--- language: en datasets: - emotion widget: - text: "I wish you were here but it is impossible" --- # T5-base fine-tuned for Emotion Recognition πŸ˜‚πŸ˜’πŸ˜‘πŸ˜ƒπŸ˜― [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) base fine-tuned on [emotion recognition](https://github.com/dair-ai/emotion_dataset) 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](https://arxiv.org/pdf/1910.10683.pdf) 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](https://i.imgur.com/jVFMMWR.png) ## Details of the downstream task (Sentiment Recognition) - Dataset πŸ“š [Elvis Saravia](https://twitter.com/omarsar0) has gathered a great [dataset](https://github.com/dair-ai/emotion_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](https://github.com/patil-suraj/exploring-T5/blob/master/t5_fine_tuning.ipynb) created by [Suraj Patil](https://github.com/patil-suraj), so all credits to him! ## Test set metrics 🧾 | |precision | recall | f1-score |support| |----------|----------|---------|----------|-------| |anger | 0.93| 0.92| 0.93| 275| |fear | 0.91| 0.87| 0.89| 224| |joy | 0.97| 0.94| 0.95| 695| |love | 0.80| 0.91| 0.85| 159| |sadness | 0.97| 0.97| 0.97| 521| |surpirse | 0.73| 0.89| 0.80| 66| | | |accuracy| | | 0.93| 2000| |macro avg| 0.89| 0.92| 0.90| 2000| |weighted avg| 0.94| 0.93| 0.93| 2000| ## Model in Action πŸš€ ```python 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 + '', 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' ``` > Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/) > Made with in Spain