From 04561ecbe6402a862e0572511c8461c39ae22c8f Mon Sep 17 00:00:00 2001 From: Manuel Romero Date: Wed, 4 Nov 2020 17:20:15 +0100 Subject: [PATCH] Model card: T5-base fine-tuned on QASC (#8299) --- .../mrm8488/t5-base-finetuned-qasc/README.md | 66 +++++++++++++++++++ 1 file changed, 66 insertions(+) create mode 100644 model_cards/mrm8488/t5-base-finetuned-qasc/README.md diff --git a/model_cards/mrm8488/t5-base-finetuned-qasc/README.md b/model_cards/mrm8488/t5-base-finetuned-qasc/README.md new file mode 100644 index 0000000000..7259477c2a --- /dev/null +++ b/model_cards/mrm8488/t5-base-finetuned-qasc/README.md @@ -0,0 +1,66 @@ +--- +language: en +datasets: +- qasc +--- + +# T5-base fine-tuned on QASC + +[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned on [QASC](https://allenai.org/data/qasc) for **QA** (via *sentence composition*) downstream task. + +## Details of T5 + +The **T5** model was presented in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf) by *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu* in Here the abstract: + +Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new “Colossal Clean Crawled Corpus”, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code. + +![model image](https://i.imgur.com/jVFMMWR.png) + + +## Details of the dataset 📚 + +**Question Answering via Sentence Composition** (QASC) is a question-answering dataset with a focus on sentence composition. It consists of 9,980 8-way multiple-choice questions about grade school science (8,134 train, 926 dev, 920 test), and comes with a corpus of 17M sentences. + + +## Model fine-tuning 🏋️‍ + +The training script is a slightly modified version of [this awesome one](https://colab.research.google.com/github/patil-suraj/exploring-T5/blob/master/T5_on_TPU.ipynb) by [Suraj Patil](https://twitter.com/psuraj28). The **context** passed to the *encoder* is the combination of the 2 *facts* (`fact1` and `fact2`). The **question** is just the `formatted_question` field. The **answer** passed to the *decoder* is the`text` right answer instead of the `label` (A, B, C... See `choices` field). More details about the dataset format/fields [here](https://huggingface.co/nlp/viewer/?dataset=qasc) + +## Metrics on validation set 📋 + +| Metric | Score | +|--------|-------| +|Accuracy (EM) | **97.73**| + + +## Model in Action 🚀 + +```python +from transformers import AutoModelWithLMHead, AutoTokenizer + +tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-qasc") +model = AutoModelWithLMHead.from_pretrained("mrm8488/t5-base-finetuned-qasc") + +def get_response(question, context, max_length=64): + input_text = 'question: %s context: %s' % (question, context) + features = tokenizer([input_text], return_tensors='pt') + + output = model.generate(input_ids=features['input_ids'], + attention_mask=features['attention_mask'], + max_length=max_length) + + return tokenizer.decode(output[0]) + +fact_1 = 'a watch is used for measuring time' +fact_2 = 'Times are measured in seconds.' +context = fact_1 + ' ' + fact_2 +question = 'What can be used to measure seconds? (A) Watch (B) seconds (C) fluid (D) Ruler (E) goggles (F) glasses (G) Drill (H) Scale' + +get_response(question, context) + +# output: 'Watch' +``` + +> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/) + +> Made with ♥ in Spain