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--- |
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base_model: aubmindlab/bert-base-arabertv02 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: arabert_baseline_relevance_task2_fold1 |
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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_baseline_relevance_task2_fold1 |
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This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1509 |
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- Qwk: 0.1747 |
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- Mse: 0.1416 |
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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 | Qwk | Mse | |
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|:-------------:|:------:|:----:|:---------------:|:-------:|:------:| |
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| No log | 0.3333 | 2 | 0.7029 | -0.0252 | 0.7093 | |
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| No log | 0.6667 | 4 | 0.1462 | -0.1951 | 0.1344 | |
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| No log | 1.0 | 6 | 0.2440 | 0.0 | 0.2429 | |
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| No log | 1.3333 | 8 | 0.1359 | 0.0 | 0.1325 | |
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| No log | 1.6667 | 10 | 0.1162 | 0.0219 | 0.1100 | |
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| No log | 2.0 | 12 | 0.1085 | 0.0345 | 0.1016 | |
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| No log | 2.3333 | 14 | 0.1197 | 0.0 | 0.1139 | |
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| No log | 2.6667 | 16 | 0.1465 | 0.0 | 0.1427 | |
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| No log | 3.0 | 18 | 0.1499 | 0.0 | 0.1462 | |
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| No log | 3.3333 | 20 | 0.1428 | 0.0 | 0.1389 | |
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| No log | 3.6667 | 22 | 0.1187 | 0.0483 | 0.1125 | |
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| No log | 4.0 | 24 | 0.1323 | 0.1217 | 0.1220 | |
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| No log | 4.3333 | 26 | 0.1627 | -0.1667 | 0.1498 | |
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| No log | 4.6667 | 28 | 0.1511 | 0.2075 | 0.1392 | |
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| No log | 5.0 | 30 | 0.1317 | 0.1217 | 0.1222 | |
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| No log | 5.3333 | 32 | 0.1331 | 0.0637 | 0.1256 | |
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| No log | 5.6667 | 34 | 0.1407 | 0.0105 | 0.1341 | |
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| No log | 6.0 | 36 | 0.1544 | 0.0105 | 0.1484 | |
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| No log | 6.3333 | 38 | 0.1639 | 0.0219 | 0.1582 | |
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| No log | 6.6667 | 40 | 0.1595 | 0.0483 | 0.1529 | |
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| No log | 7.0 | 42 | 0.1480 | 0.0808 | 0.1402 | |
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| No log | 7.3333 | 44 | 0.1440 | 0.1000 | 0.1358 | |
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| No log | 7.6667 | 46 | 0.1462 | 0.1000 | 0.1382 | |
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| No log | 8.0 | 48 | 0.1490 | 0.1747 | 0.1404 | |
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| No log | 8.3333 | 50 | 0.1535 | 0.1747 | 0.1441 | |
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| No log | 8.6667 | 52 | 0.1570 | 0.0094 | 0.1473 | |
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| No log | 9.0 | 54 | 0.1553 | 0.0094 | 0.1456 | |
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| No log | 9.3333 | 56 | 0.1529 | 0.1747 | 0.1436 | |
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| No log | 9.6667 | 58 | 0.1515 | 0.1747 | 0.1422 | |
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| No log | 10.0 | 60 | 0.1509 | 0.1747 | 0.1416 | |
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### Framework versions |
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- Transformers 4.44.0 |
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- Pytorch 2.4.0 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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