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--- |
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license: apache-2.0 |
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base_model: distilbert-base-uncased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: distilBERT_without_preprocessing_grid_search |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# distilBERT_without_preprocessing_grid_search |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co./distilbert-base-uncased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7575 |
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- Precision: 0.8533 |
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- Recall: 0.8477 |
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- F1: 0.8486 |
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- Accuracy: 0.8847 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 1.0 | 257 | 0.5635 | 0.7528 | 0.8373 | 0.7788 | 0.8439 | |
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| 0.7607 | 2.0 | 514 | 0.5324 | 0.8060 | 0.8314 | 0.8098 | 0.8648 | |
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| 0.7607 | 3.0 | 771 | 0.5216 | 0.8152 | 0.8475 | 0.8265 | 0.8765 | |
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| 0.2593 | 4.0 | 1028 | 0.5493 | 0.8179 | 0.8585 | 0.8348 | 0.8823 | |
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| 0.2593 | 5.0 | 1285 | 0.6226 | 0.8220 | 0.8419 | 0.8308 | 0.8794 | |
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| 0.1473 | 6.0 | 1542 | 0.6677 | 0.8429 | 0.8485 | 0.8442 | 0.8818 | |
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| 0.1473 | 7.0 | 1799 | 0.6611 | 0.8316 | 0.8481 | 0.8381 | 0.8823 | |
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| 0.096 | 8.0 | 2056 | 0.7404 | 0.8528 | 0.8448 | 0.8478 | 0.8857 | |
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| 0.096 | 9.0 | 2313 | 0.7401 | 0.8531 | 0.8476 | 0.8484 | 0.8862 | |
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| 0.0642 | 10.0 | 2570 | 0.7575 | 0.8533 | 0.8477 | 0.8486 | 0.8847 | |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
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