README.md

---
datasets:
- amazon_polarity
base_model: prajjwal1/bert-tiny
model-index:
- name: tinybert-sentiment-amazon 
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: amazon_polarity
      type: sentiment
      args: default
    metrics:
    - type: accuracy
      value: 0.942
      name: Accuracy
    - type: loss
      value: 0.153
      name: Loss
    - type: f1
      value: 0.940
      name: F1
---
# tinybert-sentiment-amazon

This model is a fine-tuned version of [bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on [amazon-polarity dataset](https://huggingface.co/datasets/amazon_polarity). It achieves the following results on the evaluation set:
* Loss: 0.153
* Accuracy: 0.942
* F1_score: 0.940

## 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). 
This model was trained using the entire dataset (3.6M of samples) in constrast to the [distilbert model](https://huggingface.co/AdamCodd/distilbert-base-uncased-finetuned-sentiment-amazon) which was trained on only 10% of the dataset.

## 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).