| | --- |
| | datasets: |
| | - amazon_polarity |
| | base_model: prajjwal1/bert-tiny |
| | model-index: |
| | - name: amazon_polarity |
| | 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). |
