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---
license: apache-2.0
language: en
datasets:
- sst2
metrics:
- precision
- recall
- f1
tags:
- text-classification
---
# T5-base fine-tuned for Sentiment Analysis 👍👎
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) base fine-tuned on [SST-2](https://huggingface.co/datasets/st2) 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](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*
## Model fine-tuning 🏋️
The model has been finetuned for 10 epochs on standard hyperparameters
## Val set metrics 🧾
|precision | recall | f1-score |support|
|----------|----------|---------|----------|-------|
|negative | 0.95 | 0.95| 0.95| 428 |
|positive | 0.94 | 0.96| 0.95| 444 |
|----------|----------|---------|----------|-------|
|accuracy| | | 0.95| 872 |
|macro avg| 0.95| 0.95| 0.95| 872 |
|weighted avg| 0.95| 0.95| 0.95 | 872 |
## Model in Action 🚀
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("t5-finetune-sst2")
model = T5ForConditionalGeneration.from_pretrained("t5-finetune-sst2")
def get_sentiment(text):
inputs = tokenizer("sentiment: " + text, max_length=128, truncation=True, return_tensors="pt").input_ids
preds = model.generate(inputs)
decoded_preds = tokenizer.batch_decode(sequences=preds, skip_special_tokens=True)
return decoded_preds
get_sentiment("This movie is awesome")
# labels are 'p' for 'positive' and 'n' for 'negative'
# Output: ['p']
```
> This model card is based on "mrm8488/t5-base-finetuned-imdb-sentiment" by Manuel Romero/@mrm8488
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