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---
base_model: distilbert-base-uncased
library_name: peft
license: apache-2.0
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: distilbert-ner-qlorafinetune-runs
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-ner-qlorafinetune-runs
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1707
- Precision: 0.9584
- Recall: 0.9495
- F1: 0.9539
- Accuracy: 0.9737
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0004
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 640
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 4.6043 | 0.0151 | 20 | 1.9157 | 0.0 | 0.0 | 0.0 | 0.6192 |
| 1.5758 | 0.0303 | 40 | 0.7695 | 0.7538 | 0.4952 | 0.5977 | 0.8411 |
| 0.391 | 0.0454 | 60 | 0.3487 | 0.8781 | 0.8772 | 0.8777 | 0.9548 |
| 0.2109 | 0.0605 | 80 | 0.2782 | 0.8970 | 0.9301 | 0.9133 | 0.9655 |
| 0.1793 | 0.0756 | 100 | 0.2435 | 0.9635 | 0.9318 | 0.9474 | 0.9664 |
| 0.1055 | 0.0908 | 120 | 0.2311 | 0.9614 | 0.9330 | 0.9470 | 0.9667 |
| 0.3157 | 0.1059 | 140 | 0.2210 | 0.9631 | 0.9333 | 0.9480 | 0.9677 |
| 0.1085 | 0.1210 | 160 | 0.2088 | 0.9336 | 0.9364 | 0.9350 | 0.9692 |
| 0.2085 | 0.1362 | 180 | 0.2044 | 0.9576 | 0.9351 | 0.9462 | 0.9695 |
| 0.3833 | 0.1513 | 200 | 0.1992 | 0.9478 | 0.9402 | 0.9440 | 0.9703 |
| 0.097 | 0.1664 | 220 | 0.1957 | 0.9482 | 0.9426 | 0.9454 | 0.9712 |
| 0.12 | 0.1815 | 240 | 0.1964 | 0.9541 | 0.9421 | 0.9481 | 0.9716 |
| 0.1696 | 0.1967 | 260 | 0.2697 | 0.9315 | 0.9444 | 0.9379 | 0.9718 |
| 0.1405 | 0.2118 | 280 | 0.1933 | 0.9691 | 0.9424 | 0.9555 | 0.9717 |
| 0.1992 | 0.2269 | 300 | 0.1887 | 0.9538 | 0.9444 | 0.9491 | 0.9722 |
| 0.0907 | 0.2421 | 320 | 0.1870 | 0.9629 | 0.9441 | 0.9534 | 0.9724 |
| 0.1778 | 0.2572 | 340 | 0.1852 | 0.9461 | 0.9471 | 0.9466 | 0.9730 |
| 0.1474 | 0.2723 | 360 | 0.1821 | 0.9467 | 0.9472 | 0.9469 | 0.9731 |
| 0.1972 | 0.2874 | 380 | 0.1798 | 0.9522 | 0.9472 | 0.9497 | 0.9731 |
| 0.1807 | 0.3026 | 400 | 0.1823 | 0.9646 | 0.9465 | 0.9555 | 0.9729 |
| 0.1388 | 0.3177 | 420 | 0.1771 | 0.9474 | 0.9491 | 0.9482 | 0.9733 |
| 0.1664 | 0.3328 | 440 | 0.1762 | 0.9655 | 0.9470 | 0.9562 | 0.9732 |
| 0.1559 | 0.3480 | 460 | 0.1747 | 0.9618 | 0.9482 | 0.9550 | 0.9733 |
| 0.233 | 0.3631 | 480 | 0.1750 | 0.9663 | 0.9484 | 0.9573 | 0.9733 |
| 0.1851 | 0.3782 | 500 | 0.1738 | 0.9616 | 0.9492 | 0.9554 | 0.9735 |
| 0.2512 | 0.3933 | 520 | 0.1725 | 0.9642 | 0.9491 | 0.9566 | 0.9736 |
| 0.0823 | 0.4085 | 540 | 0.1721 | 0.9624 | 0.9490 | 0.9556 | 0.9736 |
| 0.0865 | 0.4236 | 560 | 0.1717 | 0.9624 | 0.9491 | 0.9557 | 0.9737 |
| 0.0611 | 0.4387 | 580 | 0.1714 | 0.9607 | 0.9493 | 0.9550 | 0.9738 |
| 0.105 | 0.4539 | 600 | 0.1710 | 0.9612 | 0.9493 | 0.9552 | 0.9737 |
| 0.1192 | 0.4690 | 620 | 0.1709 | 0.9584 | 0.9495 | 0.9540 | 0.9737 |
| 0.1923 | 0.4841 | 640 | 0.1707 | 0.9584 | 0.9495 | 0.9539 | 0.9737 |
### Framework versions
- PEFT 0.12.0
- Transformers 4.43.3
- Pytorch 2.4.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1 |