Compare commits
6
Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 LysandreJik
|
f9667c8775 | ||
|
LysandreJik
|
e0f75b6b1b | ||
|
LysandreJik
|
3087c166be | ||
|
LysandreJik
|
559cc1d471 | ||
|
LysandreJik
|
ad4e55c291 | ||
|
LysandreJik
|
782de5184f |
+154
-100
@@ -3,6 +3,27 @@ orbs:
|
||||
gcp-gke: circleci/gcp-gke@1.0.4
|
||||
go: circleci/go@1.3.0
|
||||
|
||||
commands:
|
||||
skip-job-on-doc-only-changes:
|
||||
description: "Do not continue this job and exit with success for PRs with only doc changes"
|
||||
steps:
|
||||
|
||||
- run:
|
||||
name: docs-only changes skip check
|
||||
command: |
|
||||
# pipeline.git.base_revision is not always defined, so only proceed if all external vars are defined
|
||||
if test -n "<< pipeline.git.base_revision >>" && test -n "<< pipeline.git.revision >>" && test -n "$(git diff --name-only << pipeline.git.base_revision >>...<< pipeline.git.revision >>)"
|
||||
then
|
||||
if git diff --name-only << pipeline.git.base_revision >>...<< pipeline.git.revision >> | egrep -qv '\.(md|rst)$'
|
||||
then
|
||||
echo "Non-docs were modified in this PR, proceeding normally"
|
||||
else
|
||||
echo "Only docs were modified in this PR, quitting this job"
|
||||
circleci step halt
|
||||
fi
|
||||
else
|
||||
echo "Can't perform skipping check w/o base_revision defined, continuing the job"
|
||||
fi
|
||||
|
||||
# TPU REFERENCES
|
||||
references:
|
||||
@@ -59,27 +80,38 @@ references:
|
||||
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}')
|
||||
|
||||
setup: &setup
|
||||
working_directory: ~/transformers
|
||||
docker:
|
||||
- image: circleci/python:3.6
|
||||
environment:
|
||||
OMP_NUM_THREADS: 1
|
||||
resource_class: xlarge
|
||||
parallelism: 1
|
||||
|
||||
setup-small: &setup-small
|
||||
working_directory: ~/transformers
|
||||
docker:
|
||||
- image: circleci/python:3.6
|
||||
|
||||
store: &store
|
||||
- store_artifacts:
|
||||
path: ~/transformers/tests_output.txt
|
||||
- store_artifacts:
|
||||
path: ~/transformers/reports
|
||||
|
||||
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
|
||||
<<: *setup
|
||||
steps:
|
||||
- checkout
|
||||
- skip-job-on-doc-only-changes
|
||||
- restore_cache:
|
||||
keys:
|
||||
- v0.4-torch_and_tf-{{ checksum "setup.py" }}
|
||||
- v0.4-{{ checksum "setup.py" }}
|
||||
- run: pip install --upgrade pip
|
||||
- run: pip install .[sklearn,tf-cpu,torch,testing,sentencepiece]
|
||||
- run: pip install tapas torch-scatter -f https://pytorch-geometric.com/whl/torch-1.7.0+cpu.html
|
||||
- save_cache:
|
||||
key: v0.4-{{ checksum "setup.py" }}
|
||||
paths:
|
||||
@@ -91,22 +123,16 @@ jobs:
|
||||
path: ~/transformers/reports
|
||||
|
||||
run_tests_torch:
|
||||
working_directory: ~/transformers
|
||||
docker:
|
||||
- image: circleci/python:3.7
|
||||
environment:
|
||||
OMP_NUM_THREADS: 1
|
||||
resource_class: xlarge
|
||||
parallelism: 1
|
||||
<<: *setup
|
||||
steps:
|
||||
- checkout
|
||||
- skip-job-on-doc-only-changes
|
||||
- restore_cache:
|
||||
keys:
|
||||
- v0.4-torch-{{ checksum "setup.py" }}
|
||||
- v0.4-{{ checksum "setup.py" }}
|
||||
- run: pip install --upgrade pip
|
||||
- run: pip install .[sklearn,torch,testing,sentencepiece]
|
||||
- run: pip install tapas torch-scatter -f https://pytorch-geometric.com/whl/torch-1.7.0+cpu.html
|
||||
- save_cache:
|
||||
key: v0.4-torch-{{ checksum "setup.py" }}
|
||||
paths:
|
||||
@@ -117,16 +143,99 @@ jobs:
|
||||
- store_artifacts:
|
||||
path: ~/transformers/reports
|
||||
|
||||
run_tests_tf:
|
||||
working_directory: ~/transformers
|
||||
docker:
|
||||
- image: circleci/python:3.7
|
||||
environment:
|
||||
OMP_NUM_THREADS: 1
|
||||
resource_class: xlarge
|
||||
parallelism: 1
|
||||
run_tests_torch-1-3:
|
||||
<<: *setup
|
||||
steps:
|
||||
- checkout
|
||||
- skip-job-on-doc-only-changes
|
||||
- restore_cache:
|
||||
keys:
|
||||
- v0.4-torch-1-3-{{ checksum "setup.py" }}
|
||||
- v0.4-{{ checksum "setup.py" }}
|
||||
- run: pip install --upgrade pip
|
||||
- run: pip install .[sklearn,testing,sentencepiece]
|
||||
- run: pip install torch==1.3
|
||||
- save_cache:
|
||||
key: v0.4-torch-1-3-{{ checksum "setup.py" }}
|
||||
paths:
|
||||
- '~/.cache/pip'
|
||||
- run: python -m pytest -n 8 --dist=loadfile -s --make-reports=tests_torch ./tests/ | tee tests_output.txt
|
||||
- store_artifacts:
|
||||
path: ~/transformers/tests_output.txt
|
||||
- store_artifacts:
|
||||
path: ~/transformers/reports
|
||||
|
||||
run_tests_torch-1-4:
|
||||
<<: *setup
|
||||
steps:
|
||||
- checkout
|
||||
- skip-job-on-doc-only-changes
|
||||
- restore_cache:
|
||||
keys:
|
||||
- v0.4-torch-1-4-{{ checksum "setup.py" }}
|
||||
- v0.4-{{ checksum "setup.py" }}
|
||||
- run: pip install --upgrade pip
|
||||
- run: pip install .[sklearn,testing,sentencepiece]
|
||||
- run: pip install torch==1.4
|
||||
- save_cache:
|
||||
key: v0.4-torch-1-4-{{ checksum "setup.py" }}
|
||||
paths:
|
||||
- '~/.cache/pip'
|
||||
- run: python -m pytest -n 8 --dist=loadfile -s --make-reports=tests_torch ./tests/ | tee tests_output.txt
|
||||
- store_artifacts:
|
||||
path: ~/transformers/tests_output.txt
|
||||
- store_artifacts:
|
||||
path: ~/transformers/reports
|
||||
|
||||
run_tests_torch-1-5:
|
||||
<<: *setup
|
||||
steps:
|
||||
- checkout
|
||||
- skip-job-on-doc-only-changes
|
||||
- restore_cache:
|
||||
keys:
|
||||
- v0.4-torch-1-5-{{ checksum "setup.py" }}
|
||||
- v0.4-{{ checksum "setup.py" }}
|
||||
- run: pip install --upgrade pip
|
||||
- run: pip install .[sklearn,testing,sentencepiece]
|
||||
- run: pip install torch==1.5
|
||||
- save_cache:
|
||||
key: v0.4-torch-1-5-{{ checksum "setup.py" }}
|
||||
paths:
|
||||
- '~/.cache/pip'
|
||||
- run: python -m pytest -n 8 --dist=loadfile -s --make-reports=tests_torch ./tests/ | tee tests_output.txt
|
||||
- store_artifacts:
|
||||
path: ~/transformers/tests_output.txt
|
||||
- store_artifacts:
|
||||
path: ~/transformers/reports
|
||||
|
||||
run_tests_torch-1-6:
|
||||
<<: *setup
|
||||
steps:
|
||||
- checkout
|
||||
- skip-job-on-doc-only-changes
|
||||
- restore_cache:
|
||||
keys:
|
||||
- v0.4-torch-1-6-{{ checksum "setup.py" }}
|
||||
- v0.4-{{ checksum "setup.py" }}
|
||||
- run: pip install --upgrade pip
|
||||
- run: pip install .[sklearn,testing,sentencepiece]
|
||||
- run: pip install torch==1.6
|
||||
- save_cache:
|
||||
key: v0.4-torch-1-6-{{ checksum "setup.py" }}
|
||||
paths:
|
||||
- '~/.cache/pip'
|
||||
- run: python -m pytest -n 8 --dist=loadfile -s --make-reports=tests_torch ./tests/ | tee tests_output.txt
|
||||
- store_artifacts:
|
||||
path: ~/transformers/tests_output.txt
|
||||
- store_artifacts:
|
||||
path: ~/transformers/reports
|
||||
|
||||
run_tests_tf:
|
||||
<<: *setup
|
||||
steps:
|
||||
- checkout
|
||||
- skip-job-on-doc-only-changes
|
||||
- restore_cache:
|
||||
keys:
|
||||
- v0.4-tf-{{ checksum "setup.py" }}
|
||||
@@ -144,15 +253,10 @@ jobs:
|
||||
path: ~/transformers/reports
|
||||
|
||||
run_tests_flax:
|
||||
working_directory: ~/transformers
|
||||
docker:
|
||||
- image: circleci/python:3.7
|
||||
environment:
|
||||
OMP_NUM_THREADS: 1
|
||||
resource_class: xlarge
|
||||
parallelism: 1
|
||||
<<: *setup
|
||||
steps:
|
||||
- checkout
|
||||
- skip-job-on-doc-only-changes
|
||||
- restore_cache:
|
||||
keys:
|
||||
- v0.4-flax-{{ checksum "setup.py" }}
|
||||
@@ -170,22 +274,16 @@ jobs:
|
||||
path: ~/transformers/reports
|
||||
|
||||
run_tests_pipelines_torch:
|
||||
working_directory: ~/transformers
|
||||
docker:
|
||||
- image: circleci/python:3.7
|
||||
environment:
|
||||
OMP_NUM_THREADS: 1
|
||||
resource_class: xlarge
|
||||
parallelism: 1
|
||||
<<: *setup
|
||||
steps:
|
||||
- checkout
|
||||
- skip-job-on-doc-only-changes
|
||||
- restore_cache:
|
||||
keys:
|
||||
- v0.4-torch-{{ checksum "setup.py" }}
|
||||
- v0.4-{{ checksum "setup.py" }}
|
||||
- run: pip install --upgrade pip
|
||||
- run: pip install .[sklearn,torch,testing,sentencepiece]
|
||||
- run: pip install tapas torch-scatter -f https://pytorch-geometric.com/whl/torch-1.7.0+cpu.html
|
||||
- save_cache:
|
||||
key: v0.4-torch-{{ checksum "setup.py" }}
|
||||
paths:
|
||||
@@ -197,15 +295,10 @@ jobs:
|
||||
path: ~/transformers/reports
|
||||
|
||||
run_tests_pipelines_tf:
|
||||
working_directory: ~/transformers
|
||||
docker:
|
||||
- image: circleci/python:3.7
|
||||
environment:
|
||||
OMP_NUM_THREADS: 1
|
||||
resource_class: xlarge
|
||||
parallelism: 1
|
||||
<<: *setup
|
||||
