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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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- accuracy |
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- f1 |
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model-index: |
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- name: categorization-finetuned-20220721-164940-distilled-20220811-074207 |
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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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# categorization-finetuned-20220721-164940-distilled-20220811-074207 |
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This model is a fine-tuned version of [carted-nlp/categorization-finetuned-20220721-164940](https://huggingface.co/carted-nlp/categorization-finetuned-20220721-164940) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1499 |
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- Accuracy: 0.8771 |
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- F1: 0.8763 |
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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: 64 |
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- eval_batch_size: 96 |
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- seed: 314 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 256 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 1500 |
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- num_epochs: 30.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
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|:-------------:|:-----:|:------:|:---------------:|:--------:|:------:| |
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| 0.5644 | 0.56 | 2500 | 0.2739 | 0.7822 | 0.7774 | |
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| 0.2658 | 1.12 | 5000 | 0.2288 | 0.8159 | 0.8127 | |
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| 0.2307 | 1.69 | 7500 | 0.2082 | 0.8298 | 0.8273 | |
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| 0.2126 | 2.25 | 10000 | 0.1970 | 0.8389 | 0.8370 | |
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| 0.2012 | 2.81 | 12500 | 0.1888 | 0.8450 | 0.8433 | |
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| 0.1903 | 3.37 | 15000 | 0.1829 | 0.8496 | 0.8485 | |
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| 0.1846 | 3.94 | 17500 | 0.1783 | 0.8529 | 0.8511 | |
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| 0.1771 | 4.5 | 20000 | 0.1750 | 0.8548 | 0.8537 | |
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| 0.1726 | 5.06 | 22500 | 0.1727 | 0.8577 | 0.8564 | |
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| 0.1673 | 5.62 | 25000 | 0.1683 | 0.8602 | 0.8591 | |
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| 0.1648 | 6.19 | 27500 | 0.1675 | 0.8608 | 0.8597 | |
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| 0.1596 | 6.75 | 30000 | 0.1657 | 0.8630 | 0.8620 | |
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| 0.1563 | 7.31 | 32500 | 0.1635 | 0.8646 | 0.8639 | |
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| 0.154 | 7.87 | 35000 | 0.1613 | 0.8656 | 0.8647 | |
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| 0.1496 | 8.43 | 37500 | 0.1611 | 0.8666 | 0.8656 | |
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| 0.1496 | 9.0 | 40000 | 0.1598 | 0.8676 | 0.8669 | |
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| 0.1445 | 9.56 | 42500 | 0.1594 | 0.8681 | 0.8671 | |
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| 0.1435 | 10.12 | 45000 | 0.1588 | 0.8688 | 0.8679 | |
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| 0.1407 | 10.68 | 47500 | 0.1568 | 0.8703 | 0.8695 | |
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| 0.1382 | 11.25 | 50000 | 0.1564 | 0.8708 | 0.8700 | |
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| 0.1372 | 11.81 | 52500 | 0.1550 | 0.8720 | 0.8713 | |
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| 0.1344 | 12.37 | 55000 | 0.1559 | 0.8718 | 0.8708 | |
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| 0.1337 | 12.93 | 57500 | 0.1540 | 0.8735 | 0.8729 | |
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| 0.1303 | 13.5 | 60000 | 0.1541 | 0.8729 | 0.8721 | |
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| 0.1304 | 14.06 | 62500 | 0.1531 | 0.8735 | 0.8727 | |
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| 0.1274 | 14.62 | 65000 | 0.1535 | 0.8736 | 0.8727 | |
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| 0.1266 | 15.18 | 67500 | 0.1527 | 0.8750 | 0.8742 | |
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| 0.1251 | 15.74 | 70000 | 0.1525 | 0.8755 | 0.8748 | |
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| 0.1234 | 16.31 | 72500 | 0.1528 | 0.8753 | 0.8745 | |
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| 0.1229 | 16.87 | 75000 | 0.1516 | 0.8760 | 0.8753 | |
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| 0.121 | 17.43 | 77500 | 0.1523 | 0.8759 | 0.8752 | |
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| 0.1212 | 17.99 | 80000 | 0.1515 | 0.8760 | 0.8754 | |
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| 0.1185 | 18.56 | 82500 | 0.1514 | 0.8765 | 0.8757 | |
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| 0.1186 | 19.12 | 85000 | 0.1516 | 0.8766 | 0.8760 | |
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| 0.1172 | 19.68 | 87500 | 0.1506 | 0.8774 | 0.8767 | |
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| 0.1164 | 20.24 | 90000 | 0.1513 | 0.8770 | 0.8763 | |
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| 0.116 | 20.81 | 92500 | 0.1507 | 0.8774 | 0.8767 | |
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| 0.1145 | 21.37 | 95000 | 0.1507 | 0.8777 | 0.8770 | |
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| 0.1143 | 21.93 | 97500 | 0.1506 | 0.8776 | 0.8770 | |
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| 0.1131 | 22.49 | 100000 | 0.1507 | 0.8779 | 0.8772 | |
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| 0.1131 | 23.05 | 102500 | 0.1505 | 0.8779 | 0.8772 | |
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| 0.1123 | 23.62 | 105000 | 0.1506 | 0.8781 | 0.8774 | |
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| 0.1117 | 24.18 | 107500 | 0.1504 | 0.8783 | 0.8776 | |
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| 0.1118 | 24.74 | 110000 | 0.1503 | 0.8784 | 0.8777 | |
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| 0.1111 | 25.3 | 112500 | 0.1503 | 0.8783 | 0.8776 | |
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| 0.1111 | 25.87 | 115000 | 0.1502 | 0.8784 | 0.8777 | |
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| 0.1105 | 26.43 | 117500 | 0.1504 | 0.8783 | 0.8776 | |
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| 0.1105 | 26.99 | 120000 | 0.1502 | 0.8786 | 0.8779 | |
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| 0.1104 | 27.55 | 122500 | 0.1503 | 0.8786 | 0.8779 | |
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| 0.1096 | 28.12 | 125000 | 0.1502 | 0.8785 | 0.8779 | |
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| 0.1101 | 28.68 | 127500 | 0.1501 | 0.8786 | 0.8779 | |
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| 0.1101 | 29.24 | 130000 | 0.1502 | 0.8786 | 0.8779 | |
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| 0.1094 | 29.8 | 132500 | 0.1501 | 0.8786 | 0.8779 | |
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### Framework versions |
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- Transformers 4.17.0 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.3.2 |
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- Tokenizers 0.11.6 |
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