| --- |
| license: apache-2.0 |
| base_model: distilbert-base-uncased |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: test_model_1 |
| results: [] |
| --- |
| |
| <!-- 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. --> |
|
|
| # test_model_1 |
|
|
| This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.4457 |
| - Accuracy: 0.8989 |
|
|
| ## 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: 5e-06 |
| - train_batch_size: 32 |
| - eval_batch_size: 32 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 15 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| |
| | No log | 1.0 | 125 | 0.3754 | 0.8949 | |
| | No log | 2.0 | 250 | 0.3674 | 0.8969 | |
| | No log | 3.0 | 375 | 0.3574 | 0.8999 | |
| | 0.1345 | 4.0 | 500 | 0.3786 | 0.9029 | |
| | 0.1345 | 5.0 | 625 | 0.3912 | 0.8979 | |
| | 0.1345 | 6.0 | 750 | 0.3946 | 0.8969 | |
| | 0.1345 | 7.0 | 875 | 0.4166 | 0.8959 | |
| | 0.0837 | 8.0 | 1000 | 0.4201 | 0.8969 | |
| | 0.0837 | 9.0 | 1125 | 0.4270 | 0.9029 | |
| | 0.0837 | 10.0 | 1250 | 0.4283 | 0.8969 | |
| | 0.0837 | 11.0 | 1375 | 0.4416 | 0.9039 | |
| | 0.0554 | 12.0 | 1500 | 0.4422 | 0.9019 | |
| | 0.0554 | 13.0 | 1625 | 0.4409 | 0.8989 | |
| | 0.0554 | 14.0 | 1750 | 0.4419 | 0.8989 | |
| | 0.0554 | 15.0 | 1875 | 0.4457 | 0.8989 | |
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|
|
| ### Framework versions |
|
|
| - Transformers 4.36.1 |
| - Pytorch 2.1.2 |
| - Datasets 2.15.0 |
| - Tokenizers 0.15.0 |
|
|