steps:
|
||||
- checkout
|
||||
- skip-job-on-doc-only-changes
|
||||
- restore_cache:
|
||||
keys:
|
||||
- v0.4-tf-{{ checksum "setup.py" }}
|
||||
@@ -223,13 +316,10 @@ jobs:
|
||||
path: ~/transformers/reports
|
||||
|
||||
run_tests_custom_tokenizers:
|
||||
working_directory: ~/transformers
|
||||
docker:
|
||||
- image: circleci/python:3.7
|
||||
environment:
|
||||
RUN_CUSTOM_TOKENIZERS: yes
|
||||
<<: *setup
|
||||
steps:
|
||||
- checkout
|
||||
- skip-job-on-doc-only-changes
|
||||
- restore_cache:
|
||||
keys:
|
||||
- v0.4-custom_tokenizers-{{ checksum "setup.py" }}
|
||||
@@ -248,52 +338,29 @@ jobs:
|
||||
path: ~/transformers/reports
|
||||
|
||||
run_examples_torch:
|
||||
working_directory: ~/transformers
|
||||
docker:
|
||||
- image: circleci/python:3.6
|
||||
environment:
|
||||
OMP_NUM_THREADS: 1
|
||||
resource_class: xlarge
|
||||
parallelism: 1
|
||||
<<: *setup
|
||||
steps:
|
||||
- checkout
|
||||
- skip-job-on-doc-only-changes
|
||||
- restore_cache:
|
||||
keys:
|
||||
- v0.4-torch_examples-{{ checksum "setup.py" }}
|
||||
- v0.4-{{ checksum "setup.py" }}
|
||||
- run: pip install --upgrade pip
|
||||
- run: pip install .[sklearn,torch,sentencepiece,testing]
|
||||
- run: pip install -r examples/_tests_requirements.txt
|
||||
- run: pip install -r examples/requirements.txt
|
||||
- save_cache:
|
||||
key: v0.4-torch_examples-{{ checksum "setup.py" }}
|
||||
paths:
|
||||
- '~/.cache/pip'
|
||||
- run: python -m pytest -n 8 --dist=loadfile -s --make-reports=examples_torch ./examples/ | tee examples_output.txt
|
||||
- store_artifacts:
|
||||
path: ~/transformers/examples_output.txt
|
||||
path: ~/transformers/tests_output.txt
|
||||
- store_artifacts:
|
||||
path: ~/transformers/reports
|
||||
|
||||
run_tests_git_lfs:
|
||||
working_directory: ~/transformers
|
||||
docker:
|
||||
- image: circleci/python:3.7
|
||||
resource_class: xlarge
|
||||
parallelism: 1
|
||||
steps:
|
||||
- checkout
|
||||
- run: sudo apt-get install git-lfs
|
||||
- run: |
|
||||
git config --global user.email "ci@dummy.com"
|
||||
git config --global user.name "ci"
|
||||
- run: pip install --upgrade pip
|
||||
- run: pip install .[testing]
|
||||
- run: RUN_GIT_LFS_TESTS=1 python -m pytest -sv ./tests/test_hf_api.py -k "HfLargefilesTest"
|
||||
|
||||
build_doc:
|
||||
working_directory: ~/transformers
|
||||
docker:
|
||||
- image: circleci/python:3.6
|
||||
<<: *setup-small
|
||||
steps:
|
||||
- checkout
|
||||
- restore_cache:
|
||||
@@ -311,9 +378,7 @@ jobs:
|
||||
path: ./docs/_build
|
||||
|
||||
deploy_doc:
|
||||
working_directory: ~/transformers
|
||||
docker:
|
||||
- image: circleci/python:3.6
|
||||
<<: *setup-small
|
||||
steps:
|
||||
- add_ssh_keys:
|
||||
fingerprints:
|
||||
@@ -331,11 +396,7 @@ jobs:
|
||||
- run: ./.circleci/deploy.sh
|
||||
|
||||
check_code_quality:
|
||||
working_directory: ~/transformers
|
||||
docker:
|
||||
- image: circleci/python:3.6
|
||||
resource_class: medium
|
||||
parallelism: 1
|
||||
<<: *setup-small
|
||||
steps:
|
||||
- checkout
|
||||
- restore_cache:
|
||||
@@ -354,16 +415,11 @@ jobs:
|
||||
- run: flake8 examples tests src utils
|
||||
- run: python utils/style_doc.py src/transformers docs/source --max_len 119 --check_only
|
||||
- run: python utils/check_copies.py
|
||||
- run: python utils/check_table.py
|
||||
- run: python utils/check_dummies.py
|
||||
- run: python utils/check_repo.py
|
||||
|
||||
check_repository_consistency:
|
||||
working_directory: ~/transformers
|
||||
docker:
|
||||
- image: circleci/python:3.6
|
||||
resource_class: small
|
||||
parallelism: 1
|
||||
<<: *setup-small
|
||||
steps:
|
||||
- checkout
|
||||
- run: pip install requests
|
||||
@@ -371,12 +427,7 @@ jobs:
|
||||
|
||||
# TPU JOBS
|
||||
run_examples_tpu:
|
||||
docker:
|
||||
- image: circleci/python:3.6
|
||||
environment:
|
||||
OMP_NUM_THREADS: 1
|
||||
resource_class: xlarge
|
||||
parallelism: 1
|
||||
<<: *setup
|
||||
steps:
|
||||
- checkout
|
||||
- go/install
|
||||
@@ -414,11 +465,14 @@ workflows:
|
||||
- run_tests_custom_tokenizers
|
||||
- run_tests_torch_and_tf
|
||||
- run_tests_torch
|
||||
- run_tests_torch-1-3
|
||||
- run_tests_torch-1-4
|
||||
- run_tests_torch-1-5
|
||||
- run_tests_torch-1-6
|
||||
- run_tests_tf
|
||||
- run_tests_flax
|
||||
- run_tests_pipelines_torch
|
||||
- run_tests_pipelines_tf
|
||||
- run_tests_git_lfs
|
||||
- build_doc
|
||||
- deploy_doc: *workflow_filters
|
||||
tpu_testing_jobs:
|
||||
|
||||
@@ -1,65 +0,0 @@
|
||||
name: Model templates runner
|
||||
|
||||
on:
|
||||
push:
|
||||
paths:
|
||||
- "src/**"
|
||||
- "tests/**"
|
||||
- ".github/**"
|
||||
- "templates/**"
|
||||
|
||||
jobs:
|
||||
run_tests_templates:
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- name: Checkout repository
|
||||
uses: actions/checkout@v1
|
||||
|
||||
- name: Install Python
|
||||
uses: actions/setup-python@v1
|
||||
with:
|
||||
python-version: 3.6
|
||||
|
||||
- name: Loading cache.
|
||||
uses: actions/cache@v2
|
||||
id: cache
|
||||
with:
|
||||
path: ~/.cache/pip
|
||||
key: v1.2-tests_templates
|
||||
restore-keys: |
|
||||
v1.2-tests_templates-${{ hashFiles('setup.py') }}
|
||||
v1.2-tests_templates
|
||||
|
||||
- name: Install dependencies
|
||||
run: |
|
||||
pip install --upgrade pip
|
||||
pip install .[dev]
|
||||
- name: Create model files
|
||||
run: |
|
||||
transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/encoder-bert-tokenizer.json --path=templates/adding_a_new_model
|
||||
transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/pt-encoder-bert-tokenizer.json --path=templates/adding_a_new_model
|
||||
transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/standalone.json --path=templates/adding_a_new_model
|
||||
transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/tf-encoder-bert-tokenizer.json --path=templates/adding_a_new_model
|
||||
make style
|
||||
python utils/check_table.py --fix_and_overwrite
|
||||
python utils/check_dummies.py --fix_and_overwrite
|
||||
|
||||
- name: Run all non-slow tests
|
||||
run: |
|
||||
python -m pytest -n 2 --dist=loadfile -s --make-reports=tests_templates tests/*template*
|
||||
|
||||
- name: Run style changes
|
||||
run: |
|
||||
git fetch origin master:master
|
||||
make fixup
|
||||
|
||||
- name: Failure short reports
|
||||
if: ${{ always() }}
|
||||
run: cat reports/tests_templates_failures_short.txt
|
||||
|
||||
- name: Test suite reports artifacts
|
||||
if: ${{ always() }}
|
||||
uses: actions/upload-artifact@v2
|
||||
with:
|
||||
name: run_all_tests_templates_test_reports
|
||||
path: reports
|
||||
@@ -50,7 +50,6 @@ jobs:
|
||||
pip install --upgrade pip
|
||||
pip install .[torch,sklearn,testing,onnxruntime,sentencepiece]
|
||||
pip install git+https://github.com/huggingface/datasets
|
||||
pip install pandas torch-scatter -f https://pytorch-geometric.com/whl/torch-$(python -c "import torch; print(''.join(torch.__version__)")+$(python -c "import torch; print(''.join(torch.version.cuda.split('.')))").html
|
||||
|
||||
- name: Are GPUs recognized by our DL frameworks
|
||||
run: |
|
||||
@@ -188,7 +187,6 @@ jobs:
|
||||
pip install --upgrade pip
|
||||
pip install .[torch,sklearn,testing,onnxruntime,sentencepiece]
|
||||
pip install git+https://github.com/huggingface/datasets
|
||||
pip install pandas torch-scatter -f https://pytorch-geometric.com/whl/torch-$(python -c "import torch; print(''.join(torch.__version__)")+$(python -c "import torch; print(''.join(torch.version.cuda.split('.')))").html
|
||||
|
||||
- name: Are GPUs recognized by our DL frameworks
|
||||
run: |
|
||||
|
||||
@@ -159,6 +159,3 @@ tags
|
||||
|
||||
# pre-commit
|
||||
.pre-commit*
|
||||
|
||||
# .lock
|
||||
*.lock
|
||||
@@ -1,19 +1,3 @@
|
||||
<!---
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License");
|
||||
you may not use this file except in compliance with the License.
|
||||
You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software
|
||||
distributed under the License is distributed on an "AS IS" BASIS,
|
||||
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
See the License for the specific language governing permissions and
|
||||
limitations under the License.
|
||||
-->
|
||||
|
||||
# How to contribute to transformers?
|
||||
|
||||
Everyone is welcome to contribute, and we value everybody's contribution. Code
|
||||
|
||||
@@ -1,4 +1,3 @@
|
||||
Copyright 2018- The Hugging Face team. All rights reserved.
