tinybert-sentiment-amazon
This model is a fine-tuned version of bert-tiny on amazon-polarity dataset. 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 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.
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
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