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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- bleu |
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base_model: distilbert/distilgpt2 |
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model-index: |
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- name: distilgpt2-finetuned |
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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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# distilgpt2-finetuned |
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This model is a fine-tuned version of [distilbert/distilgpt2](https://huggingface.co/distilbert/distilgpt2) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.7322 |
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- Bleu: 0.0145 |
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- Bertscore Precision: 0.1505 |
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- Bertscore Recall: 0.1674 |
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- Bertscore F1: 0.1581 |
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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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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 16 |
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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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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Bleu | Bertscore Precision | Bertscore Recall | Bertscore F1 | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------------------:|:----------------:|:------------:| |
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| 5.0087 | 1.0 | 3223 | 3.9456 | 0.0088 | 0.1478 | 0.1638 | 0.1551 | |
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| 4.8889 | 2.0 | 6446 | 3.7706 | 0.0093 | 0.1480 | 0.1642 | 0.1554 | |
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| 4.9152 | 3.0 | 9669 | 3.6252 | 0.0097 | 0.1483 | 0.1646 | 0.1557 | |
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| 4.647 | 4.0 | 12892 | 3.5105 | 0.0103 | 0.1486 | 0.1649 | 0.1560 | |
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| 4.4683 | 5.0 | 16115 | 3.4093 | 0.0108 | 0.1489 | 0.1652 | 0.1563 | |
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| 4.4007 | 6.0 | 19338 | 3.3225 | 0.0110 | 0.1491 | 0.1654 | 0.1565 | |
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| 4.3966 | 7.0 | 22561 | 3.2444 | 0.0115 | 0.1493 | 0.1656 | 0.1567 | |
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| 4.3414 | 8.0 | 25784 | 3.1662 | 0.0117 | 0.1494 | 0.1657 | 0.1568 | |
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| 4.2446 | 9.0 | 29007 | 3.1021 | 0.0122 | 0.1497 | 0.1660 | 0.1571 | |
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| 4.2464 | 10.0 | 32230 | 3.0384 | 0.0125 | 0.1499 | 0.1662 | 0.1573 | |
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| 4.1739 | 11.0 | 35453 | 2.9789 | 0.0128 | 0.1499 | 0.1665 | 0.1574 | |
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| 4.08 | 12.0 | 38676 | 2.9295 | 0.0131 | 0.1501 | 0.1666 | 0.1576 | |
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| 4.001 | 13.0 | 41899 | 2.8857 | 0.0135 | 0.1502 | 0.1668 | 0.1577 | |
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| 3.9277 | 14.0 | 45122 | 2.8464 | 0.0136 | 0.1502 | 0.1669 | 0.1578 | |
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| 3.9709 | 15.0 | 48345 | 2.8137 | 0.0139 | 0.1503 | 0.1670 | 0.1578 | |
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| 3.9192 | 16.0 | 51568 | 2.7872 | 0.0141 | 0.1503 | 0.1672 | 0.1579 | |
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| 3.8916 | 17.0 | 54791 | 2.7644 | 0.0143 | 0.1504 | 0.1673 | 0.1580 | |
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| 3.8489 | 18.0 | 58014 | 2.7475 | 0.0144 | 0.1505 | 0.1674 | 0.1581 | |
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| 3.9091 | 19.0 | 61237 | 2.7364 | 0.0145 | 0.1505 | 0.1674 | 0.1581 | |
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| 3.9271 | 20.0 | 64460 | 2.7322 | 0.0145 | 0.1505 | 0.1674 | 0.1581 | |
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### Framework versions |
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- Transformers 4.40.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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