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mrm8488/t5-small-finetuned-imdb-sentiment mrm8488/t5-small-finetuned-imdb-sentiment
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Contributed by

mrm8488 Manuel Romero
155 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-imdb-sentiment") model = AutoModelForSeq2SeqLM.from_pretrained("mrm8488/t5-small-finetuned-imdb-sentiment")

T5-small fine-tuned for Sentiment Anlalysis πŸŽžοΈπŸ‘πŸ‘Ž

Google's T5 small fine-tuned on IMDB dataset for Sentiment Analysis 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 downstream task (Sentiment analysis) - Dataset πŸ“š


This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. It provides a set of 25,000 highly polar movie reviews for training, and 25,000 for testing.

Model fine-tuning πŸ‹οΈβ€

The training script is a slightly modified version of this Colab Notebook created by Suraj Patil, so all credits to him!

Test set metrics 🧾

precision recall f1-score support
negative 0.92 0.93 0.92 12500
positive 0.93 0.92 0.92 12500
---------- ---------- --------- ---------- -------
accuracy 0.92 25000
macro avg 0.92 0.92 0.92 25000
weighted avg 0.92 0.92 0.92 25000

Model in Action πŸš€

from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-small-finetuned-imdb-sentiment")

model = AutoModelWithLMHead.from_pretrained("mrm8488/t5-small-finetuned-imdb-sentiment")

def get_sentiment(text):
  input_ids = tokenizer.encode(text + '</s>', return_tensors='pt')

  output = model.generate(input_ids=input_ids,

  dec = [tokenizer.decode(ids) for ids in output]
  label = dec[0]
  return label

get_sentiment("I dislike a lot that film")

# Output: 'negative'

Created by Manuel Romero/@mrm8488 | LinkedIn

Made with in Spain