|
||||
|
||||
Apache License
|
||||
Version 2.0, January 2004
|
||||
|
||||
@@ -23,7 +23,6 @@ deps_table_update:
|
||||
|
||||
extra_quality_checks: deps_table_update
|
||||
python utils/check_copies.py
|
||||
python utils/check_table.py
|
||||
python utils/check_dummies.py
|
||||
python utils/check_repo.py
|
||||
python utils/style_doc.py src/transformers docs/source --max_len 119
|
||||
@@ -51,7 +50,6 @@ fixup: modified_only_fixup extra_quality_checks
|
||||
|
||||
fix-copies:
|
||||
python utils/check_copies.py --fix_and_overwrite
|
||||
python utils/check_table.py --fix_and_overwrite
|
||||
python utils/check_dummies.py --fix_and_overwrite
|
||||
|
||||
# Run tests for the library
|
||||
|
||||
@@ -1,19 +1,3 @@
|
||||
<!---
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License");
|
||||
you may not use this file except in compliance with the License.
|
||||
You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software
|
||||
distributed under the License is distributed on an "AS IS" BASIS,
|
||||
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
See the License for the specific language governing permissions and
|
||||
limitations under the License.
|
||||
-->
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
<img src="https://raw.githubusercontent.com/huggingface/transformers/master/docs/source/imgs/transformers_logo_name.png" width="400"/>
|
||||
@@ -47,6 +31,9 @@ limitations under the License.
|
||||
|
||||
🤗 Transformers is backed by the two most popular deep learning libraries, [PyTorch](https://pytorch.org/) and [TensorFlow](https://www.tensorflow.org/), with a seamless integration between them, allowing you to train your models with one then load it for inference with the other.
|
||||
|
||||
### Recent contributors
|
||||
[](https://sourcerer.io/fame/clmnt/huggingface/transformers/links/0)[](https://sourcerer.io/fame/clmnt/huggingface/transformers/links/1)[](https://sourcerer.io/fame/clmnt/huggingface/transformers/links/2)[](https://sourcerer.io/fame/clmnt/huggingface/transformers/links/3)[](https://sourcerer.io/fame/clmnt/huggingface/transformers/links/4)[](https://sourcerer.io/fame/clmnt/huggingface/transformers/links/5)[](https://sourcerer.io/fame/clmnt/huggingface/transformers/links/6)[](https://sourcerer.io/fame/clmnt/huggingface/transformers/links/7)
|
||||
|
||||
## Online demos
|
||||
|
||||
You can test most of our models directly on their pages from the [model hub](https://huggingface.co/models). We also offer an [inference API](https://huggingface.co/pricing) to use those models.
|
||||
@@ -213,7 +200,6 @@ Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
|
||||
1. **[LXMERT](https://huggingface.co/transformers/model_doc/lxmert.html)** (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.
|
||||
1. **[MarianMT](https://huggingface.co/transformers/model_doc/marian.html)** Machine translation models trained using [OPUS](http://opus.nlpl.eu/) data by Jörg Tiedemann. The [Marian Framework](https://marian-nmt.github.io/) is being developed by the Microsoft Translator Team.
|
||||
1. **[MBart](https://huggingface.co/transformers/model_doc/mbart.html)** (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.
|
||||
1. **[MPNet](https://huggingface.co/transformers/model_doc/mpnet.html)** (from Microsoft Research) released with the paper [MPNet: Masked and Permuted Pre-training for Language Understanding](https://arxiv.org/abs/2004.09297) by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu.
|
||||
1. **[MT5](https://huggingface.co/transformers/model_doc/mt5.html)** (from Google AI) released with the paper [mT5: A massively multilingual pre-trained text-to-text transformer](https://arxiv.org/abs/2010.11934) by Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel.
|
||||
1. **[Pegasus](https://huggingface.co/transformers/model_doc/pegasus.html)** (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.
|
||||
1. **[ProphetNet](https://huggingface.co/transformers/model_doc/prophetnet.html)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
|
||||
@@ -222,7 +208,6 @@ Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
|
||||
ultilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers/tree/master/examples/distillation) and a German version of DistilBERT.
|
||||
1. **[SqueezeBert](https://huggingface.co/transformers/model_doc/squeezebert.html)** released with the paper [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer.
|
||||
1. **[T5](https://huggingface.co/transformers/model_doc/t5.html)** (from Google AI) 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 and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
|
||||
1. **[TAPAS](https://huggingface.co/transformers/master/model_doc/tapas.html)** released with the paper [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://arxiv.org/abs/2004.02349) by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos.
|
||||
1. **[Transformer-XL](https://huggingface.co/transformers/model_doc/transformerxl.html)** (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, Ruslan Salakhutdinov.
|
||||
1. **[XLM](https://huggingface.co/transformers/model_doc/xlm.html)** (from Facebook) released together with the paper [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample and Alexis Conneau.
|
||||
1. **[XLM-ProphetNet](https://huggingface.co/transformers/model_doc/xlmprophetnet.html)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
|
||||
|
||||
@@ -1,19 +1,3 @@
|
||||
<!---
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License");
|
||||
you may not use this file except in compliance with the License.
|
||||
You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software
|
||||
distributed under the License is distributed on an "AS IS" BASIS,
|
||||
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
See the License for the specific language governing permissions and
|
||||
limitations under the License.
|
||||
-->
|
||||
|
||||
# Generating the documentation
|
||||
|
||||
To generate the documentation, you first have to build it. Several packages are necessary to build the doc,
|
||||
|
||||
@@ -1,10 +1,10 @@
|
||||
// These two things need to be updated at each release for the version selector.
|
||||
// Last stable version
|
||||
const stableVersion = "v4.0.0"
|
||||
// Dictionary doc folder to label. The last stable version should have an empty key.
|
||||
// Dictionary doc folder to label
|
||||
const versionMapping = {
|
||||
"master": "master",
|
||||
"": "v4.0.0 (stable)",
|
||||
"v4.0.0": "v4.0.0",
|
||||
"v3.5.1": "v3.5.0/v3.5.1",
|
||||
"v3.4.0": "v3.4.0",
|
||||
"v3.3.1": "v3.3.0/v3.3.1",
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
Benchmarks
|
||||
=======================================================================================================================
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
BERTology
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
+1
-1
@@ -20,7 +20,7 @@ sys.path.insert(0, os.path.abspath('../../src'))
|
||||
# -- Project information -----------------------------------------------------
|
||||
|
||||
project = u'transformers'
|
||||
copyright = u'2020, The Hugging Face Team, Licenced under the Apache License, Version 2.0'
|
||||
copyright = u'2020, huggingface'
|
||||
author = u'huggingface'
|
||||
|
||||
# The short X.Y version
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
Converting Tensorflow Checkpoints
|
||||
=======================================================================================================================
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
Fine-tuning with custom datasets
|
||||
=======================================================================================================================
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
Glossary
|
||||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||||
|
||||
|
||||
+13
-36
@@ -22,18 +22,6 @@ State-of-the-art NLP for everyone:
|
||||
- Hands-on practitioners
|
||||
- AI/ML/NLP teachers and educators
|
||||
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
Lower compute costs, smaller carbon footprint:
|
||||
|
||||
- Researchers can share trained models instead of always retraining
|
||||
@@ -151,47 +139,41 @@ and conversion utilities for the following models:
|
||||
22. :doc:`MBart <model_doc/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.
|
||||
23. :doc:`MPNet <model_doc/mpnet>` (from Microsoft Research) released with the paper `MPNet: Masked and Permuted
|
||||
Pre-training for Language Understanding <https://arxiv.org/abs/2004.09297>`__ by Kaitao Song, Xu Tan, Tao Qin,
|
||||
Jianfeng Lu, Tie-Yan Liu.
|
||||
24. :doc:`MT5 <model_doc/mt5>` (from Google AI) released with the paper `mT5: A massively multilingual pre-trained
|
||||
23. :doc:`MT5 <model_doc/mt5>` (from Google AI) released with the paper `mT5: A massively multilingual pre-trained
|
||||
text-to-text transformer <https://arxiv.org/abs/2010.11934>`__ by Linting Xue, Noah Constant, Adam Roberts, Mihir
|
||||
Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel.
|
||||
25. :doc:`Pegasus <model_doc/pegasus>` (from Google) released with the paper `PEGASUS: Pre-training with Extracted
|
||||
24. :doc:`Pegasus <model_doc/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.
|
||||
26. :doc:`ProphetNet <model_doc/prophetnet>` (from Microsoft Research) released with the paper `ProphetNet: Predicting
|
||||
25. :doc:`ProphetNet <model_doc/prophetnet>` (from Microsoft Research) released with the paper `ProphetNet: Predicting
|
||||
Future N-gram for Sequence-to-Sequence Pre-training <https://arxiv.org/abs/2001.04063>`__ by Yu Yan, Weizhen Qi,
|
||||
Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
|
||||
27. :doc:`Reformer <model_doc/reformer>` (from Google Research) released with the paper `Reformer: The Efficient
|
||||
26. :doc:`Reformer <model_doc/reformer>` (from Google Research) released with the paper `Reformer: The Efficient
|
||||
Transformer <https://arxiv.org/abs/2001.04451>`__ by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
|
||||
28. :doc:`RoBERTa <model_doc/roberta>` (from Facebook), released together with the paper a `Robustly Optimized BERT
|
||||
27. :doc:`RoBERTa <model_doc/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, Veselin Stoyanov. ultilingual BERT into `DistilmBERT
|
||||
<https://github.com/huggingface/transformers/tree/master/examples/distillation>`__ and a German version of
|
||||
DistilBERT.
|
||||
29. :doc:`SqueezeBert <model_doc/squeezebert>` released with the paper `SqueezeBERT: What can computer vision teach NLP
|
||||
28. :doc:`SqueezeBert <model_doc/squeezebert>` released with the paper `SqueezeBERT: What can computer vision teach NLP
|
||||
about efficient neural networks? <https://arxiv.org/abs/2006.11316>`__ by Forrest N. Iandola, Albert E. Shaw, Ravi
|
||||
Krishna, and Kurt W. Keutzer.
|
||||
30. :doc:`T5 <model_doc/t5>` (from Google AI) released with the paper `Exploring the Limits of Transfer Learning with a
|
||||
29. :doc:`T5 <model_doc/t5>` (from Google AI) 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 and Noam Shazeer and Adam
|
||||
Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
|
||||
31. `TAPAS <https://huggingface.co/transformers/master/model_doc/tapas.html>`__ released with the paper `TAPAS: Weakly
|
||||
Supervised Table Parsing via Pre-training <https://arxiv.org/abs/2004.02349>`__ by Jonathan Herzig, Paweł Krzysztof
|
||||
Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos.
|
||||
32. :doc:`Transformer-XL <model_doc/transformerxl>` (from Google/CMU) released with the paper `Transformer-XL:
|
||||
30. :doc:`Transformer-XL <model_doc/transformerxl>` (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, Ruslan Salakhutdinov.
|
||||
33. :doc:`XLM <model_doc/xlm>` (from Facebook) released together with the paper `Cross-lingual Language Model
|
||||
31. :doc:`XLM <model_doc/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.
|
||||
34. :doc:`XLM-ProphetNet <model_doc/xlmprophetnet>` (from Microsoft Research) released with the paper `ProphetNet:
|
||||
32. :doc:`XLM-ProphetNet <model_doc/xlmprophetnet>` (from Microsoft Research) released with the paper `ProphetNet:
|
||||
Predicting Future N-gram for Sequence-to-Sequence Pre-training <https://arxiv.org/abs/2001.04063>`__ by Yu Yan,
|
||||
Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
|
||||
35. :doc:`XLM-RoBERTa <model_doc/xlmroberta>` (from Facebook AI), released together with the paper `Unsupervised
|
||||
33. :doc:`XLM-RoBERTa <model_doc/xlmroberta>` (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.
|
||||
36. :doc:`XLNet <model_doc/xlnet>` (from Google/CMU) released with the paper `XLNet: Generalized Autoregressive
|
||||
34. :doc:`XLNet <model_doc/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, Quoc V. Le.
|
||||
|
||||
@@ -246,8 +228,6 @@ TensorFlow and/or Flax.
