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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: 0.8262 |
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- Precision: 0.8491 |
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- Recall: 0.8536 |
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- F1: 0.8511 |
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- Accuracy: 0.8837 |
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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: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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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| 0.8922 | 1.0 | 514 | 0.5350 | 0.7953 | 0.8363 | 0.8092 | 0.8628 | |
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| 0.4521 | 2.0 | 1028 | 0.5359 | 0.8214 | 0.8385 | 0.8282 | 0.8652 | |
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| 0.2928 | 3.0 | 1542 | 0.5876 | 0.8264 | 0.8504 | 0.8367 | 0.8798 | |
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| 0.2099 | 4.0 | 2056 | 0.6974 | 0.8288 | 0.8435 | 0.8351 | 0.8764 | |
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| 0.1531 | 5.0 | 2570 | 0.8245 | 0.8367 | 0.8125 | 0.8232 | 0.8710 | |
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| 0.1124 | 6.0 | 3084 | 0.7553 | 0.8349 | 0.8543 | 0.8435 | 0.8764 | |
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| 0.1045 | 7.0 | 3598 | 0.7912 | 0.8452 | 0.8538 | 0.8492 | 0.8822 | |
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| 0.0716 | 8.0 | 4112 | 0.7909 | 0.8422 | 0.8529 | 0.8471 | 0.8788 | |
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| 0.0746 | 9.0 | 4626 | 0.8364 | 0.8462 | 0.8458 | 0.8458 | 0.8779 | |
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| 0.0533 | 10.0 | 5140 | 0.8262 | 0.8491 | 0.8536 | 0.8511 | 0.8837 | |
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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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