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--- |
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library_name: transformers |
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license: mit |
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base_model: openai-community/gpt2-large |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: gpt2-large-countdown |
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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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# gpt2-large-countdown |
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This model is a fine-tuned version of [openai-community/gpt2-large](https://huggingface.co/openai-community/gpt2-large) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1586 |
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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: 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 5 |
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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 | |
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|:-------------:|:------:|:-----:|:---------------:| |
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| 0.241 | 0.0533 | 500 | 0.2299 | |
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| 0.2104 | 0.1067 | 1000 | 0.2008 | |
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| 0.1994 | 0.16 | 1500 | 0.1927 | |
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| 0.1927 | 0.2133 | 2000 | 0.1869 | |
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| 0.1894 | 0.2667 | 2500 | 0.1828 | |
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| 0.1847 | 0.32 | 3000 | 0.1803 | |
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| 0.1825 | 0.3733 | 3500 | 0.1785 | |
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| 0.1803 | 0.4267 | 4000 | 0.1761 | |
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| 0.1781 | 0.48 | 4500 | 0.1748 | |
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| 0.1768 | 0.5333 | 5000 | 0.1730 | |
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| 0.1764 | 0.5867 | 5500 | 0.1721 | |
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| 0.1745 | 0.64 | 6000 | 0.1712 | |
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| 0.1727 | 0.6933 | 6500 | 0.1708 | |
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| 0.1719 | 0.7467 | 7000 | 0.1690 | |
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| 0.171 | 0.8 | 7500 | 0.1691 | |
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| 0.1704 | 0.8533 | 8000 | 0.1679 | |
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| 0.1697 | 0.9067 | 8500 | 0.1677 | |
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| 0.1696 | 0.96 | 9000 | 0.1670 | |
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| 0.1663 | 1.0133 | 9500 | 0.1665 | |
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| 0.1663 | 1.0667 | 10000 | 0.1665 | |
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| 0.1667 | 1.12 | 10500 | 0.1663 | |
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| 0.1661 | 1.1733 | 11000 | 0.1657 | |
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| 0.166 | 1.2267 | 11500 | 0.1656 | |
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| 0.1655 | 1.28 | 12000 | 0.1651 | |
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| 0.1645 | 1.3333 | 12500 | 0.1649 | |
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| 0.1645 | 1.3867 | 13000 | 0.1646 | |
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| 0.1642 | 1.44 | 13500 | 0.1642 | |
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| 0.1642 | 1.4933 | 14000 | 0.1637 | |
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| 0.1641 | 1.5467 | 14500 | 0.1639 | |
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| 0.1635 | 1.6 | 15000 | 0.1635 | |
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| 0.1634 | 1.6533 | 15500 | 0.1631 | |
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| 0.1637 | 1.7067 | 16000 | 0.1629 | |
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| 0.1636 | 1.76 | 16500 | 0.1630 | |
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| 0.1628 | 1.8133 | 17000 | 0.1627 | |
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| 0.1623 | 1.8667 | 17500 | 0.1624 | |
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| 0.1623 | 1.92 | 18000 | 0.1620 | |
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| 0.1621 | 1.9733 | 18500 | 0.1621 | |
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| 0.1596 | 2.0267 | 19000 | 0.1619 | |
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| 0.1597 | 2.08 | 19500 | 0.1619 | |
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| 0.159 | 2.1333 | 20000 | 0.1618 | |
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| 0.1594 | 2.1867 | 20500 | 0.1616 | |
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| 0.1591 | 2.24 | 21000 | 0.1615 | |
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| 0.1595 | 2.2933 | 21500 | 0.1613 | |
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| 0.1593 | 2.3467 | 22000 | 0.1611 | |
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| 0.1591 | 2.4 | 22500 | 0.1612 | |
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| 0.1591 | 2.4533 | 23000 | 0.1609 | |
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| 0.159 | 2.5067 | 23500 | 0.1607 | |
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| 0.1586 | 2.56 | 24000 | 0.1606 | |
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| 0.1592 | 2.6133 | 24500 | 0.1607 | |
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| 0.1581 | 2.6667 | 25000 | 0.1604 | |
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| 0.1586 | 2.7200 | 25500 | 0.1601 | |
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| 0.1584 | 2.7733 | 26000 | 0.1602 | |
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| 0.1581 | 2.8267 | 26500 | 0.1600 | |
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| 0.1579 | 2.88 | 27000 | 0.1599 | |
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| 0.1584 | 2.9333 | 27500 | 0.1598 | |
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| 0.1581 | 2.9867 | 28000 | 0.1597 | |
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| 0.1553 | 3.04 | 28500 | 0.1601 | |
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| 0.1554 | 3.0933 | 29000 | 0.1599 | |
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| 0.155 | 3.1467 | 29500 | 0.1601 | |
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| 0.1551 | 3.2 | 30000 | 0.1600 | |
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| 0.1554 | 3.2533 | 30500 | 0.1597 | |
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| 0.1549 | 3.3067 | 31000 | 0.1597 | |
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| 0.1549 | 3.36 | 31500 | 0.1596 | |
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| 0.1548 | 3.4133 | 32000 | 0.1597 | |
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| 0.1545 | 3.4667 | 32500 | 0.1594 | |
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| 0.1548 | 3.52 | 33000 | 0.1595 | |
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| 0.1549 | 3.5733 | 33500 | 0.1593 | |
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| 0.1544 | 3.6267 | 34000 | 0.1592 | |
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| 0.1551 | 3.68 | 34500 | 0.1592 | |
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| 0.1549 | 3.7333 | 35000 | 0.1591 | |
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| 0.1547 | 3.7867 | 35500 | 0.1590 | |
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| 0.1544 | 3.84 | 36000 | 0.1588 | |
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| 0.1545 | 3.8933 | 36500 | 0.1587 | |
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| 0.1547 | 3.9467 | 37000 | 0.1588 | |
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| 0.1549 | 4.0 | 37500 | 0.1588 | |
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| 0.1519 | 4.0533 | 38000 | 0.1591 | |
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| 0.1514 | 4.1067 | 38500 | 0.1592 | |
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| 0.1516 | 4.16 | 39000 | 0.1593 | |
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| 0.1518 | 4.2133 | 39500 | 0.1592 | |
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| 0.1514 | 4.2667 | 40000 | 0.1591 | |
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| 0.1516 | 4.32 | 40500 | 0.1591 | |
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| 0.1514 | 4.3733 | 41000 | 0.1590 | |
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| 0.152 | 4.4267 | 41500 | 0.1589 | |
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| 0.1512 | 4.48 | 42000 | 0.1589 | |
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| 0.152 | 4.5333 | 42500 | 0.1588 | |
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| 0.1511 | 4.5867 | 43000 | 0.1588 | |
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| 0.1511 | 4.64 | 43500 | 0.1588 | |
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| 0.1514 | 4.6933 | 44000 | 0.1588 | |
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| 0.1513 | 4.7467 | 44500 | 0.1586 | |
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| 0.1511 | 4.8 | 45000 | 0.1586 | |
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| 0.1513 | 4.8533 | 45500 | 0.1586 | |
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| 0.1514 | 4.9067 | 46000 | 0.1586 | |
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| 0.1511 | 4.96 | 46500 | 0.1586 | |
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### Framework versions |
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- Transformers 4.51.1 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.5.0 |
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- Tokenizers 0.21.1 |
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