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mrm8488/t5-small-finetuned-quora-for-paraphrasing mrm8488/t5-small-finetuned-quora-for-paraphrasing
310 downloads
last 30 days

pytorch

tf

Contributed by

mrm8488 Manuel Romero
156 models

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

			
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-small-finetuned-quora-for-paraphrasing") model = AutoModelForSeq2SeqLM.from_pretrained("mrm8488/t5-small-finetuned-quora-for-paraphrasing")

T5-base fine-tuned on Quora question pair dataset for Question Paraphrasing ❓↔️❓

Google's T5 fine-tuned on Quodra question pair dataset for Question Paraphrasing 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 downstream task (Question Paraphrasing) - Dataset πŸ“šβ“β†”οΈβ“

Dataset ID: quora from HugginFace/NLP

Dataset Split # samples
quora train 404290
quora after filter repeated questions train 149263

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 one

Model in Action πŸš€

from transformers import AutoModelWithLMHead, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-small-finetuned-quora-for-paraphrasing")
model = AutoModelWithLMHead.from_pretrained("mrm8488/t5-small-finetuned-quora-for-paraphrasing")

def paraphrase(text, max_length=128):

  input_ids = tokenizer.encode(text, return_tensors="pt", add_special_tokens=True)

  generated_ids = model.generate(input_ids=input_ids, num_return_sequences=5, num_beams=5, max_length=max_length, no_repeat_ngram_size=2, repetition_penalty=3.5, length_penalty=1.0, early_stopping=True)

  preds = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True) for g in generated_ids]

  return preds

preds = paraphrase("paraphrase: What is the best framework for dealing with a huge text dataset?")

for pred in preds:
  print(pred)

# Output:
'''
What is the best framework for dealing with a huge text dataset?
What is the best framework for dealing with a large text dataset?
What is the best framework to deal with a huge text dataset?
What are the best frameworks for dealing with a huge text dataset?
What is the best framework for dealing with huge text datasets?
'''

Created by Manuel Romero/@mrm8488 | LinkedIn

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