
|
||||
----------------------------------------------------
|
||||
|
||||
Overview
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
The MobileBERT model was proposed in `MobileBERT: a Compact Task-Agnostic BERT
|
||||
for Resource-Limited Devices <https://arxiv.org/abs/2004.02984>`__
|
||||
by Zhiqing Sun, Hongkun Yu, Xiaodan Song, Renjie Liu, Yiming Yang, and Denny Zhou. It's a bidirectional transformer
|
||||
based on the BERT model, which is compressed and accelerated using several approaches.
|
||||
|
||||
The abstract from the paper is the following:
|
||||
|
||||
*Natural Language Processing (NLP) has recently achieved great success by using huge pre-trained models with hundreds
|
||||
of millions of parameters. However, these models suffer from heavy model sizes and high latency such that they cannot
|
||||
be deployed to resource-limited mobile devices. In this paper, we propose MobileBERT for compressing and accelerating
|
||||
the popular BERT model. Like the original BERT, MobileBERT is task-agnostic, that is, it can be generically applied
|
||||
to various downstream NLP tasks via simple fine-tuning. Basically, MobileBERT is a thin version of BERT_LARGE, while
|
||||
equipped with bottleneck structures and a carefully designed balance between self-attentions and feed-forward
|
||||
networks. To train MobileBERT, we first train a specially designed teacher model, an inverted-bottleneck incorporated
|
||||
BERT_LARGE model. Then, we conduct knowledge transfer from this teacher to MobileBERT. Empirical studies show that
|
||||
MobileBERT is 4.3x smaller and 5.5x faster than BERT_BASE while achieving competitive results on well-known
|
||||
benchmarks. On the natural language inference tasks of GLUE, MobileBERT achieves a GLUEscore o 77.7
|
||||
(0.6 lower than BERT_BASE), and 62 ms latency on a Pixel 4 phone. On the SQuAD v1.1/v2.0 question answering task,
|
||||
MobileBERT achieves a dev F1 score of 90.0/79.2 (1.5/2.1 higher than BERT_BASE).*
|
||||
|
||||
Tips:
|
||||
|
||||
- MobileBERT is a model with absolute position embeddings so it's usually advised to pad the inputs on
|
||||
the right rather than the left.
|
||||
- MobileBERT is similar to BERT and therefore relies on the masked language modeling (MLM) objective.
|
||||
It is therefore efficient at predicting masked tokens and at NLU in general, but is not optimal for
|
||||
text generation. Models trained with a causal language modeling (CLM) objective are better in that regard.
|
||||
|
||||
The original code can be found `here <https://github.com/google-research/mobilebert>`_.
|
||||
|
||||
MobileBertConfig
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.MobileBertConfig
|
||||
:members:
|
||||
|
||||
|
||||
MobileBertTokenizer
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.MobileBertTokenizer
|
||||
:members: build_inputs_with_special_tokens, get_special_tokens_mask,
|
||||
create_token_type_ids_from_sequences, save_vocabulary
|
||||
|
||||
|
||||
MobileBertTokenizerFast
|
||||
~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.MobileBertTokenizerFast
|
||||
:members:
|
||||
|
||||
|
||||
MobileBert specific outputs
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.modeling_mobilebert.MobileBertForPreTrainingOutput
|
||||
:members:
|
||||
|
||||
.. autoclass:: transformers.modeling_tf_mobilebert.TFMobileBertForPreTrainingOutput
|
||||
:members:
|
||||
|
||||
|
||||
MobileBertModel
|
||||
~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.MobileBertModel
|
||||
:members:
|
||||
|
||||
|
||||
MobileBertForPreTraining
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.MobileBertForPreTraining
|
||||
:members:
|
||||
|
||||
|
||||
MobileBertForMaskedLM
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.MobileBertForMaskedLM
|
||||
:members:
|
||||
|
||||
|
||||
MobileBertForNextSentencePrediction
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.MobileBertForNextSentencePrediction
|
||||
:members:
|
||||
|
||||
|
||||
MobileBertForSequenceClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.MobileBertForSequenceClassification
|
||||
:members:
|
||||
|
||||
|
||||
MobileBertForMultipleChoice
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.MobileBertForMultipleChoice
|
||||
:members:
|
||||
|
||||
|
||||
MobileBertForTokenClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.MobileBertForTokenClassification
|
||||
:members:
