T5 Base Finetuned Emotion

🚀 T5-base fine-tuned for Emotion Recognition 😂😢😡😃😯

This is a fine-tuned version of Google's T5 base model on the emotion recognition dataset for the downstream task of emotion recognition. It leverages the power of transfer learning to accurately classify emotions in text.

🚀 Quick Start

from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-emotion")

model = AutoModelWithLMHead.from_pretrained("mrm8488/t5-base-finetuned-emotion")

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

  output = model.generate(input_ids=input_ids,
               max_length=2)
  
  dec = [tokenizer.decode(ids) for ids in output]
  label = dec[0]
  return label
  
get_emotion("i feel as if i havent blogged in ages are at least truly blogged i am doing an update cute") # Output: 'joy'
 
get_emotion("i have a feeling i kinda lost my best friend") # Output: 'sadness'

✨ Features

• Based on Google's T5 base model, which has shown excellent performance in various NLP tasks.
• Fine-tuned on a high - quality emotion recognition dataset, enabling accurate classification of 6 different emotions.
• Comes with a simple and easy - to - use Python API for quick integration into projects.

📚 Documentation

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. Here is 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.

Details of the downstream task (Sentiment Recognition) - Dataset 📚

Elvis Saravia has gathered a great dataset for emotion recognition. It allows to classify the text into one of the following 6 emotions:

• sadness 😢
• joy 😃
• love 🥰
• anger 😡
• fear 😱
• surprise 😯

Model fine-tuning 🏋️‍

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

Test set metrics 🧾

Created by Manuel Romero/@mrm8488 | LinkedIn

Made with ♥ in Spain