Update README.md

README.md

@@ -1,3 +1,55 @@
 ---
 license: apache-2.0
+language: en
+datasets:
+- sst2
 ---
+
+# 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  |     1.00 |     1.00|      1.00|   428 |
+    |positive  |     1.00 |     1.00|      1.00|   444 |
+    |----------|----------|---------|----------|-------|
+    |accuracy|            |         |      1.00|   872 |
+    |macro avg|       1.00|     1.00|      1.00|   872 |
+    |weighted avg|    1.00|     1.00|     1.00 |   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")
+
+# Output: ['positive']
+```
+
+> This model card is based on "mrm8488/t5-base-finetuned-imdb-sentiment" by Manuel Romero/@mrm8488