|
||||
|
||||
|
||||
MobileBertForQuestionAnswering
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.MobileBertForQuestionAnswering
|
||||
:members:
|
||||
|
||||
|
||||
TFMobileBertModel
|
||||
~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFMobileBertModel
|
||||
:members:
|
||||
|
||||
|
||||
TFMobileBertForPreTraining
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFMobileBertForPreTraining
|
||||
:members:
|
||||
|
||||
|
||||
TFMobileBertForMaskedLM
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFMobileBertForMaskedLM
|
||||
:members:
|
||||
|
||||
|
||||
TFMobileBertForNextSentencePrediction
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFMobileBertForNextSentencePrediction
|
||||
:members:
|
||||
|
||||
|
||||
TFMobileBertForSequenceClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFMobileBertForSequenceClassification
|
||||
:members:
|
||||
|
||||
|
||||
TFMobileBertForMultipleChoice
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFMobileBertForMultipleChoice
|
||||
:members:
|
||||
|
||||
|
||||
TFMobileBertForTokenClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFMobileBertForTokenClassification
|
||||
:members:
|
||||
|
||||
|
||||
TFMobileBertForQuestionAnswering
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFMobileBertForQuestionAnswering
|
||||
:members:
|
||||
|
||||
@@ -1,117 +0,0 @@
|
||||
Pegasus
|
||||
----------------------------------------------------
|
||||
**DISCLAIMER:** If you see something strange,
|
||||
file a `Github Issue <https://github.com/huggingface/transformers/issues/new?assignees=sshleifer&labels=&template=bug-report.md&title>`__ and assign
|
||||
@sshleifer.
|
||||
|
||||
|
||||
Overview
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
The Pegasus model was proposed in `PEGASUS: Pre-training with Extracted Gap-sentences for
|
||||
Abstractive Summarization <https://arxiv.org/pdf/1912.08777.pdf>`_ by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019.
|
||||
According to the abstract,
|
||||
|
||||
- Pegasus' pretraining task is intentionally similar to summarization: important sentences are removed/masked from an input document and are generated together as one output sequence from the remaining sentences, similar to an extractive summary.
|
||||
- Pegasus achieves SOTA summarization performance on all 12 downstream tasks, as measured by ROUGE and human eval.
|
||||
|
||||
The Authors' code can be found `here <https://github.com/google-research/pegasus>`_.
|
||||
|
||||
|
||||
Checkpoints
|
||||
~~~~~~~~~~~
|
||||
All the `checkpoints <https://huggingface.co/models?search=pegasus>`_ are finetuned for summarization, besides ``pegasus-large``, whence the other checkpoints are finetuned.
|
||||
- Each checkpoint is 2.2 GB on disk and 568M parameters.
|
||||
- FP16 is not supported (help/ideas on this appreciated!).
|
||||
- Summarizing xsum in fp32 takes about 400ms/sample, with default parameters on a v100 GPU.
|
||||
- For XSUM, The paper reports rouge1,rouge2, rougeL of paper: 47.21/24.56/39.25. As of Aug 9, this port scores 46.91/24.34/39.1.
|
||||
The gap is likely because of different alpha/length_penalty implementations in beam search.
|
||||
|
||||
|
||||
Implementation Notes
|
||||
~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
- All models are transformer encoder-decoders with 16 layers in each component.
|
||||
- The implementation is completely inherited from ``BartForConditionalGeneration``
|
||||
- Some key configuration differences:
|
||||
- static, sinusoidal position embeddings
|
||||
- no ``layernorm_embedding`` (``PegasusConfig.normalize_embedding=False``)
|
||||
- the model starts generating with pad_token_id (which has 0 token_embedding) as the prefix.
|
||||
- ``num_beams=8``
|
||||
- All pretrained pegasus checkpoints are the same besides three attributes: ``tokenizer.model_max_length`` (max input size), ``max_length`` (max num tokens to generate) and ``length_penalty``
|
||||
- Code to convert checkpoints trained in the author's `repo <https://github.com/google-research/pegasus>`_ can be found in ``convert_pegasus_tf_to_pytorch.py``
|
||||
|
||||
|
||||
Usage Example
|
||||
~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
from transformers import PegasusForConditionalGeneration, PegasusTokenizer
|
||||
import torch
|
||||
src_text = [
|
||||
""" PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow."""
