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--- |
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license: apache-2.0 |
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base_model: bert-base-uncased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: bert-base-uncased-finetuned-toxic-comment-detection-ws23 |
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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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# bert-base-uncased-finetuned-toxic-comment-detection-ws23 |
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1991 |
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- Accuracy: 0.945 |
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- Precision: 0.7273 |
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- Recall: 0.7619 |
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- F1: 0.7442 |
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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: 2e-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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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 8 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 0.4756 | 1.0 | 50 | 0.2585 | 0.91 | 1.0 | 0.1429 | 0.25 | |
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| 0.1843 | 2.0 | 100 | 0.1417 | 0.93 | 0.7333 | 0.5238 | 0.6111 | |
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| 0.1014 | 3.0 | 150 | 0.2207 | 0.935 | 0.9 | 0.4286 | 0.5806 | |
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| 0.0481 | 4.0 | 200 | 0.1991 | 0.945 | 0.7273 | 0.7619 | 0.7442 | |
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| 0.0105 | 5.0 | 250 | 0.2082 | 0.945 | 0.75 | 0.7143 | 0.7317 | |
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| 0.0028 | 6.0 | 300 | 0.2249 | 0.945 | 0.75 | 0.7143 | 0.7317 | |
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| 0.0017 | 7.0 | 350 | 0.2379 | 0.945 | 0.7273 | 0.7619 | 0.7442 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu118 |
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- Tokenizers 0.15.0 |
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