---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-ner
  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-base-uncased-ner

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: 0.1419
- Precision: 0.9526
- Recall: 0.9431
- F1: 0.9479
- Accuracy: 0.9434

## 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: 2e-05
- 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
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2866        | 0.14  | 500   | 0.1970          | 0.9329    | 0.9213 | 0.9271 | 0.9212   |
| 0.198         | 0.28  | 1000  | 0.1851          | 0.9412    | 0.9218 | 0.9314 | 0.9253   |
| 0.1892        | 0.43  | 1500  | 0.1772          | 0.9431    | 0.9250 | 0.9340 | 0.9280   |
| 0.179         | 0.57  | 2000  | 0.1697          | 0.9440    | 0.9296 | 0.9367 | 0.9313   |
| 0.1719        | 0.71  | 2500  | 0.1618          | 0.9453    | 0.9330 | 0.9391 | 0.9339   |
| 0.1718        | 0.85  | 3000  | 0.1587          | 0.9443    | 0.9351 | 0.9397 | 0.9351   |
| 0.1664        | 0.99  | 3500  | 0.1569          | 0.9486    | 0.9340 | 0.9412 | 0.9361   |
| 0.1504        | 1.14  | 4000  | 0.1566          | 0.9480    | 0.9356 | 0.9417 | 0.9368   |
| 0.1479        | 1.28  | 4500  | 0.1539          | 0.9492    | 0.9369 | 0.9430 | 0.9381   |
| 0.1467        | 1.42  | 5000  | 0.1501          | 0.9499    | 0.9383 | 0.9441 | 0.9391   |
| 0.1478        | 1.56  | 5500  | 0.1489          | 0.9513    | 0.9368 | 0.9440 | 0.9390   |
| 0.147         | 1.7   | 6000  | 0.1457          | 0.9503    | 0.9402 | 0.9452 | 0.9407   |
| 0.1453        | 1.85  | 6500  | 0.1447          | 0.9510    | 0.9408 | 0.9459 | 0.9412   |
| 0.1384        | 1.99  | 7000  | 0.1442          | 0.9521    | 0.9405 | 0.9463 | 0.9415   |
| 0.1325        | 2.13  | 7500  | 0.1446          | 0.9494    | 0.9441 | 0.9467 | 0.9425   |
| 0.13          | 2.27  | 8000  | 0.1467          | 0.9524    | 0.9403 | 0.9463 | 0.9416   |
| 0.1286        | 2.41  | 8500  | 0.1435          | 0.9501    | 0.9440 | 0.9470 | 0.9427   |
| 0.1311        | 2.56  | 9000  | 0.1446          | 0.9529    | 0.9417 | 0.9473 | 0.9427   |
| 0.1258        | 2.7   | 9500  | 0.1438          | 0.9528    | 0.9425 | 0.9476 | 0.9431   |
| 0.1257        | 2.84  | 10000 | 0.1437          | 0.9527    | 0.9431 | 0.9479 | 0.9434   |
| 0.1289        | 2.98  | 10500 | 0.1420          | 0.9526    | 0.9430 | 0.9478 | 0.9433   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0