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--- |
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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-base-arabert-BioNER-EN-AR |
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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-arabert-BioNER-EN-AR |
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This model is a fine-tuned version of [StivenLancheros/bert-base-arabert-BioNER-EN](https://huggingface.co/StivenLancheros/bert-base-arabert-BioNER-EN) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4250 |
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- Precision: 0.7143 |
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- Recall: 0.8209 |
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- F1: 0.7639 |
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- Accuracy: 0.9197 |
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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: 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: 20 |
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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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| 0.6376 | 1.0 | 680 | 0.7457 | 0.4379 | 0.6384 | 0.5195 | 0.8242 | |
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| 0.4549 | 2.0 | 1360 | 0.7120 | 0.4878 | 0.7113 | 0.5787 | 0.8346 | |
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| 0.3214 | 3.0 | 2040 | 0.5576 | 0.5676 | 0.7529 | 0.6473 | 0.8749 | |
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| 0.2883 | 4.0 | 2720 | 0.5304 | 0.5916 | 0.7745 | 0.6708 | 0.8808 | |
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| 0.2596 | 5.0 | 3400 | 0.4942 | 0.6117 | 0.7884 | 0.6889 | 0.8906 | |
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| 0.2168 | 6.0 | 4080 | 0.5229 | 0.6204 | 0.7977 | 0.6979 | 0.8898 | |
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| 0.2105 | 7.0 | 4760 | 0.4630 | 0.6501 | 0.7935 | 0.7147 | 0.8999 | |
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| 0.1889 | 8.0 | 5440 | 0.5048 | 0.6407 | 0.8066 | 0.7141 | 0.8958 | |
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| 0.1714 | 9.0 | 6120 | 0.4538 | 0.6909 | 0.7986 | 0.7409 | 0.9105 | |
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| 0.1626 | 10.0 | 6800 | 0.4433 | 0.6912 | 0.8070 | 0.7446 | 0.9130 | |
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| 0.1559 | 11.0 | 7480 | 0.4282 | 0.7006 | 0.8054 | 0.7493 | 0.9144 | |
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| 0.1451 | 12.0 | 8160 | 0.4475 | 0.6978 | 0.8150 | 0.7519 | 0.9135 | |
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| 0.1384 | 13.0 | 8840 | 0.4535 | 0.6928 | 0.8215 | 0.7517 | 0.9145 | |
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| 0.1331 | 14.0 | 9520 | 0.4250 | 0.7143 | 0.8209 | 0.7639 | 0.9197 | |
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| 0.1282 | 15.0 | 10200 | 0.4350 | 0.7108 | 0.8237 | 0.7631 | 0.9200 | |
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| 0.1216 | 16.0 | 10880 | 0.4385 | 0.7096 | 0.8231 | 0.7621 | 0.9188 | |
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| 0.1195 | 17.0 | 11560 | 0.4376 | 0.7134 | 0.8275 | 0.7662 | 0.9204 | |
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| 0.1187 | 18.0 | 12240 | 0.4461 | 0.7092 | 0.8297 | 0.7647 | 0.9183 | |
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| 0.1159 | 19.0 | 12920 | 0.4359 | 0.7215 | 0.8264 | 0.7704 | 0.9219 | |
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| 0.1121 | 20.0 | 13600 | 0.4358 | 0.7198 | 0.8264 | 0.7694 | 0.9217 | |
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### Framework versions |
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- Transformers 4.27.2 |
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- Pytorch 1.13.0+cu117 |
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- Datasets 2.7.1 |
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- Tokenizers 0.13.2 |
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