---
license: apache-2.0
language: en
datasets:
- sst2
metrics:
- precision
- recall
- f1
tags:
- text-classification
---

# T5-base fine-tuned for Sentiment Analysis 👍👎


[OpenAI's GPT-2](https://openai.com/blog/tags/gpt-2/) medium fine-tuned on [SST-2](https://huggingface.co/datasets/st2) dataset for **Sentiment Analysis** downstream task.

## Details of T5

The **GPT-2** model was presented in [Language Models are Unsupervised Multitask Learners](https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf) by *Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever*

## Model fine-tuning 🏋️‍

The model has been finetuned for 10 epochs on standard hyperparameters


## Val set metrics 🧾

               |precision | recall  | f1-score |support|
    |----------|----------|---------|----------|-------|
    |negative  |     0.92 |     0.92|      0.92|   428 |
    |positive  |     0.92 |     0.93|      0.92|   444 |
    |----------|----------|---------|----------|-------|
    |accuracy|            |         |      0.92|   872 |
    |macro avg|       0.92|     0.92|      0.92|   872 |
    |weighted avg|    0.92|     0.92|      0.92|   872 |


## Model in Action 🚀

```python
from transformers import GPT2Tokenizer, GPT2ForSequenceClassification

tokenizer = GPT2Tokenizer.from_pretrained("michelecafagna26/gpt2-medium-finetuned-sst2-sentiment")
model = GPT2ForSequenceClassification.from_pretrained("michelecafagna26/gpt2-medium-finetuned-sst2-sentiment")

inputs = tokenizer("I love it", return_tensors="pt")

model(**inputs).logits.argmax(axis=1)

# 1: Positive, 0: Negative
# Output: tensor([1])
```

> This model card is based on "mrm8488/t5-base-finetuned-imdb-sentiment" by Manuel Romero/@mrm8488