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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: google-bert/bert-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: VF_BERT_ST_1800 |
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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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# VF_BERT_ST_1800 |
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This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1814 |
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- Precision: 0.8104 |
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- Recall: 0.8406 |
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- F1: 0.8252 |
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- Accuracy: 0.9657 |
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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: 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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| 0.2052 | 1.0 | 569 | 0.1207 | 0.7731 | 0.8082 | 0.7903 | 0.9622 | |
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| 0.0774 | 2.0 | 1138 | 0.1369 | 0.8062 | 0.7998 | 0.8030 | 0.9629 | |
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| 0.0507 | 3.0 | 1707 | 0.1351 | 0.8127 | 0.8386 | 0.8254 | 0.9654 | |
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| 0.0328 | 4.0 | 2276 | 0.1331 | 0.8005 | 0.8414 | 0.8204 | 0.9658 | |
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| 0.0221 | 5.0 | 2845 | 0.1398 | 0.8144 | 0.8429 | 0.8284 | 0.9668 | |
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| 0.0157 | 6.0 | 3414 | 0.1481 | 0.8137 | 0.8401 | 0.8267 | 0.9671 | |
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| 0.0117 | 7.0 | 3983 | 0.1804 | 0.8110 | 0.8439 | 0.8271 | 0.9650 | |
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| 0.0062 | 8.0 | 4552 | 0.1731 | 0.8133 | 0.8434 | 0.8281 | 0.9658 | |
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| 0.005 | 9.0 | 5121 | 0.1835 | 0.8100 | 0.8416 | 0.8255 | 0.9660 | |
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| 0.0043 | 10.0 | 5690 | 0.1814 | 0.8104 | 0.8406 | 0.8252 | 0.9657 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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