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| Longformer | ✅ | ✅ | ✅ | ✅ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| MPNet | ✅ | ✅ | ✅ | ✅ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| Marian | ✅ | ❌ | ✅ | ✅ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| MobileBERT | ✅ | ✅ | ✅ | ✅ | ❌ |
|
||||
@@ -272,8 +252,6 @@ TensorFlow and/or Flax.
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| T5 | ✅ | ✅ | ✅ | ✅ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| TAPAS | ✅ | ❌ | ✅ | ❌ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| Transformer-XL | ✅ | ❌ | ✅ | ✅ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| XLM | ✅ | ❌ | ✅ | ✅ | ❌ |
|
||||
@@ -289,6 +267,7 @@ TensorFlow and/or Flax.
|
||||
| mT5 | ✅ | ✅ | ✅ | ✅ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
:caption: Get started
|
||||
@@ -375,7 +354,6 @@ TensorFlow and/or Flax.
|
||||
model_doc/marian
|
||||
model_doc/mbart
|
||||
model_doc/mobilebert
|
||||
model_doc/mpnet
|
||||
model_doc/mt5
|
||||
model_doc/gpt
|
||||
model_doc/gpt2
|
||||
@@ -387,7 +365,6 @@ TensorFlow and/or Flax.
|
||||
model_doc/roberta
|
||||
model_doc/squeezebert
|
||||
model_doc/t5
|
||||
model_doc/tapas
|
||||
model_doc/transformerxl
|
||||
model_doc/xlm
|
||||
model_doc/xlmprophetnet
|
||||
|
||||
@@ -1,19 +1,3 @@
|
||||
<!---
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License");
|
||||
you may not use this file except in compliance with the License.
|
||||
You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software
|
||||
distributed under the License is distributed on an "AS IS" BASIS,
|
||||
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
See the License for the specific language governing permissions and
|
||||
limitations under the License.
|
||||
-->
|
||||
|
||||
# Installation
|
||||
|
||||
🤗 Transformers is tested on Python 3.6+, and PyTorch 1.1.0+ or TensorFlow 2.0+.
|
||||
|
||||
@@ -1,22 +1,9 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
Utilities for Generation
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
This page lists all the utility functions used by :meth:`~transformers.PretrainedModel.generate`,
|
||||
:meth:`~transformers.PretrainedModel.greedy_search`, :meth:`~transformers.PretrainedModel.sample`,
|
||||
:meth:`~transformers.PretrainedModel.beam_search`, :meth:`~transformers.PretrainedModel.beam_sample`, and
|
||||
:meth:`~transformers.PretrainedModel.group_beam_search`.
|
||||
:meth:`~transformers.PretrainedModel.beam_search`, and :meth:`~transformers.PretrainedModel.beam_sample`.
|
||||
|
||||
Most of those are only useful if you are studying the code of the generate methods in the library.
|
||||
|
||||
@@ -32,9 +19,6 @@ generation.
|
||||
.. autoclass:: transformers.LogitsProcessorList
|
||||
:members: __call__
|
||||
|
||||
.. autoclass:: transformers.LogitsWarper
|
||||
:members: __call__
|
||||
|
||||
.. autoclass:: transformers.MinLengthLogitsProcessor
|
||||
:members: __call__
|
||||
|
||||
@@ -56,12 +40,6 @@ generation.
|
||||
.. autoclass:: transformers.NoBadWordsLogitsProcessor
|
||||
:members: __call__
|
||||
|
||||
.. autoclass:: transformers.PrefixConstrainedLogitsProcessor
|
||||
:members: __call__
|
||||
|
||||
.. autoclass:: transformers.HammingDiversityLogitsProcessor
|
||||
:members: __call__
|
||||
|
||||
BeamSearch
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
@@ -70,10 +48,3 @@ BeamSearch
|
||||
|
||||
.. autoclass:: transformers.BeamSearchScorer
|
||||
:members: process, finalize
|
||||
|
||||
Utilities
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autofunction:: transformers.top_k_top_p_filtering
|
||||
|
||||
.. autofunction:: transformers.tf_top_k_top_p_filtering
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
Custom Layers and Utilities
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
Utilities for pipelines
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
Utilities for Tokenizers
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
Utilities for Trainer
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
@@ -22,8 +10,6 @@ Utilities
|
||||
|
||||
.. autoclass:: transformers.EvalPrediction
|
||||
|
||||
.. autoclass:: transformers.EvaluationStrategy
|
||||
|
||||
.. autofunction:: transformers.set_seed
|
||||
|
||||
.. autofunction:: transformers.torch_distributed_zero_first
|
||||
@@ -34,15 +20,8 @@ Callbacks internals
|
||||
|
||||
.. autoclass:: transformers.trainer_callback.CallbackHandler
|
||||
|
||||
|
||||
Distributed Evaluation
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.trainer_pt_utils.DistributedTensorGatherer
|
||||
:members:
|
||||
|
||||
|
||||
Distributed Evaluation
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.HfArgumentParser
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
Callbacks
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
Configuration
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
Logging
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
Models
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
Optimization
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
@@ -74,10 +62,6 @@ Learning Rate Schedules (Pytorch)
|
||||
:target: /imgs/warmup_linear_schedule.png
|
||||
:alt:
|
||||
|
||||
|
||||
.. autofunction:: transformers.get_polynomial_decay_schedule_with_warmup
|
||||
|
||||
|
||||
Warmup (TensorFlow)
|
||||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
Model outputs
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
Pipelines
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
@@ -73,9 +61,8 @@ FillMaskPipeline
|
||||
NerPipeline
|
||||
=======================================================================================================================
|
||||
|
||||
.. autoclass:: transformers.NerPipeline
|
||||
|
||||
See :class:`~transformers.TokenClassificationPipeline` for all details.
|
||||
This class is an alias of the :class:`~transformers.TokenClassificationPipeline` defined below. Please refer to that
|
||||
pipeline for documentation and usage examples.
|
||||
|
||||
QuestionAnsweringPipeline
|
||||
=======================================================================================================================
|
||||
@@ -119,13 +106,6 @@ TokenClassificationPipeline
|
||||
:special-members: __call__
|
||||
:members:
|
||||
|
||||
TranslationPipeline
|
||||
=======================================================================================================================
|
||||
|
||||
.. autoclass:: transformers.TranslationPipeline
|
||||
:special-members: __call__
|
||||
:members:
|
||||
|
||||
ZeroShotClassificationPipeline
|
||||
=======================================================================================================================
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
Processors
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
Tokenizer
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
Trainer
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
@@ -1,19 +1,3 @@
|
||||
<!---
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License");
|
||||
you may not use this file except in compliance with the License.
|
||||
You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software
|
||||
distributed under the License is distributed on an "AS IS" BASIS,
|
||||
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
See the License for the specific language governing permissions and
|
||||
limitations under the License.
|
||||
-->
|
||||
|
||||
# Migrating from previous packages
|
||||
|
||||
## Migrating from transformers `v3.x` to `v4.x`
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
ALBERT
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
@@ -60,13 +48,6 @@ AlbertTokenizer
|
||||
create_token_type_ids_from_sequences, save_vocabulary
|
||||
|
||||
|
||||
AlbertTokenizerFast
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.AlbertTokenizerFast
|
||||
:members:
|
||||
|
||||
|
||||
Albert specific outputs
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
Auto Classes
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
@@ -175,10 +163,3 @@ TFAutoModelForQuestionAnswering
|
||||
|
||||
.. autoclass:: transformers.TFAutoModelForQuestionAnswering
|
||||
:members:
|
||||
|
||||
|
||||
FlaxAutoModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.FlaxAutoModel
|
||||
:members:
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
BART
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
@@ -98,12 +86,6 @@ BartTokenizer
|
||||
:members:
|
||||
|
||||
|
||||
BartTokenizerFast
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.BartTokenizerFast
|
||||
:members:
|
||||
|
||||
|
||||
BartModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
@@ -111,6 +93,8 @@ BartModel
|
||||
.. autoclass:: transformers.BartModel
|
||||
:members: forward
|
||||
|
||||
.. autofunction:: transformers.models.bart.modeling_bart._prepare_bart_decoder_inputs
|
||||
|
||||
|
||||
BartForConditionalGeneration
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
BARThez
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
@@ -51,9 +39,3 @@ BarthezTokenizer
|
||||
.. autoclass:: transformers.BarthezTokenizer
|
||||
:members:
|
||||
|
||||
|
||||
BarthezTokenizerFast
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.BarthezTokenizerFast
|
||||
:members:
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
BERT
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
@@ -207,10 +195,3 @@ FlaxBertModel
|
||||
|
||||
.. autoclass:: transformers.FlaxBertModel
|
||||
:members: __call__
|
||||
|
||||
|
||||
FlaxBertForMaskedLM
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.FlaxBertForMaskedLM
|
||||
:members: __call__
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
BertGeneration
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
Blenderbot
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
CamemBERT
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
@@ -54,13 +42,6 @@ CamembertTokenizer
|
||||
create_token_type_ids_from_sequences, save_vocabulary
|
||||
|
||||
|
||||
CamembertTokenizerFast
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.CamembertTokenizerFast
|
||||
:members:
|
||||
|
||||
|
||||
CamembertModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
CTRL
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
DeBERTa
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
DialoGPT
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
DistilBERT
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
DPR
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
ELECTRA
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
Encoder Decoder Models
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
FlauBERT
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
FSMT
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
Funnel Transformer
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
OpenAI GPT
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
OpenAI GPT2
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
@@ -126,15 +114,3 @@ TFGPT2DoubleHeadsModel
|
||||
|
||||
.. autoclass:: transformers.TFGPT2DoubleHeadsModel
|
||||
:members: call
|
||||
|
||||
TFGPT2ForSequenceClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFGPT2ForSequenceClassification
|
||||
:members: call
|
||||
|
||||
TFSequenceClassifierOutputWithPast
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.modeling_tf_outputs.TFSequenceClassifierOutputWithPast
|
||||
:members:
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
LayoutLM
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
@@ -57,13 +45,6 @@ LayoutLMTokenizer
|
||||
:members:
|
||||
|
||||
|
||||
LayoutLMTokenizerFast
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.LayoutLMTokenizerFast
|
||||
:members:
|
||||
|
||||
|
||||
LayoutLMModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
Longformer
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
LXMERT
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
MarianMT
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
MBart
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
@@ -90,13 +78,6 @@ MBartTokenizer
|
||||
:members: build_inputs_with_special_tokens, prepare_seq2seq_batch
|
||||
|
||||
|
||||
MBartTokenizerFast
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.MBartTokenizerFast
|
||||
:members:
|
||||
|
||||
|
||||
MBartForConditionalGeneration
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
MobileBERT
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
@@ -1,149 +0,0 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
MPNet
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
Overview
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
The MPNet model was proposed in `MPNet: Masked and Permuted Pre-training for Language Understanding
|
||||
<https://arxiv.org/abs/2004.09297>`__ by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu.
