---
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