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https://api-inference.huggingface.co/models/mrm8488/t5-base-finetuned-question-generation-ap
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mrm8488/t5-base-finetuned-question-generation-ap mrm8488/t5-base-finetuned-question-generation-ap
3,044 downloads
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pytorch

tf

Contributed by

mrm8488 Manuel Romero
146 models

How to use this model directly from the πŸ€—/transformers library:

			
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from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-question-generation-ap") model = AutoModelWithLMHead.from_pretrained("mrm8488/t5-base-finetuned-question-generation-ap")

T5-base fine-tuned on SQuAD for Question Generation

Google's T5 fine-tuned on SQuAD v1.1 for Question Generation by just prepending the answer to the context.

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 downstream task (Q&A) - Dataset πŸ“š 🧐 ❓

Dataset ID: squad from HugginFace/NLP

Dataset Split # samples
squad train 87599
squad valid 10570

How to load it from nlp

train_dataset  = nlp.load_dataset('squad, split=nlp.Split.TRAIN)
valid_dataset = nlp.load_dataset('squad', split=nlp.Split.VALIDATION)

Check out more about this dataset and others in NLP Viewer

Model fine-tuning πŸ‹οΈβ€

The training script is a slightly modified version of this awesome one by Suraj Patil

He also made a great research on Question Generation

Model in Action πŸš€

# Tip: By now, install transformers from source

from transformers import AutoModelWithLMHead, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-question-generation-ap")
model = AutoModelWithLMHead.from_pretrained("mrm8488/t5-base-finetuned-question-generation-ap")

def get_question(answer, context, max_length=64):
  input_text = "answer: %s  context: %s </s>" % (answer, 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])

context = "Manuel have created RuPERTa-base with the support of HF-Transformers and Google"
answer = "Manuel"

get_question(answer, context)

# output: question: Who created the RuPERTa-base?

Created by Manuel Romero/@mrm8488 | LinkedIn

Made with in Spain