Update README.md

README.md

@@ -1,3 +1,56 @@
 ---
 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