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--- |
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base_model: distilbert-base-uncased |
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library_name: peft |
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license: apache-2.0 |
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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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tags: |
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- generated_from_trainer |
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model-index: |
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- name: distilbert-ner-qlorafinetune-runs |
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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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# distilbert-ner-qlorafinetune-runs |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1707 |
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- Precision: 0.9584 |
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- Recall: 0.9495 |
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- F1: 0.9539 |
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- Accuracy: 0.9737 |
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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: 0.0004 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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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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- training_steps: 640 |
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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 | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 4.6043 | 0.0151 | 20 | 1.9157 | 0.0 | 0.0 | 0.0 | 0.6192 | |
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| 1.5758 | 0.0303 | 40 | 0.7695 | 0.7538 | 0.4952 | 0.5977 | 0.8411 | |
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| 0.391 | 0.0454 | 60 | 0.3487 | 0.8781 | 0.8772 | 0.8777 | 0.9548 | |
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| 0.2109 | 0.0605 | 80 | 0.2782 | 0.8970 | 0.9301 | 0.9133 | 0.9655 | |
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| 0.1793 | 0.0756 | 100 | 0.2435 | 0.9635 | 0.9318 | 0.9474 | 0.9664 | |
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| 0.1055 | 0.0908 | 120 | 0.2311 | 0.9614 | 0.9330 | 0.9470 | 0.9667 | |
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| 0.3157 | 0.1059 | 140 | 0.2210 | 0.9631 | 0.9333 | 0.9480 | 0.9677 | |
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| 0.1085 | 0.1210 | 160 | 0.2088 | 0.9336 | 0.9364 | 0.9350 | 0.9692 | |
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| 0.2085 | 0.1362 | 180 | 0.2044 | 0.9576 | 0.9351 | 0.9462 | 0.9695 | |
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| 0.3833 | 0.1513 | 200 | 0.1992 | 0.9478 | 0.9402 | 0.9440 | 0.9703 | |
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| 0.097 | 0.1664 | 220 | 0.1957 | 0.9482 | 0.9426 | 0.9454 | 0.9712 | |
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| 0.12 | 0.1815 | 240 | 0.1964 | 0.9541 | 0.9421 | 0.9481 | 0.9716 | |
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| 0.1696 | 0.1967 | 260 | 0.2697 | 0.9315 | 0.9444 | 0.9379 | 0.9718 | |
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| 0.1405 | 0.2118 | 280 | 0.1933 | 0.9691 | 0.9424 | 0.9555 | 0.9717 | |
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| 0.1992 | 0.2269 | 300 | 0.1887 | 0.9538 | 0.9444 | 0.9491 | 0.9722 | |
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| 0.0907 | 0.2421 | 320 | 0.1870 | 0.9629 | 0.9441 | 0.9534 | 0.9724 | |
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| 0.1778 | 0.2572 | 340 | 0.1852 | 0.9461 | 0.9471 | 0.9466 | 0.9730 | |
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| 0.1474 | 0.2723 | 360 | 0.1821 | 0.9467 | 0.9472 | 0.9469 | 0.9731 | |
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| 0.1972 | 0.2874 | 380 | 0.1798 | 0.9522 | 0.9472 | 0.9497 | 0.9731 | |
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| 0.1807 | 0.3026 | 400 | 0.1823 | 0.9646 | 0.9465 | 0.9555 | 0.9729 | |
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| 0.1388 | 0.3177 | 420 | 0.1771 | 0.9474 | 0.9491 | 0.9482 | 0.9733 | |
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| 0.1664 | 0.3328 | 440 | 0.1762 | 0.9655 | 0.9470 | 0.9562 | 0.9732 | |
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| 0.1559 | 0.3480 | 460 | 0.1747 | 0.9618 | 0.9482 | 0.9550 | 0.9733 | |
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| 0.233 | 0.3631 | 480 | 0.1750 | 0.9663 | 0.9484 | 0.9573 | 0.9733 | |
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| 0.1851 | 0.3782 | 500 | 0.1738 | 0.9616 | 0.9492 | 0.9554 | 0.9735 | |
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| 0.2512 | 0.3933 | 520 | 0.1725 | 0.9642 | 0.9491 | 0.9566 | 0.9736 | |
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| 0.0823 | 0.4085 | 540 | 0.1721 | 0.9624 | 0.9490 | 0.9556 | 0.9736 | |
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| 0.0865 | 0.4236 | 560 | 0.1717 | 0.9624 | 0.9491 | 0.9557 | 0.9737 | |
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| 0.0611 | 0.4387 | 580 | 0.1714 | 0.9607 | 0.9493 | 0.9550 | 0.9738 | |
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| 0.105 | 0.4539 | 600 | 0.1710 | 0.9612 | 0.9493 | 0.9552 | 0.9737 | |
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| 0.1192 | 0.4690 | 620 | 0.1709 | 0.9584 | 0.9495 | 0.9540 | 0.9737 | |
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| 0.1923 | 0.4841 | 640 | 0.1707 | 0.9584 | 0.9495 | 0.9539 | 0.9737 | |
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### Framework versions |
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- PEFT 0.12.0 |
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- Transformers 4.43.3 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
