Create README.md

README.md

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+---
+datasets:
+- amazon_polarity
+---
+# tinybert-sentiment-amazon
+
+This model is a fine-tuned version of [bert-tiny](prajjwal1/bert-tiny) on [amazon-polarity dataset](https://huggingface.co/datasets/amazon_polarity).
+
+## Model description
+
+TinyBERT is 7.5 times smaller and 9.4 times faster on inference compared to its teacher BERT model (while DistilBERT is 40% smaller and 1.6 times faster than BERT). 
+Compared to the [distilbert model](https://huggingface.co/AdamCodd/distilbert-base-uncased-finetuned-sentiment-amazon) which was trained on 10% of the dataset, this model was trained on the full dataset (3.6M of samples).
+
+## Intended uses & limitations
+While this model may not be as accurate as the distilbert model, its performance should be enough for most use cases.
+
+```python
+from transformers import pipeline
+
+# Create the pipeline
+sentiment_classifier = pipeline('text-classification', model='AdamCodd/tinybert-sentiment-amazon')
+
+# Now you can use the pipeline to classify emotions
+result = sentiment_classifier("This product doesn't fit me at all.")
+print(result)
+#[{'label': 'negative', 'score': 0.9969743490219116}]
+```
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 3e-05
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 1270
+- optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 150
+- num_epochs: 1
+- weight_decay: 0.01
+
+### Framework versions
+
+- Transformers 4.35.0
+- Pytorch lightning 2.1.0
+- Tokenizers 0.14.1
+
+If you want to support me, you can [here](https://ko-fi.com/adamcodd).