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T5-base fine-tuned on QASC

Google's T5 fine-tuned on 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 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 β€œColossal 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

Details of the dataset πŸ“š

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 πŸ‹οΈβ€

The training script is a slightly modified version of this awesome one by Suraj Patil. 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 thetext right answer instead of the label (A, B, C... See choices field). More details about the dataset format/fields here

Metrics on validation set πŸ“‹

Metric Score
Accuracy (EM) 97.73

Model in Action πŸš€

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'], 

  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 | LinkedIn

Made with β™₯ in Spain

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Dataset used to train mrm8488/t5-base-finetuned-qasc