|
||||
]
|
||||
|
||||
model_name = 'google/pegasus-xsum'
|
||||
torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'
|
||||
tokenizer = PegasusTokenizer.from_pretrained(model_name)
|
||||
model = PegasusForConditionalGeneration.from_pretrained(model_name).to(torch_device)
|
||||
batch = tokenizer.prepare_seq2seq_batch(src_text, truncation=True, padding='longest').to(torch_device)
|
||||
translated = model.generate(**batch)
|
||||
tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True)
|
||||
assert tgt_text[0] == "California's largest electricity provider has turned off power to hundreds of thousands of customers."
|
||||
|
||||
PegasusForConditionalGeneration
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
This class inherits all functionality from ``BartForConditionalGeneration``, see that page for method signatures.
|
||||
Available models are listed at `Model List <https://huggingface.co/models?search=pegasus>`__
|
||||
|
||||
.. autoclass:: transformers.PegasusForConditionalGeneration
|
||||
:members:
|
||||
|
||||
|
||||
PegasusConfig
|
||||
~~~~~~~~~~~~~~~~~~~
|
||||
This config fully inherits from ``BartConfig``, but pegasus uses different default values:
|
||||
Up to date parameter values can be seen in `S3 <https://s3.amazonaws.com/models.huggingface.co/bert/google/pegasus-xsum/config.json>`_.
|
||||
As of Aug 10, 2020, they are:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
dict(
|
||||
vocab_size=96103,
|
||||
max_position_embeddings=512,
|
||||
d_model=1024,
|
||||
encoder_ffn_dim=4096,
|
||||
decoder_ffn_dim=4096,
|
||||
encoder_attention_heads=16,
|
||||
decoder_attention_heads=16,
|
||||
encoder_layers=16,
|
||||
decoder_layers=16,
|
||||
dropout=0.1,
|
||||
attention_dropout=0.1,
|
||||
activation_dropout=0.1,
|
||||
pad_token_id=0,
|
||||
eos_token_id=1,
|
||||
is_encoder_decoder=True,
|
||||
normalize_before=True,
|
||||
scale_embedding=True,
|
||||
normalize_embedding=False,
|
||||
add_final_layer_norm=True,
|
||||
static_position_embeddings=True,
|
||||
num_beams=8,
|
||||
activation_function="relu",
|
||||
)
|
||||
|
||||
|
||||
PegasusTokenizer
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
warning: ``add_tokens`` does not work at the moment.
|
||||
|
||||
.. autoclass:: transformers.PegasusTokenizer
|
||||
:members: __call__, prepare_seq2seq_batch
|
||||
|
||||
|
||||
|
||||
@@ -1,135 +0,0 @@
|
||||
Reformer
|
||||
----------------------------------------------------
|
||||
**DISCLAIMER:** This model is still a work in progress, if you see something strange,
|
||||
file a `Github Issue <https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title>`_
|
||||
|
||||
Overview
|
||||
~~~~~~~~~~
|
||||
The Reformer model was presented in `Reformer: The Efficient Transformer <https://arxiv.org/abs/2001.04451.pdf>`_ by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
|
||||
Here the abstract:
|
||||
|
||||
*Large Transformer models routinely achieve state-of-the-art results on a number of tasks but training these models can be prohibitively costly, especially on long sequences. We introduce two techniques to improve the efficiency of Transformers. For one, we replace dot-product attention by one that uses locality-sensitive hashing, changing its complexity from O(L^2) to O(Llog(L)), where L is the length of the sequence. Furthermore, we use reversible residual layers instead of the standard residuals, which allows storing activations only once in the training process instead of N times, where N is the number of layers. The resulting model, the Reformer, performs on par with Transformer models while being much more memory-efficient and much faster on long sequences.*
|
||||
|
||||
The Authors' code can be found `here <https://github.com/google/trax/tree/master/trax/models/reformer>`_ .
