From bf493d556990271d1d2bfbce14d89443dc20f927 Mon Sep 17 00:00:00 2001 From: Manuel Romero Date: Mon, 22 Jun 2020 19:45:45 +0200 Subject: [PATCH] Model card for t5-base-finetuned-emotion (recognition) (#5179) --- .../t5-base-finetuned-emotion/README.md | 81 +++++++++++++++++++ 1 file changed, 81 insertions(+) create mode 100644 model_cards/mrm8488/t5-base-finetuned-emotion/README.md diff --git a/model_cards/mrm8488/t5-base-finetuned-emotion/README.md b/model_cards/mrm8488/t5-base-finetuned-emotion/README.md new file mode 100644 index 0000000000..f819be9bd5 --- /dev/null +++ b/model_cards/mrm8488/t5-base-finetuned-emotion/README.md @@ -0,0 +1,81 @@ +-- +language: english +--- + +# 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://camo.githubusercontent.com/623b4dea0b653f2ad3f36c71ebfe749a677ac0a1/68747470733a2f2f6d69726f2e6d656469756d2e636f6d2f6d61782f343030362f312a44304a31674e51663876727255704b657944387750412e706e67) + +## 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