Back to all models
translation mask_token:
Query this model
πŸ”₯ This model is currently loaded and running on the Inference API. ⚠️ This model could not be loaded by the inference API. ⚠️ This model can be loaded on the Inference API on-demand.
JSON Output
API endpoint  

⚑️ Upgrade your account to access the Inference API

Share Copied link to clipboard

Monthly model downloads

mrm8488/t5-small-finetuned-emotion mrm8488/t5-small-finetuned-emotion
last 30 days



Contributed by

mrm8488 Manuel Romero
155 models

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

Copy to clipboard
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-small-finetuned-emotion") model = AutoModelForSeq2SeqLM.from_pretrained("mrm8488/t5-small-finetuned-emotion")

T5-small fine-tuned for Emotion Recognition πŸ˜‚πŸ˜’πŸ˜‘πŸ˜ƒπŸ˜―

Google's T5 small fine-tuned on emotion recognition dataset for Emotion Recognition 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 Recognition) - Dataset πŸ“š

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

  • sadness 😒
  • joy πŸ˜ƒ
  • love πŸ₯°
  • anger 😑
  • fear 😱
  • surprise 😯

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
anger 0.92 0.93 0.92 275
fear 0.90 0.90 0.90 224
joy 0.97 0.91 0.94 695
love 0.75 0.89 0.82 159
sadness 0.96 0.97 0.96 581
surpirse 0.73 0.80 0.76 66
accuracy 0.92 2000
macro avg 0.87 0.90 0.88 2000
weighted avg 0.93 0.92 0.92 2000

Confusion Matrix


Model in Action πŸš€

from transformers import AutoTokenizer, AutoModelWithLMHead

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

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

def get_emotion(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_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'

Created by Manuel Romero/@mrm8488 | LinkedIn

Made with in Spain