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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: BERT_ST_DA_1000 |
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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_ST_DA_1000 |
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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: 0.1864 |
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- Precision: 0.9653 |
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- Recall: 0.9736 |
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- F1: 0.9694 |
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- Accuracy: 0.9655 |
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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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- 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 | 495 | 0.1375 | 0.9614 | 0.9662 | 0.9638 | 0.9590 | |
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| 0.2452 | 2.0 | 990 | 0.1262 | 0.9652 | 0.9705 | 0.9679 | 0.9632 | |
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| 0.0853 | 3.0 | 1485 | 0.1396 | 0.9638 | 0.9677 | 0.9657 | 0.9619 | |
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| 0.0479 | 4.0 | 1980 | 0.1485 | 0.9637 | 0.9729 | 0.9683 | 0.9651 | |
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| 0.0275 | 5.0 | 2475 | 0.1641 | 0.9633 | 0.9727 | 0.9680 | 0.9642 | |
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| 0.0181 | 6.0 | 2970 | 0.1753 | 0.9641 | 0.9739 | 0.9689 | 0.9655 | |
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| 0.0112 | 7.0 | 3465 | 0.1675 | 0.9659 | 0.9724 | 0.9691 | 0.9657 | |
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| 0.0074 | 8.0 | 3960 | 0.1817 | 0.9650 | 0.9745 | 0.9697 | 0.9663 | |
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| 0.0054 | 9.0 | 4455 | 0.1878 | 0.9652 | 0.9737 | 0.9694 | 0.9657 | |
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| 0.0038 | 10.0 | 4950 | 0.1864 | 0.9653 | 0.9736 | 0.9694 | 0.9655 | |
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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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