|
||||
|
||||
Axial Positional Encodings
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
Axial Positional Encodings were first implemented in Google's `trax library <https://github.com/google/trax/blob/4d99ad4965bab1deba227539758d59f0df0fef48/trax/layers/research/position_encodings.py#L29>`_ and developed by the authors of this model's paper. In models that are treating very long input sequences, the conventional position id encodings store an embedings vector of size :math:`d` being the ``config.hidden_size`` for every position :math:`i, \ldots, n_s`, with :math:`n_s` being ``config.max_embedding_size``. *E.g.*, having a sequence length of :math:`n_s = 2^{19} \approx 0.5M` and a ``config.hidden_size`` of :math:`d = 2^{10} \approx 1000` would result in a position encoding matrix:
|
||||
|
||||
.. math::
|
||||
X_{i,j}, \text{ with } i \in \left[1,\ldots, d\right] \text{ and } j \in \left[1,\ldots, n_s\right]
|
||||
|
||||
which alone has over 500M parameters to store. Axial positional encodings factorize :math:`X_{i,j}` into two matrices:
|
||||
|
||||
.. math::
|
||||
X^{1}_{i,j}, \text{ with } i \in \left[1,\ldots, d^1\right] \text{ and } j \in \left[1,\ldots, n_s^1\right]
|
||||
|
||||
and
|
||||
|
||||
.. math::
|
||||
X^{2}_{i,j}, \text{ with } i \in \left[1,\ldots, d^2\right] \text{ and } j \in \left[1,\ldots, n_s^2\right]
|
||||
|
||||
with:
|
||||
|
||||
.. math::
|
||||
d = d^1 + d^2 \text{ and } n_s = n_s^1 \times n_s^2 .
|
||||
|
||||
Therefore the following holds:
|
||||
|
||||
.. math::
|
||||
X_{i,j} = \begin{cases}
|
||||
X^{1}_{i, k}, & \text{if }\ i < d^1 \text{ with } k = j \mod n_s^1 \\
|
||||
X^{2}_{i - d^1, l}, & \text{if } i \ge d^1 \text{ with } l = \lfloor\frac{j}{n_s^1}\rfloor
|
||||
\end{cases}
|
||||
|
||||
Intuitively, this means that a position embedding vector :math:`x_j \in \mathbb{R}^{d}` is now the composition of two factorized embedding vectors: :math:`x^1_{k, l} + x^2_{l, k}`, where as the ``config.max_embedding_size`` dimension :math:`j` is factorized into :math:`k \text{ and } l`.
|
||||
This design ensures that each position embedding vector :math:`x_j` is unique.
|
||||
|
||||
Using the above example again, axial position encoding with :math:`d^1 = 2^5, d^2 = 2^5, n_s^1 = 2^9, n_s^2 = 2^{10}` can drastically reduced the number of parameters to :math:`2^{14} + 2^{15} \approx 49000` parameters.
|
||||
|
||||
In practice, the parameter ``config.axial_pos_embds_dim`` is set to ``list``:math:`(d^1, d^2)` which sum has to be equal to ``config.hidden_size`` and ``config.axial_pos_shape`` is set to ``list``:math:`(n_s^1, n_s^2)` and which product has to be equal to ``config.max_embedding_size`` which during training has to be equal to the ``sequence length`` of the ``input_ids``.
|
||||
|
||||
|
||||
|
||||
LSH Self Attention
|
||||
~~~~~~~~~~~~~~~~~~~~
|
||||
In Locality sensitive hashing (LSH) self attention the key and query projection weights are tied. Therefore, the key query embedding vectors are also tied.
|
||||
LSH self attention uses the locality sensitive
|
||||
hashing mechanism proposed in `Practical and Optimal LSH for Angular Distance <https://arxiv.org/abs/1509.02897>`_ to assign each of the tied key query embedding vectors to one of ``config.num_buckets`` possible buckets. The premise is that the more "similar" key query embedding vectors (in terms of *cosine similarity*) are to each other, the more likely they are assigned to the same bucket.
|
||||
The accuracy of the LSH mechanism can be improved by increasing ``config.num_hashes`` or directly the argument ``num_hashes`` of the forward function so that the output of the LSH self attention better approximates the output of the "normal" full self attention.
|
||||
The buckets are then sorted and chunked into query key embedding vector chunks each of length ``config.lsh_chunk_length``. For each chunk, the query embedding vectors attend to its key vectors (which are tied to themselves) and to the key embedding vectors of ``config.lsh_num_chunks_before`` previous neighboring chunks and ``config.lsh_num_chunks_after`` following neighboring chunks.
|
||||
For more information, see the `original Paper <https://arxiv.org/abs/2001.04451>`_ or this great `blog post <https://www.pragmatic.ml/reformer-deep-dive/>`_.
|
||||
|
||||
Note that ``config.num_buckets`` can also be factorized into a ``list``:math:`(n_{\text{buckets}}^1, n_{\text{buckets}}^2)`. This way instead of assigning the query key embedding vectors to one of :math:`(1,\ldots, n_{\text{buckets}})` they are assigned to one of :math:`(1-1,\ldots, n_{\text{buckets}}^1-1, \ldots, 1-n_{\text{buckets}}^2, \ldots, n_{\text{buckets}}^1-n_{\text{buckets}}^2)`. This is crucial for very long sequences to save memory.