|
||||
|
||||
MPNet adopts a novel pre-training method, named masked and permuted language modeling, to inherit the advantages of
|
||||
masked language modeling and permuted language modeling for natural language understanding.
|
||||
|
||||
The abstract from the paper is the following:
|
||||
|
||||
*BERT adopts masked language modeling (MLM) for pre-training and is one of the most successful pre-training models.
|
||||
Since BERT neglects dependency among predicted tokens, XLNet introduces permuted language modeling (PLM) for
|
||||
pre-training to address this problem. However, XLNet does not leverage the full position information of a sentence and
|
||||
thus suffers from position discrepancy between pre-training and fine-tuning. In this paper, we propose MPNet, a novel
|
||||
pre-training method that inherits the advantages of BERT and XLNet and avoids their limitations. MPNet leverages the
|
||||
dependency among predicted tokens through permuted language modeling (vs. MLM in BERT), and takes auxiliary position
|
||||
information as input to make the model see a full sentence and thus reducing the position discrepancy (vs. PLM in
|
||||
XLNet). We pre-train MPNet on a large-scale dataset (over 160GB text corpora) and fine-tune on a variety of
|
||||
down-streaming tasks (GLUE, SQuAD, etc). Experimental results show that MPNet outperforms MLM and PLM by a large
|
||||
margin, and achieves better results on these tasks compared with previous state-of-the-art pre-trained methods (e.g.,
|
||||
BERT, XLNet, RoBERTa) under the same model setting.*
|
||||
|
||||
Tips:
|
||||
|
||||
- MPNet doesn't have :obj:`token_type_ids`, you don't need to indicate which token belongs to which segment. just
|
||||
separate your segments with the separation token :obj:`tokenizer.sep_token` (or :obj:`[sep]`).
|
||||
|
||||
The original code can be found `here <https://github.com/microsoft/MPNet>`__.
|
||||
|
||||
MPNetConfig
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.MPNetConfig
|
||||
:members:
|
||||
|
||||
|
||||
MPNetTokenizer
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.MPNetTokenizer
|
||||
:members: build_inputs_with_special_tokens, get_special_tokens_mask,
|
||||
create_token_type_ids_from_sequences, save_vocabulary
|
||||
|
||||
|
||||
MPNetTokenizerFast
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.MPNetTokenizerFast
|
||||
:members:
|
||||
|
||||
|
||||
MPNetModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.MPNetModel
|
||||
:members: forward
|
||||
|
||||
|
||||
MPNetForMaskedLM
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.MPNetForMaskedLM
|
||||
:members: forward
|
||||
|
||||
|
||||
MPNetForSequenceClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.MPNetForSequenceClassification
|
||||
:members: forward
|
||||
|
||||
|
||||
MPNetForMultipleChoice
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.MPNetForMultipleChoice
|
||||
:members: forward
|
||||
|
||||
|
||||
MPNetForTokenClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.MPNetForTokenClassification
|
||||
:members: forward
|
||||
|
||||
|
||||
MPNetForQuestionAnswering
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.MPNetForQuestionAnswering
|
||||
:members: forward
|
||||
|
||||
|
||||
TFMPNetModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFMPNetModel
|
||||
:members: call
|
||||
|
||||
|
||||
TFMPNetForMaskedLM
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFMPNetForMaskedLM
|
||||
:members: call
|
||||
|
||||
|
||||
TFMPNetForSequenceClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFMPNetForSequenceClassification
|
||||
:members: call
|
||||
|
||||
|
||||
TFMPNetForMultipleChoice
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFMPNetForMultipleChoice
|
||||
:members: call
|
||||
|
||||
|
||||
TFMPNetForTokenClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFMPNetForTokenClassification
|
||||
:members: call
|
||||
|
||||
|
||||
TFMPNetForQuestionAnswering
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFMPNetForQuestionAnswering
|
||||
:members: call
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
MT5
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
@@ -37,22 +25,6 @@ MT5Config
|
||||
:members:
|
||||
|
||||
|
||||
MT5Tokenizer
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.MT5Tokenizer
|
||||
|
||||
See :class:`~transformers.T5Tokenizer` for all details.
|
||||
|
||||
|
||||
MT5TokenizerFast
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.MT5TokenizerFast
|
||||
|
||||
See :class:`~transformers.T5TokenizerFast` for all details.
|
||||
|
||||
|
||||
MT5Model
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
Pegasus
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
@@ -112,13 +100,6 @@ warning: ``add_tokens`` does not work at the moment.
|
||||
:members: __call__, prepare_seq2seq_batch
|
||||
|
||||
|
||||
PegasusTokenizerFast
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.PegasusTokenizerFast
|
||||
:members:
|
||||
|
||||
|
||||
PegasusForConditionalGeneration
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
ProphetNet
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
RAG
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
Reformer
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
@@ -163,13 +151,6 @@ ReformerTokenizer
|
||||
:members: save_vocabulary
|
||||
|
||||
|
||||
ReformerTokenizerFast
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.ReformerTokenizerFast
|
||||
:members:
|
||||
|
||||
|
||||
ReformerModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
RetriBERT
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
RoBERTa
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
SqueezeBERT
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
T5
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
@@ -107,13 +95,6 @@ T5Tokenizer
|
||||
create_token_type_ids_from_sequences, prepare_seq2seq_batch, save_vocabulary
|
||||
|
||||
|
||||
T5TokenizerFast
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.T5TokenizerFast
|
||||
:members:
|
||||
|
||||
|
||||
T5Model
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
|
||||
@@ -1,444 +0,0 @@
|
||||
TAPAS
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
Overview
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
The TAPAS model was proposed in `TAPAS: Weakly Supervised Table Parsing via Pre-training
|
||||
<https://www.aclweb.org/anthology/2020.acl-main.398>`__ by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller,
|
||||
Francesco Piccinno and Julian Martin Eisenschlos. It's a BERT-based model specifically designed (and pre-trained) for
|
||||
answering questions about tabular data. Compared to BERT, TAPAS uses relative position embeddings and has 7 token types
|
||||
that encode tabular structure. TAPAS is pre-trained on the masked language modeling (MLM) objective on a large dataset
|
||||
comprising millions of tables from English Wikipedia and corresponding texts. For question answering, TAPAS has 2 heads
|
||||
on top: a cell selection head and an aggregation head, for (optionally) performing aggregations (such as counting or
|
||||
summing) among selected cells. TAPAS has been fine-tuned on several datasets: `SQA
|
||||
<https://www.microsoft.com/en-us/download/details.aspx?id=54253>`_ (Sequential Question Answering by Microsoft), `WTQ
|
||||
<https://github.com/ppasupat/WikiTableQuestions>`__ (Wiki Table Questions by Stanford University) and `WikiSQL
|
||||
<https://github.com/salesforce/WikiSQL>`__ (by Salesforce). It achieves state-of-the-art on both SQA and WTQ, while
|
||||
having comparable performance to SOTA on WikiSQL, with a much simpler architecture.
|
||||
|
||||
The abstract from the paper is the following:
|
||||
|
||||
*Answering natural language questions over tables is usually seen as a semantic parsing task. To alleviate the
|
||||
collection cost of full logical forms, one popular approach focuses on weak supervision consisting of denotations
|
||||
instead of logical forms. However, training semantic parsers from weak supervision poses difficulties, and in addition,
|
||||
the generated logical forms are only used as an intermediate step prior to retrieving the denotation. In this paper, we
|
||||
present TAPAS, an approach to question answering over tables without generating logical forms. TAPAS trains from weak
|
||||
supervision, and predicts the denotation by selecting table cells and optionally applying a corresponding aggregation
|
||||
operator to such selection. TAPAS extends BERT's architecture to encode tables as input, initializes from an effective
|
||||
joint pre-training of text segments and tables crawled from Wikipedia, and is trained end-to-end. We experiment with
|
||||
three different semantic parsing datasets, and find that TAPAS outperforms or rivals semantic parsing models by
|
||||
improving state-of-the-art accuracy on SQA from 55.1 to 67.2 and performing on par with the state-of-the-art on WIKISQL
|
||||
and WIKITQ, but with a simpler model architecture. We additionally find that transfer learning, which is trivial in our
|
||||
setting, from WIKISQL to WIKITQ, yields 48.7 accuracy, 4.2 points above the state-of-the-art.*
|
||||
|
||||
In addition, the authors have further pre-trained TAPAS to recognize **table entailment**, by creating a balanced
|
||||
dataset of millions of automatically created training examples which are learned in an intermediate step prior to
|
||||
fine-tuning. The authors of TAPAS call this further pre-training intermediate pre-training (since TAPAS is first
|
||||
pre-trained on MLM, and then on another dataset). They found that intermediate pre-training further improves
|
||||
performance on SQA, achieving a new state-of-the-art as well as state-of-the-art on `TabFact
|
||||
<https://github.com/wenhuchen/Table-Fact-Checking>`__, a large-scale dataset with 16k Wikipedia tables for table
|
||||
entailment (a binary classification task). For more details, see their follow-up paper: `Understanding tables with
|
||||
intermediate pre-training <https://www.aclweb.org/anthology/2020.findings-emnlp.27/>`__ by Julian Martin Eisenschlos,
|
||||
Syrine Krichene and Thomas Müller.
|
||||
|
||||
The original code can be found `here <https://github.com/google-research/tapas>`__.
|
||||
|
||||
Tips:
|
||||
|
||||
- TAPAS is a model that uses relative position embeddings by default (restarting the position embeddings at every cell
|
||||
of the table). Note that this is something that was added after the publication of the original TAPAS paper.
|
||||
According to the authors, this usually results in a slightly better performance, and allows you to encode longer
|
||||
sequences without running out of embeddings. This is reflected in the ``reset_position_index_per_cell`` parameter of
|
||||
:class:`~transformers.TapasConfig`, which is set to ``True`` by default. The latest versions of the models available
|
||||
in the `model hub <https://huggingface.co/models?search=tapas>`_ all use relative position embeddings. You can still
|
||||
use the ones with absolute position embeddings by passing in a certain version when calling the
|
||||
``.from_pretrained()`` method as explained in the model cards. Note that it's usually advised to pad the inputs on
|
||||
the right rather than the left.
