From de697935a20470c1adc772cb1a592254dbdd3cfa Mon Sep 17 00:00:00 2001 From: Manuel Romero Date: Mon, 16 Mar 2020 08:58:55 +0100 Subject: [PATCH] Create model card for spanbert-finetuned-squadv2 --- .../spanbert-finetuned-squadv2/README.md | 86 +++++++++++++++++++ 1 file changed, 86 insertions(+) create mode 100644 model_cards/mrm8488/spanbert-finetuned-squadv2/README.md diff --git a/model_cards/mrm8488/spanbert-finetuned-squadv2/README.md b/model_cards/mrm8488/spanbert-finetuned-squadv2/README.md new file mode 100644 index 00000000..47a4cc42 --- /dev/null +++ b/model_cards/mrm8488/spanbert-finetuned-squadv2/README.md @@ -0,0 +1,86 @@ +--- +language: english +thumbnail: +--- + +# SpanBERT (spanbert-base-cased) fine-tuned on SQuAD v2 + +[SpanBERT](https://github.com/facebookresearch/SpanBERT) created by [Facebook Research](https://github.com/facebookresearch) and fine-tuned on [SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) for **Q&A** downstream task. + +## Details of SpanBERT + +[SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://arxiv.org/abs/1907.10529) + +## Details of the downstream task (Q&A) - Dataset + +[SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. To do well on SQuAD2.0, systems must not only answer questions when possible, but also determine when no answer is supported by the paragraph and abstain from answering. + +| Dataset | Split | # samples | +| -------- | ----- | --------- | +| SQuAD2.0 | train | 130k | +| SQuAD2.0 | eval | 12.3k | + +## Model training + +The model was trained on a Tesla P100 GPU and 25GB of RAM. +The script for fine tuning can be found [here](https://github.com/huggingface/transformers/blob/master/examples/run_squad.py) + +## Results: + +| Metric | # Value | +| ------ | --------- | +| **EM** | **78.80** | +| **F1** | **82.22** | + +### Raw metrics: + +```json +{ + "exact": 78.80064010780762, + "f1": 82.22801347271162, + "total": 11873, + "HasAns_exact": 78.74493927125506, + "HasAns_f1": 85.60951483831069, + "HasAns_total": 5928, + "NoAns_exact": 78.85618166526493, + "NoAns_f1": 78.85618166526493, + "NoAns_total": 5945, + "best_exact": 78.80064010780762, + "best_exact_thresh": 0.0, + "best_f1": 82.2280134727116, + "best_f1_thresh": 0.0 +} +``` + +## Comparison: + +| Model | EM | F1 score | +| ----------------------------------------------------------------------------------------- | --------- | --------- | +| [SpanBert official repo](https://github.com/facebookresearch/SpanBERT#pre-trained-models) | - | 83.6\* | +| [spanbert-finetuned-squadv2](https://huggingface.co/mrm8488/spanbert-finetuned-squadv2) | **78.80** | **82.22** | + +## Model in action + +Fast usage with **pipelines**: + +```python +from transformers import pipeline + +qa_pipeline = pipeline( + "question-answering", + model="mrm8488/spanbert-finetuned-squadv2", + tokenizer="mrm8488/spanbert-finetuned-squadv2" +) + +qa_pipeline({ + 'context': "Manuel Romero has been working hardly in the repository hugginface/transformers lately", + 'question': "Who has been working hard for hugginface/transformers lately?" + +}) + +# Output: {'answer': 'Manuel Romero','end': 13,'score': 6.836378586818937e-09, 'start': 0} +``` + +> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) + +> Made with ♥ in Spain