| | --- |
| | license: apache-2.0 |
| | base_model: distilbert-base-uncased |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | model-index: |
| | - name: distilbert-q-classifier-2 |
| | 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. --> |
| |
|
| | # distilbert-q-classifier-2 |
| |
|
| | This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.2779 |
| | - Accuracy: 0.9421 |
| | - Precision Weighted: 0.9429 |
| | - Recall Weighted: 0.9421 |
| | - F1 Weighted: 0.9421 |
| | - Precision Macro: 0.9429 |
| | - Recall Macro: 0.9421 |
| | - F1 Macro: 0.9421 |
| |
|
| | ## 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: 2e-05 |
| | - 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: 10 |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision Weighted | Recall Weighted | F1 Weighted | Precision Macro | Recall Macro | F1 Macro | |
| | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------------------:|:---------------:|:-----------:|:---------------:|:------------:|:--------:| |
| | | No log | 1.0 | 48 | 0.2252 | 0.9144 | 0.9144 | 0.9144 | 0.9144 | 0.9144 | 0.9144 | 0.9144 | |
| | | No log | 2.0 | 96 | 0.1682 | 0.9329 | 0.9333 | 0.9329 | 0.9329 | 0.9333 | 0.9329 | 0.9329 | |
| | | No log | 3.0 | 144 | 0.2251 | 0.9236 | 0.9269 | 0.9236 | 0.9235 | 0.9269 | 0.9236 | 0.9235 | |
| | | No log | 4.0 | 192 | 0.2421 | 0.9352 | 0.9376 | 0.9352 | 0.9351 | 0.9376 | 0.9352 | 0.9351 | |
| | | No log | 5.0 | 240 | 0.2138 | 0.9375 | 0.9383 | 0.9375 | 0.9375 | 0.9383 | 0.9375 | 0.9375 | |
| | | No log | 6.0 | 288 | 0.2165 | 0.9398 | 0.9399 | 0.9398 | 0.9398 | 0.9399 | 0.9398 | 0.9398 | |
| | | No log | 7.0 | 336 | 0.2470 | 0.9398 | 0.9408 | 0.9398 | 0.9398 | 0.9408 | 0.9398 | 0.9398 | |
| | | No log | 8.0 | 384 | 0.2509 | 0.9352 | 0.9353 | 0.9352 | 0.9352 | 0.9353 | 0.9352 | 0.9352 | |
| | | No log | 9.0 | 432 | 0.2686 | 0.9352 | 0.9355 | 0.9352 | 0.9352 | 0.9355 | 0.9352 | 0.9352 | |
| | | No log | 10.0 | 480 | 0.2779 | 0.9421 | 0.9429 | 0.9421 | 0.9421 | 0.9429 | 0.9421 | 0.9421 | |
| |
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| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.43.3 |
| | - Pytorch 2.3.1 |
| | - Datasets 2.20.0 |
| | - Tokenizers 0.19.1 |
| |
|