|
||||
- TAPAS is based on BERT, so ``TAPAS-base`` for example corresponds to a ``BERT-base`` architecture. Of course,
|
||||
TAPAS-large will result in the best performance (the results reported in the paper are from TAPAS-large). Results of
|
||||
the various sized models are shown on the `original Github repository <https://github.com/google-research/tapas>`_.
|
||||
- TAPAS has checkpoints fine-tuned on SQA, which are capable of answering questions related to a table in a
|
||||
conversational set-up. This means that you can ask follow-up questions such as "what is his age?" related to the
|
||||
previous question. Note that the forward pass of TAPAS is a bit different in case of a conversational set-up: in that
|
||||
case, you have to feed every table-question pair one by one to the model, such that the `prev_labels` token type ids
|
||||
can be overwritten by the predicted `labels` of the model to the previous question. See "Usage" section for more
|
||||
info.
|
||||
- TAPAS 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.
|
||||
|
||||
|
||||
Usage: fine-tuning
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
Here we explain how you can fine-tune :class:`~transformers.TapasForQuestionAnswering` on your own dataset.
|
||||
|
||||
**STEP 1: Choose one of the 3 ways in which you can use TAPAS - or experiment**
|
||||
|
||||
Basically, there are 3 different ways in which one can fine-tune :class:`~transformers.TapasForQuestionAnswering`,
|
||||
corresponding to the different datasets on which Tapas was fine-tuned:
|
||||
|
||||
1. SQA: if you're interested in asking follow-up questions related to a table, in a conversational set-up. For example
|
||||
if you first ask "what's the name of the first actor?" then you can ask a follow-up question such as "how old is
|
||||
he?". Here, questions do not involve any aggregation (all questions are cell selection questions).
|
||||
2. WTQ: if you're not interested in asking questions in a conversational set-up, but rather just asking questions
|
||||
related to a table, which might involve aggregation, such as counting a number of rows, summing up cell values or
|
||||
averaging cell values. You can then for example ask "what's the total number of goals Cristiano Ronaldo made in his
|
||||
career?". This case is also called **weak supervision**, since the model itself must learn the appropriate
|
||||
aggregation operator (SUM/COUNT/AVERAGE/NONE) given only the answer to the question as supervision.
|
||||
3. WikiSQL-supervised: this dataset is based on WikiSQL with the model being given the ground truth aggregation
|
||||
operator during training. This is also called **strong supervision**. Here, learning the appropriate aggregation
|
||||
operator is much easier.
|
||||
|
||||
To summarize:
|
||||
|
||||
+------------------------------------+----------------------+-------------------------------------------------------------------------------------------------------------------+
|
||||
| **Task** | **Example dataset** | **Description** |
|
||||
+------------------------------------+----------------------+-------------------------------------------------------------------------------------------------------------------+
|
||||
| Conversational | SQA | Conversational, only cell selection questions |
|
||||
+------------------------------------+----------------------+-------------------------------------------------------------------------------------------------------------------+
|
||||
| Weak supervision for aggregation | WTQ | Questions might involve aggregation, and the model must learn this given only the answer as supervision |
|
||||
+------------------------------------+----------------------+-------------------------------------------------------------------------------------------------------------------+
|
||||
| Strong supervision for aggregation | WikiSQL-supervised | Questions might involve aggregation, and the model must learn this given the gold aggregation operator |
|
||||
+------------------------------------+----------------------+-------------------------------------------------------------------------------------------------------------------+
|
||||
|
||||
Initializing a model with a pre-trained base and randomly initialized classification heads from the model hub can be
|
||||
done as follows (be sure to have installed the `torch-scatter dependency <https://github.com/rusty1s/pytorch_scatter>`_
|
||||
for your environment):
|
||||
|
||||
.. code-block::
|
||||
|
||||
>>> from transformers import TapasConfig, TapasForQuestionAnswering
|
||||
|
||||
>>> # for example, the base sized model with default SQA configuration
|
||||
>>> model = TapasForQuestionAnswering.from_pretrained('google/tapas-base')
|
||||
|
||||
>>> # or, the base sized model with WTQ configuration
|
||||
>>> config = TapasConfig(
|
||||
... num_aggregation_labels = 4,
|
||||
... use_answer_as_supervision = True,
|
||||
... answer_loss_cutoff = 0.664694,
|
||||
... cell_selection_preference = 0.207951,
|
||||
... huber_loss_delta = 0.121194,
|
||||
... init_cell_selection_weights_to_zero = True,
|
||||
... select_one_column = True,
|
||||
... allow_empty_column_selection = False,
|
||||
... temperature = 0.0352513,
|
||||
... )
|
||||
>>> model = TapasForQuestionAnswering.from_pretrained('google/tapas-base', config=config)
|
||||
|
||||
>>> # or, the base sized model with WikiSQL configuration
|
||||
>>> config = TapasConfig(
|
||||
... num_aggregation_labels = 4
|
||||
... use_answer_as_supervision = False
|
||||
... answer_loss_cutoff = 36.4519
|
||||
... cell_selection_preference = 0.903421
|
||||
... huber_loss_delta = 222.088
|
||||
... init_cell_selection_weights_to_zero = True
|
||||
... select_one_column = True
|
||||
... allow_empty_column_selection = True
|
||||
... temperature = 0.763141
|
||||
... )
|
||||
>>> model = TapasForQuestionAnswering.from_pretrained('google/tapas-base', config=config)
|
||||
|
||||
|
||||
Of course, you don't necessarily have to follow one of these three ways in which TAPAS was fine-tuned. You can also
|
||||
experiment by defining any hyperparameters you want when initializing :class:`~transformers.TapasConfig`, and then
|
||||
create a :class:`~transformers.TapasForQuestionAnswering` based on that configuration. For example, if you have a
|
||||
dataset that has both conversational questions and questions that might involve aggregation, then you can do it this
|
||||
way. Here's an example:
|
||||
|
||||
.. code-block::
|
||||
|
||||
>>> from transformers import TapasConfig, TapasForQuestionAnswering
|
||||
|
||||
>>> # you can initialize the classification heads any way you want (see docs of TapasConfig)
|
||||
>>> config = TapasConfig(num_aggregation_labels=3, average_logits_per_cell=True, select_one_column=False)
|
||||
>>> # initializing the pre-trained base sized model with our custom classification heads
|
||||
>>> model = TapasForQuestionAnswering.from_pretrained('google/tapas-base', config=config)
|
||||
|
||||
What you can also do is start from an already fine-tuned checkpoint. A note here is that the already fine-tuned
|
||||
checkpoint on WTQ has some issues due to the L2-loss which is somewhat brittle. See `here
|
||||
<https://github.com/google-research/tapas/issues/91#issuecomment-735719340>`__ for more info.
|
||||
|
||||
For a list of all pre-trained and fine-tuned TAPAS checkpoints available in the HuggingFace model hub, see `here
|
||||
<https://huggingface.co/models?search=tapas>`__.
|
||||
|
||||
**STEP 2: Prepare your data in the SQA format**
|
||||
|
||||
Second, no matter what you picked above, you should prepare your dataset in the `SQA format
|
||||
<https://www.microsoft.com/en-us/download/details.aspx?id=54253>`__. This format is a TSV/CSV file with the following
|
||||
columns:
|
||||
|
||||
- ``id``: optional, id of the table-question pair, for bookkeeping purposes.
|
||||
- ``annotator``: optional, id of the person who annotated the table-question pair, for bookkeeping purposes.
|
||||
- ``position``: integer indicating if the question is the first, second, third,... related to the table. Only required
|
||||
in case of conversational setup (SQA). You don't need this column in case you're going for WTQ/WikiSQL-supervised.
|
||||
- ``question``: string
|
||||
- ``table_file``: string, name of a csv file containing the tabular data
|
||||
- ``answer_coordinates``: list of one or more tuples (each tuple being a cell coordinate, i.e. row, column pair that is
|
||||
part of the answer)
|
||||
- ``answer_text``: list of one or more strings (each string being a cell value that is part of the answer)
|
||||
- ``aggregation_label``: index of the aggregation operator. Only required in case of strong supervision for aggregation
|
||||
(the WikiSQL-supervised case)
|
||||
- ``float_answer``: the float answer to the question, if there is one (np.nan if there isn't). Only required in case of
|
||||
weak supervision for aggregation (such as WTQ and WikiSQL)
|
||||
|
||||
The tables themselves should be present in a folder, each table being a separate csv file. Note that the authors of the
|
||||
TAPAS algorithm used conversion scripts with some automated logic to convert the other datasets (WTQ, WikiSQL) into the
|
||||
SQA format. The author explains this `here
|
||||
<https://github.com/google-research/tapas/issues/50#issuecomment-705465960>`__. Interestingly, these conversion scripts
|
||||
are not perfect (the ``answer_coordinates`` and ``float_answer`` fields are populated based on the ``answer_text``),
|
||||
meaning that WTQ and WikiSQL results could actually be improved.
|
||||
|
||||
**STEP 3: Convert your data into PyTorch tensors using TapasTokenizer**
|
||||
|
||||
Third, given that you've prepared your data in this TSV/CSV format (and corresponding CSV files containing the tabular
|
||||
data), you can then use :class:`~transformers.TapasTokenizer` to convert table-question pairs into :obj:`input_ids`,
|
||||
:obj:`attention_mask`, :obj:`token_type_ids` and so on. Again, based on which of the three cases you picked above,
|
||||
:class:`~transformers.TapasForQuestionAnswering` requires different inputs to be fine-tuned:
|
||||
|
||||
+------------------------------------+----------------------------------------------------------------------------------------------+
|
||||
| **Task** | **Required inputs** |
|
||||
+------------------------------------+----------------------------------------------------------------------------------------------+
|
||||
| Conversational | ``input_ids``, ``attention_mask``, ``token_type_ids``, ``labels`` |
|
||||
+------------------------------------+----------------------------------------------------------------------------------------------+
|
||||
| Weak supervision for aggregation | ``input_ids``, ``attention_mask``, ``token_type_ids``, ``labels``, ``numeric_values``, |
|
||||
| | ``numeric_values_scale``, ``float_answer`` |
|
||||
+------------------------------------+----------------------------------------------------------------------------------------------+
|
||||
| Strong supervision for aggregation | ``input ids``, ``attention mask``, ``token type ids``, ``labels``, ``aggregation_labels`` |
|
||||
+------------------------------------+----------------------------------------------------------------------------------------------+
|
||||
|
||||
:class:`~transformers.TapasTokenizer` creates the ``labels``, ``numeric_values`` and ``numeric_values_scale`` based on
|
||||
the ``answer_coordinates`` and ``answer_text`` columns of the TSV file. The ``float_answer`` and ``aggregation_labels``
|
||||
are already in the TSV file of step 2. Here's an example:
|
||||
|
||||
.. code-block::
|
||||
|
||||
>>> from transformers import TapasTokenizer
|
||||
>>> import pandas as pd
|
||||
|
||||
>>> model_name = 'google/tapas-base'
|
||||
>>> tokenizer = TapasTokenizer.from_pretrained(model_name)
|
||||
|
||||
>>> data = {'Actors': ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"], 'Number of movies': ["87", "53", "69"]}
|
||||
>>> queries = ["What is the name of the first actor?", "How many movies has George Clooney played in?", "What is the total number of movies?"]