|
||||
|
||||
When training a model from scratch, it is recommended to leave ``config.num_buckets=None``, so that depending on the sequence length a good value for ``num_buckets`` is calculated on the fly. This value will then automatically be saved in the config and should be reused for inference.
|
||||
|
||||
Using LSH self attention, the memory and time complexity of the query-key matmul operation can be reduced from :math:`\mathcal{O}(n_s \times n_s)` to :math:`\mathcal{O}(n_s \times \log(n_s))`, which usually represents the memory and time bottleneck in a transformer model, with :math:`n_s` being the sequence length.
|
||||
|
||||
|
||||
Local Self Attention
|
||||
~~~~~~~~~~~~~~~~~~~~
|
||||
Local self attention is essentially a "normal" self attention layer with
|
||||
key, query and value projections, but is chunked so that in each chunk of length ``config.local_chunk_length`` the query embedding vectors only attends to the key embedding vectors in its chunk and to the key embedding vectors of ``config.local_num_chunks_before`` previous neighboring chunks and ``config.local_num_chunks_after`` following neighboring chunks.
|
||||
|
||||
Using Local self attention, the memory and time complexity of the query-key matmul operation can be reduced from :math:`\mathcal{O}(n_s \times n_s)` to :math:`\mathcal{O}(n_s \times \log(n_s))`, which usually represents the memory and time bottleneck in a transformer model, with :math:`n_s` being the sequence length.
|
||||
|
||||
|
||||
Training
|
||||
~~~~~~~~~~~~~~~~~~~~
|
||||
During training, we must ensure that the sequence length is set to a value that can be divided by the least common multiple of ``config.lsh_chunk_length`` and ``config.local_chunk_length`` and that the parameters of the Axial Positional Encodings are correctly set as described above. Reformer is very memory efficient so that the model can easily be trained on sequences as long as 64000 tokens.
|
||||
For training, the ``ReformerModelWithLMHead`` should be used as follows:
|
||||
|
||||
::
|
||||
|
||||
input_ids = tokenizer.encode('This is a sentence from the training data', return_tensors='pt')
|
||||
loss = model(input_ids, labels=input_ids)[0]
|
||||
|
||||
|
||||
ReformerConfig
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.ReformerConfig
|
||||
:members:
|
||||
|
||||
|
||||
ReformerTokenizer
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.ReformerTokenizer
|
||||
:members:
|
||||
|
||||
|
||||
ReformerModel
|
||||
~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.ReformerModel
|
||||
:members:
|
||||
|
||||
|
||||
ReformerModelWithLMHead
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.ReformerModelWithLMHead
|
||||
:members:
|
||||
|
||||
|
||||
ReformerForMaskedLM
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.ReformerForMaskedLM
|
||||
:members:
|
||||
|
||||
|
||||
ReformerForSequenceClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.ReformerForSequenceClassification
|
||||
:members:
|
||||
|
||||
|
||||
ReformerForQuestionAnswering
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.ReformerForQuestionAnswering
|
||||
:members:
|
||||
@@ -1,39 +0,0 @@
|
||||
RetriBERT
|
||||
----------------------------------------------------
|
||||
|
||||
Overview
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
The RetriBERT model was proposed in the blog post
|
||||
`Explain Anything Like I'm Five: A Model for Open Domain Long Form Question Answering <https://yjernite.github.io/lfqa.html>`__,
|
||||
RetriBERT is a small model that uses either a single or pair of Bert encoders with lower-dimension projection for dense semantic indexing of text.
|
||||
|
||||
Code to train and use the model can be found `here <https://github.com/huggingface/transformers/tree/master/examples/distillation>`_.
|
||||
|
||||
|
||||
RetriBertConfig
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.RetriBertConfig
|
||||
:members:
|
||||
|
||||
|
||||
RetriBertTokenizer
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.RetriBertTokenizer
|
||||
:members:
|
||||
|
||||
|
||||
RetriBertTokenizerFast
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.RetriBertTokenizerFast
|
||||
:members:
|
||||
|
||||
|
||||
RetriBertModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.RetriBertModel
|
||||
:members:
|
||||
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Reference in New Issue
Block a user