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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: AraBERT_token_classification__AraEval24_truncated_rand |
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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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# AraBERT_token_classification__AraEval24_truncated_rand |
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This model is a fine-tuned version of [aubmindlab/bert-base-arabert](https://huggingface.co/aubmindlab/bert-base-arabert) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.0021 |
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- Precision: 0.1263 |
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- Recall: 0.1171 |
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- F1: 0.1215 |
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- Accuracy: 0.5544 |
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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: 8 |
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- eval_batch_size: 8 |
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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 | 479 | 1.7309 | 0.0864 | 0.0189 | 0.0310 | 0.5847 | |
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| 1.6653 | 2.0 | 958 | 1.6503 | 0.0855 | 0.0393 | 0.0539 | 0.5793 | |
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| 1.3608 | 3.0 | 1437 | 1.6761 | 0.1075 | 0.0579 | 0.0753 | 0.5869 | |
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| 1.1267 | 4.0 | 1916 | 1.7633 | 0.1003 | 0.0786 | 0.0882 | 0.5442 | |
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| 0.9119 | 5.0 | 2395 | 1.7995 | 0.1050 | 0.0877 | 0.0956 | 0.5442 | |
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| 0.783 | 6.0 | 2874 | 1.8613 | 0.1151 | 0.0937 | 0.1033 | 0.5607 | |
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| 0.6667 | 7.0 | 3353 | 1.9148 | 0.1155 | 0.1061 | 0.1106 | 0.5472 | |
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| 0.5967 | 8.0 | 3832 | 1.9480 | 0.1267 | 0.1175 | 0.1219 | 0.5511 | |
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| 0.5397 | 9.0 | 4311 | 1.9909 | 0.1235 | 0.1126 | 0.1178 | 0.5487 | |
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| 0.4948 | 10.0 | 4790 | 2.0021 | 0.1263 | 0.1171 | 0.1215 | 0.5544 | |
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### Framework versions |
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- Transformers 4.30.2 |
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- Pytorch 1.12.1 |
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- Datasets 2.13.2 |
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- Tokenizers 0.13.3 |
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