|
||||
>>> answer_coordinates = [[(0, 0)], [(2, 1)], [(0, 1), (1, 1), (2, 1)]]
|
||||
>>> answer_text = [["Brad Pitt"], ["69"], ["209"]]
|
||||
>>> table = pd.DataFrame.from_dict(data)
|
||||
>>> inputs = tokenizer(table=table, queries=queries, answer_coordinates=answer_coordinates, answer_text=answer_text, padding='max_length', return_tensors='pt')
|
||||
>>> inputs
|
||||
{'input_ids': tensor([[ ... ]]), 'attention_mask': tensor([[...]]), 'token_type_ids': tensor([[[...]]]),
|
||||
'numeric_values': tensor([[ ... ]]), 'numeric_values_scale: tensor([[ ... ]]), labels: tensor([[ ... ]])}
|
||||
|
||||
Note that :class:`~transformers.TapasTokenizer` expects the data of the table to be **text-only**. You can use
|
||||
``.astype(str)`` on a dataframe to turn it into text-only data. Of course, this only shows how to encode a single
|
||||
training example. It is advised to create a PyTorch dataset and a corresponding dataloader:
|
||||
|
||||
.. code-block::
|
||||
|
||||
>>> import torch
|
||||
>>> import pandas as pd
|
||||
|
||||
>>> tsv_path = "your_path_to_the_tsv_file"
|
||||
>>> table_csv_path = "your_path_to_a_directory_containing_all_csv_files"
|
||||
|
||||
>>> class TableDataset(torch.utils.data.Dataset):
|
||||
... def __init__(self, data, tokenizer):
|
||||
... self.data = data
|
||||
... self.tokenizer = tokenizer
|
||||
...
|
||||
... def __getitem__(self, idx):
|
||||
... item = data.iloc[idx]
|
||||
... table = pd.read_csv(table_csv_path + item.table_file).astype(str) # be sure to make your table data text only
|
||||
... encoding = self.tokenizer(table=table,
|
||||
... queries=item.question,
|
||||
... answer_coordinates=item.answer_coordinates,
|
||||
... answer_text=item.answer_text,
|
||||
... truncation=True,
|
||||
... padding="max_length",
|
||||
... return_tensors="pt"
|
||||
... )
|
||||
... # remove the batch dimension which the tokenizer adds by default
|
||||
... encoding = {key: val.squeeze(0) for key, val in encoding.items()}
|
||||
... # add the float_answer which is also required (weak supervision for aggregation case)
|
||||
... encoding["float_answer"] = torch.tensor(item.float_answer)
|
||||
... return encoding
|
||||
...
|
||||
... def __len__(self):
|
||||
... return len(self.data)
|
||||
|
||||
>>> data = pd.read_csv(tsv_path, sep='\t')
|
||||
>>> train_dataset = TableDataset(data, tokenizer)
|
||||
>>> train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=32)
|
||||
|
||||
Note that here, we encode each table-question pair independently. This is fine as long as your dataset is **not
|
||||
conversational**. In case your dataset involves conversational questions (such as in SQA), then you should first group
|
||||
together the ``queries``, ``answer_coordinates`` and ``answer_text`` per table (in the order of their ``position``
|
||||
index) and batch encode each table with its questions. This will make sure that the ``prev_labels`` token types (see
|
||||
docs of :class:`~transformers.TapasTokenizer`) are set correctly.
|
||||
|
||||
**STEP 4: Train (fine-tune) TapasForQuestionAnswering**
|
||||
|
||||
You can then fine-tune :class:`~transformers.TapasForQuestionAnswering` using native PyTorch as follows (shown here for
|
||||
the weak supervision for aggregation case):
|
||||
|
||||
.. code-block::
|
||||
|
||||
>>> from transformers import TapasConfig, TapasForQuestionAnswering, AdamW
|
||||
|
||||
>>> # this is the default WTQ configuration
|
||||
>>> config = TapasConfig(
|
||||
... num_aggregation_labels = 4,
|
||||
... use_answer_as_supervision = True,
|
||||
... answer_loss_cutoff = 0.664694,
|
||||
... cell_selection_preference = 0.207951,
|
||||
... huber_loss_delta = 0.121194,
|
||||
... init_cell_selection_weights_to_zero = True,
|
||||
... select_one_column = True,
|
||||
... allow_empty_column_selection = False,
|
||||
... temperature = 0.0352513,
|
||||
... )
|
||||
>>> model = TapasForQuestionAnswering.from_pretrained("google/tapas-base", config=config)
|
||||
|
||||
>>> optimizer = AdamW(model.parameters(), lr=5e-5)
|
||||
|
||||
>>> for epoch in range(2): # loop over the dataset multiple times
|
||||
... for idx, batch in enumerate(train_dataloader):
|
||||
... # get the inputs;
|
||||
... input_ids = batch["input_ids"]
|
||||
... attention_mask = batch["attention_mask"]
|
||||
... token_type_ids = batch["token_type_ids"]
|
||||
... labels = batch["labels"]
|
||||
... numeric_values = batch["numeric_values"]
|
||||
... numeric_values_scale = batch["numeric_values_scale"]
|
||||
... float_answer = batch["float_answer"]
|
||||
|
||||
... # zero the parameter gradients
|
||||
... optimizer.zero_grad()
|
||||
|
||||
... # forward + backward + optimize
|
||||
... outputs = model(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids,
|
||||
... labels=labels, numeric_values=numeric_values, numeric_values_scale=numeric_values_scale,
|
||||
... float_answer=float_answer)
|
||||
... loss = outputs.loss
|
||||
... loss.backward()
|
||||
... optimizer.step()
|
||||
|
||||
Usage: inference
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
Here we explain how you can use :class:`~transformers.TapasForQuestionAnswering` for inference (i.e. making predictions
|
||||
on new data). For inference, only ``input_ids``, ``attention_mask`` and ``token_type_ids`` (which you can obtain using
|
||||
:class:`~transformers.TapasTokenizer`) have to be provided to the model to obtain the logits. Next, you can use the
|
||||
handy ``convert_logits_to_predictions`` method of :class:`~transformers.TapasTokenizer` to convert these into predicted
|
||||
coordinates and optional aggregation indices.
|
||||
|
||||
However, note that inference is **different** depending on whether or not the setup is conversational. In a
|
||||
non-conversational set-up, inference can be done in parallel on all table-question pairs of a batch. Here's an example
|
||||
of that:
|
||||
|
||||
.. code-block::
|
||||
|
||||
>>> from transformers import TapasTokenizer, TapasForQuestionAnswering
|
||||
>>> import pandas as pd
|
||||
|
||||
>>> model_name = 'google/tapas-base-finetuned-wtq'
|
||||
>>> model = TapasForQuestionAnswering.from_pretrained(model_name)
|
||||
>>> tokenizer = TapasTokenizer.from_pretrained(model_name)
|
||||
|
||||
>>> data = {'Actors': ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"], 'Number of movies': ["87", "53", "69"]}
|
||||
>>> queries = ["What is the name of the first actor?", "How many movies has George Clooney played in?", "What is the total number of movies?"]
|
||||
>>> table = pd.DataFrame.from_dict(data)
|
||||
>>> inputs = tokenizer(table=table, queries=queries, padding='max_length', return_tensors="pt")
|
||||
>>> outputs = model(**inputs)
|
||||
>>> predicted_answer_coordinates, predicted_aggregation_indices = tokenizer.convert_logits_to_predictions(
|
||||
... inputs,
|
||||
... outputs.logits.detach(),
|
||||
... outputs.logits_aggregation.detach()
|
||||
...)
|
||||
|
||||
>>> # let's print out the results:
|
||||
>>> id2aggregation = {0: "NONE", 1: "SUM", 2: "AVERAGE", 3:"COUNT"}
|
||||
>>> aggregation_predictions_string = [id2aggregation[x] for x in predicted_aggregation_indices]
|
||||
|
||||
>>> answers = []
|
||||
>>> for coordinates in predicted_answer_coordinates:
|
||||
... if len(coordinates) == 1:
|
||||
... # only a single cell:
|
||||
... answers.append(table.iat[coordinates[0]])
|
||||
... else:
|
||||
... # multiple cells
|
||||
... cell_values = []
|
||||
... for coordinate in coordinates:
|
||||
... cell_values.append(table.iat[coordinate])
|
||||
... answers.append(", ".join(cell_values))
|
||||
|
||||
>>> display(table)
|
||||
>>> print("")
|
||||
>>> for query, answer, predicted_agg in zip(queries, answers, aggregation_predictions_string):
|
||||
... print(query)
|
||||
... if predicted_agg == "NONE":
|
||||
... print("Predicted answer: " + answer)
|
||||
... else:
|
||||
... print("Predicted answer: " + predicted_agg + " > " + answer)
|
||||
What is the name of the first actor?
|
||||
Predicted answer: Brad Pitt
|
||||
How many movies has George Clooney played in?
|
||||
Predicted answer: COUNT > 69
|
||||
What is the total number of movies?
|
||||
Predicted answer: SUM > 87, 53, 69
|
||||
|
||||
In case of a conversational set-up, then each table-question pair must be provided **sequentially** to the model, such
|
||||
that the ``prev_labels`` token types can be overwritten by the predicted ``labels`` of the previous table-question
|
||||
pair.
