readme: ---
  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 <span style="color: #e25555;">&hearts;</span> in Spain
  