| --- |
| license: apache-2.0 |
| tags: |
| - generated_from_trainer |
| datasets: |
| - glue |
| metrics: |
| - accuracy |
| model-index: |
| - name: tiny-bert-sst2-distilled |
| results: |
| - task: |
| name: Text Classification |
| type: text-classification |
| dataset: |
| name: glue |
| type: glue |
| args: sst2 |
| metrics: |
| - name: Accuracy |
| type: accuracy |
| value: 0.8325688073394495 |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # tiny-bert-sst2-distilled |
|
|
| This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the glue dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 1.7305 |
| - Accuracy: 0.8326 |
|
|
| ## Model description |
|
|
| More information needed |
|
|
| ## Intended uses & limitations |
|
|
| More information needed |
|
|
| ## Training and evaluation data |
|
|
| More information needed |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 0.0007199555649276667 |
| - train_batch_size: 1024 |
| - eval_batch_size: 1024 |
| - seed: 33 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 7 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| |
| | 1.77 | 1.0 | 66 | 1.6939 | 0.8165 | |
| | 0.729 | 2.0 | 132 | 1.5090 | 0.8326 | |
| | 0.5242 | 3.0 | 198 | 1.5369 | 0.8257 | |
| | 0.4017 | 4.0 | 264 | 1.7025 | 0.8326 | |
| | 0.327 | 5.0 | 330 | 1.6743 | 0.8245 | |
| | 0.2749 | 6.0 | 396 | 1.7305 | 0.8337 | |
| | 0.2521 | 7.0 | 462 | 1.7305 | 0.8326 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.12.3 |
| - Pytorch 1.9.1 |
| - Datasets 1.15.1 |
| - Tokenizers 0.10.3 |
|
|