|
||||
|
||||
|
||||
Tapas specific outputs
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.models.tapas.modeling_tapas.TableQuestionAnsweringOutput
|
||||
:members:
|
||||
|
||||
|
||||
TapasConfig
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TapasConfig
|
||||
:members:
|
||||
|
||||
|
||||
TapasTokenizer
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TapasTokenizer
|
||||
:members: __call__, convert_logits_to_predictions, save_vocabulary
|
||||
|
||||
|
||||
TapasModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TapasModel
|
||||
:members:
|
||||
|
||||
|
||||
TapasForMaskedLM
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TapasForMaskedLM
|
||||
:members:
|
||||
|
||||
|
||||
TapasForSequenceClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TapasForSequenceClassification
|
||||
:members: forward
|
||||
|
||||
|
||||
TapasForQuestionAnswering
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TapasForQuestionAnswering
|
||||
:members:
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
Transformer XL
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
@@ -105,11 +93,3 @@ TFTransfoXLLMHeadModel
|
||||
|
||||
.. autoclass:: transformers.TFTransfoXLLMHeadModel
|
||||
:members: call
|
||||
|
||||
|
||||
Internal Layers
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.AdaptiveEmbedding
|
||||
|
||||
.. autoclass:: transformers.TFAdaptiveEmbedding
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
XLM
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
XLM-ProphetNet
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
XLM-RoBERTa
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
@@ -62,13 +50,6 @@ XLMRobertaTokenizer
|
||||
create_token_type_ids_from_sequences, save_vocabulary
|
||||
|
||||
|
||||
XLMRobertaTokenizerFast
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.XLMRobertaTokenizerFast
|
||||
:members:
|
||||
|
||||
|
||||
XLMRobertaModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
XLNet
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
@@ -62,13 +50,6 @@ XLNetTokenizer
|
||||
create_token_type_ids_from_sequences, save_vocabulary
|
||||
|
||||
|
||||
XLNetTokenizerFast
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.XLNetTokenizerFast
|
||||
:members:
|
||||
|
||||
|
||||
XLNet specific outputs
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
Model sharing and uploading
|
||||
=======================================================================================================================
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
Summary of the models
|
||||
=======================================================================================================================
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
Multi-lingual models
|
||||
=======================================================================================================================
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
Perplexity of fixed-length models
|
||||
=======================================================================================================================
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
Philosophy
|
||||
=======================================================================================================================
|
||||
|
||||
|
||||
@@ -1,16 +1,4 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
reprocessing data
|
||||
Preprocessing data
|
||||
=======================================================================================================================
|
||||
|
||||
In this tutorial, we'll explore how to preprocess your data using 🤗 Transformers. The main tool for this is what we
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
Pretrained models
|
||||
=======================================================================================================================
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
Quick tour
|
||||
=======================================================================================================================
|
||||
|
||||
@@ -194,7 +182,6 @@ and get tensors back. You can specify all of that to the tokenizer:
|
||||
... ["We are very happy to show you the 🤗 Transformers library.", "We hope you don't hate it."],
|
||||
... padding=True,
|
||||
... truncation=True,
|
||||
... max_length=512,
|
||||
... return_tensors="pt"
|
||||
... )
|
||||
>>> ## TENSORFLOW CODE
|
||||
@@ -202,7 +189,6 @@ and get tensors back. You can specify all of that to the tokenizer:
|
||||
... ["We are very happy to show you the 🤗 Transformers library.", "We hope you don't hate it."],
|
||||
... padding=True,
|
||||
... truncation=True,
|
||||
... max_length=512,
|
||||
... return_tensors="tf"
|
||||
... )
|
||||
|
||||
|
||||
@@ -1,15 +1,4 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
***********************************************************************************************************************
|
||||
Exporting transformers models
|
||||
***********************************************************************************************************************
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
Summary of the tasks
|
||||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
Testing
|
||||
=======================================================================================================================
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
Summary of the tokenizers
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
@@ -1,15 +1,3 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
Training and fine-tuning
|
||||
=======================================================================================================================
|
||||
|
||||
|
||||
+26
-41
@@ -1,73 +1,58 @@
|
||||
<!---
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License");
|
||||
you may not use this file except in compliance with the License.
|
||||
You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software
|
||||
distributed under the License is distributed on an "AS IS" BASIS,
|
||||
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
See the License for the specific language governing permissions and
|
||||
limitations under the License.
|
||||
-->
|
||||
|
||||
# Examples
|
||||
|
||||
This folder contains actively maintained examples of use of 🤗 Transformers organized along NLP tasks. If you are looking for an example that used to
|
||||
be in this folder, it may have moved to our [research projects](https://github.com/huggingface/transformers/tree/master/examples/research_projects) subfolder (which contains frozen snapshots of research projects).
|
||||
Version 2.9 of 🤗 Transformers introduced a new [`Trainer`](https://github.com/huggingface/transformers/blob/master/src/transformers/trainer.py) class for PyTorch, and its equivalent [`TFTrainer`](https://github.com/huggingface/transformers/blob/master/src/transformers/trainer_tf.py) for TF 2.
|
||||
Running the examples requires PyTorch 1.3.1+ or TensorFlow 2.2+.
|
||||
|
||||
Here is the list of all our examples:
|
||||
- **grouped by task** (all official examples work for multiple models)
|
||||
- with information on whether they are **built on top of `Trainer`/`TFTrainer`** (if not, they still work, they might
|
||||
just lack some features),
|
||||
- whether or not they leverage the [🤗 Datasets](https://github.com/huggingface/datasets) library.
|
||||
- links to **Colab notebooks** to walk through the scripts and run them easily,
|
||||
- links to **Cloud deployments** to be able to deploy large-scale trainings in the Cloud with little to no setup.
|
||||
|
||||
|
||||
## Important note
|
||||
|
||||
**Important**
|
||||
|
||||
To make sure you can successfully run the latest versions of the example scripts, you have to **install the library from source** and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:
|
||||
To make sure you can successfully run the latest versions of the example scripts, you have to **install the library from source** and install some example-specific requirements.
|
||||
Execute the following steps in a new virtual environment:
|
||||
|
||||
```bash
|
||||
git clone https://github.com/huggingface/transformers
|
||||
cd transformers
|
||||
pip install .
|
||||
```
|
||||
Then cd in the example folder of your choice and run
|
||||
```bash
|
||||
pip install -r requirements.txt
|
||||
pip install -r ./examples/requirements.txt
|
||||
```
|
||||
|
||||
Alternatively, you can run the version of the examples as they were for your current version of Transformers via (for instance with v3.5.1):
|
||||
Alternatively, you can run the version of the examples as they were for your current version of Transformers via (for instance with v3.4.0):
|
||||
```bash
|
||||
git checkout tags/v3.5.1
|
||||
git checkout tags/v3.4.0
|
||||
```
|
||||
|
||||
## The Big Table of Tasks
|
||||
|
||||
Here is the list of all our examples:
|
||||
- with information on whether they are **built on top of `Trainer`/`TFTrainer`** (if not, they still work, they might
|
||||
just lack some features),
|
||||
- whether or not they leverage the [🤗 Datasets](https://github.com/huggingface/datasets) library.
|
||||
- links to **Colab notebooks** to walk through the scripts and run them easily,
|
||||
<!--
|
||||
Coming soon!
|
||||
- links to **Cloud deployments** to be able to deploy large-scale trainings in the Cloud with little to no setup.
|
||||
-->
|
||||
|
||||
| Task | Example datasets | Trainer support | TFTrainer support | 🤗 Datasets | Colab
|
||||
|---|---|:---:|:---:|:---:|:---:|
|
||||
| [**`language-modeling`**](https://github.com/huggingface/transformers/tree/master/examples/language-modeling) | Raw text | ✅ | - | ✅ | [](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/01_how_to_train.ipynb)
|
||||
| [**`multiple-choice`**](https://github.com/huggingface/transformers/tree/master/examples/multiple-choice) | SWAG, RACE, ARC | ✅ | ✅ | - | [](https://colab.research.google.com/github/ViktorAlm/notebooks/blob/master/MPC_GPU_Demo_for_TF_and_PT.ipynb)
|
||||
| [**`question-answering`**](https://github.com/huggingface/transformers/tree/master/examples/question-answering) | SQuAD | ✅ | ✅ | ✅ | -
|
||||
| [**`summarization`**](https://github.com/huggingface/transformers/tree/master/examples/seq2seq) | CNN/Daily Mail | ✅ | - | - | -
|
||||
| [**`text-classification`**](https://github.com/huggingface/transformers/tree/master/examples/text-classification) | GLUE, XNLI | ✅ | ✅ | ✅ | [](https://github.com/huggingface/notebooks/blob/master/examples/text_classification.ipynb)
|
||||
| [**`text-generation`**](https://github.com/huggingface/transformers/tree/master/examples/text-generation) | - | n/a | n/a | - | [](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/02_how_to_generate.ipynb)
|
||||
| [**`token-classification`**](https://github.com/huggingface/transformers/tree/master/examples/token-classification) | CoNLL NER | ✅ | ✅ | ✅ | -
|
||||
| [**`multiple-choice`**](https://github.com/huggingface/transformers/tree/master/examples/multiple-choice) | SWAG, RACE, ARC | ✅ | ✅ | - | [](https://colab.research.google.com/github/ViktorAlm/notebooks/blob/master/MPC_GPU_Demo_for_TF_and_PT.ipynb)
|
||||
| [**`question-answering`**](https://github.com/huggingface/transformers/tree/master/examples/question-answering) | SQuAD | ✅ | ✅ | - | -
|
||||
| [**`text-generation`**](https://github.com/huggingface/transformers/tree/master/examples/text-generation) | - | n/a | n/a | - | [](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/02_how_to_generate.ipynb)
|
||||
| [**`distillation`**](https://github.com/huggingface/transformers/tree/master/examples/distillation) | All | - | - | - | -
|
||||
| [**`summarization`**](https://github.com/huggingface/transformers/tree/master/examples/seq2seq) | CNN/Daily Mail | ✅ | - | - | -
|
||||
| [**`translation`**](https://github.com/huggingface/transformers/tree/master/examples/seq2seq) | WMT | ✅ | - | - | -
|
||||
| [**`bertology`**](https://github.com/huggingface/transformers/tree/master/examples/bertology) | - | - | - | - | -
|
||||
| [**`adversarial`**](https://github.com/huggingface/transformers/tree/master/examples/adversarial) | HANS | ✅ | - | - | -
|
||||
|
||||
|
||||
<!--
|
||||
<br>
|
||||
|
||||
## One-click Deploy to Cloud (wip)
|
||||
|
||||
**Coming soon!**
|
||||
-->
|
||||
|
||||
## Running on TPUs
|
||||
|
||||
|
||||
@@ -1,20 +0,0 @@
|
||||
tensorboard
|
||||
scikit-learn
|
||||
seqeval
|
||||
psutil
|
||||
sacrebleu
|
||||
rouge-score
|
||||
tensorflow_datasets
|
||||
matplotlib
|
||||
git-python==1.0.3
|
||||
faiss-cpu
|
||||
streamlit
|
||||
elasticsearch
|
||||
nltk
|
||||
pandas
|
||||
datasets >= 1.1.3
|
||||
fire
|
||||
pytest
|
||||
conllu
|
||||
sentencepiece != 0.1.92
|
||||
protobuf
|
||||
@@ -1,19 +1,3 @@
|
||||
<!---
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License");
|
||||
you may not use this file except in compliance with the License.
|
||||
You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software
|
||||
distributed under the License is distributed on an "AS IS" BASIS,
|
||||
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
See the License for the specific language governing permissions and
|
||||
limitations under the License.
|
||||
-->
|
||||
|
||||
# 🤗 Benchmark results
|
||||
|
||||
Here, you can find a list of the different benchmark results created by the community.
|
||||
|
||||
@@ -1,17 +1,3 @@
|
||||
# Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import csv
|
||||
from collections import defaultdict
|
||||
from dataclasses import dataclass, field
|
||||
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user