---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-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-finetuned-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: 0.1877
- Precision: 0.9674
- Recall: 0.9649
- F1: 0.9662
- Accuracy: 0.9754

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1465        | 1.0   | 4367  | 0.1766          | 0.9258    | 0.9374 | 0.9316 | 0.9498   |
| 0.0851        | 2.0   | 8734  | 0.1438          | 0.9421    | 0.9499 | 0.9460 | 0.9635   |
| 0.0474        | 3.0   | 13101 | 0.1626          | 0.9507    | 0.9499 | 0.9503 | 0.9695   |
| 0.0316        | 4.0   | 17468 | 0.1539          | 0.9535    | 0.9591 | 0.9563 | 0.9693   |
| 0.0197        | 5.0   | 21835 | 0.1689          | 0.9568    | 0.9616 | 0.9592 | 0.9664   |
| 0.0143        | 6.0   | 26202 | 0.1967          | 0.9632    | 0.9608 | 0.9620 | 0.9689   |
| 0.0111        | 7.0   | 30569 | 0.1872          | 0.9552    | 0.9608 | 0.9580 | 0.9677   |
| 0.0158        | 8.0   | 34936 | 0.1921          | 0.9619    | 0.9683 | 0.9651 | 0.9733   |
| 0.0066        | 9.0   | 39303 | 0.2145          | 0.9594    | 0.9674 | 0.9634 | 0.9704   |
| 0.0056        | 10.0  | 43670 | 0.1856          | 0.9617    | 0.9633 | 0.9625 | 0.9738   |
| 0.0031        | 11.0  | 48037 | 0.1561          | 0.9657    | 0.9649 | 0.9653 | 0.9792   |
| 0.0029        | 12.0  | 52404 | 0.1850          | 0.9683    | 0.9683 | 0.9683 | 0.9788   |
| 0.0034        | 13.0  | 56771 | 0.2072          | 0.9640    | 0.9616 | 0.9628 | 0.9723   |
| 0.0015        | 14.0  | 61138 | 0.2165          | 0.9641    | 0.9641 | 0.9641 | 0.9719   |
| 0.0011        | 15.0  | 65505 | 0.1877          | 0.9674    | 0.9649 | 0.9662 | 0.9754   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.3