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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: 0.6943 |
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- Precision: 0.8467 |
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- Recall: 0.8562 |
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- F1: 0.8509 |
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- Accuracy: 0.8793 |
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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 | 0.5790 | 0.7659 | 0.8225 | 0.7875 | 0.8472 | |
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| 0.7473 | 2.0 | 514 | 0.5007 | 0.8115 | 0.8503 | 0.8264 | 0.8647 | |
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| 0.7473 | 3.0 | 771 | 0.4903 | 0.8007 | 0.8418 | 0.8174 | 0.8594 | |
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| 0.2608 | 4.0 | 1028 | 0.5370 | 0.8249 | 0.8491 | 0.8350 | 0.8657 | |
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| 0.2608 | 5.0 | 1285 | 0.6034 | 0.8424 | 0.8514 | 0.8455 | 0.8803 | |
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| 0.1543 | 6.0 | 1542 | 0.5988 | 0.8396 | 0.8565 | 0.8466 | 0.8788 | |
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| 0.1543 | 7.0 | 1799 | 0.6736 | 0.8486 | 0.8453 | 0.8458 | 0.8769 | |
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| 0.0981 | 8.0 | 2056 | 0.6476 | 0.8400 | 0.8605 | 0.8492 | 0.8788 | |
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| 0.0981 | 9.0 | 2313 | 0.6837 | 0.8443 | 0.8510 | 0.8469 | 0.8788 | |
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| 0.0713 | 10.0 | 2570 | 0.6943 | 0.8467 | 0.8562 | 0.8509 | 0.8793 | |
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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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