---
license: apache-2.0
base_model: distilbert-base-uncased
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: 0.4181
- Precision: 0.6106
- Recall: 0.6615
- F1: 0.635
- Accuracy: 0.9189

## 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: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 23   | 0.3610          | 0.4795    | 0.6094 | 0.5367 | 0.9045   |
| No log        | 2.0   | 46   | 0.3516          | 0.5330    | 0.5885 | 0.5594 | 0.9141   |
| No log        | 3.0   | 69   | 0.3591          | 0.5346    | 0.6042 | 0.5672 | 0.9147   |
| No log        | 4.0   | 92   | 0.3602          | 0.5226    | 0.6615 | 0.5839 | 0.9129   |
| No log        | 5.0   | 115  | 0.3706          | 0.5315    | 0.6146 | 0.5700 | 0.9123   |
| No log        | 6.0   | 138  | 0.3652          | 0.5631    | 0.6042 | 0.5829 | 0.9165   |
| No log        | 7.0   | 161  | 0.3618          | 0.5640    | 0.6198 | 0.5906 | 0.9153   |
| No log        | 8.0   | 184  | 0.3680          | 0.5755    | 0.6354 | 0.6040 | 0.9165   |
| No log        | 9.0   | 207  | 0.3782          | 0.5789    | 0.6302 | 0.6035 | 0.9183   |
| No log        | 10.0  | 230  | 0.3926          | 0.6020    | 0.6302 | 0.6158 | 0.9189   |
| No log        | 11.0  | 253  | 0.3816          | 0.5845    | 0.6667 | 0.6229 | 0.9171   |
| No log        | 12.0  | 276  | 0.3811          | 0.5942    | 0.6406 | 0.6165 | 0.9195   |
| No log        | 13.0  | 299  | 0.3857          | 0.5885    | 0.6406 | 0.6135 | 0.9189   |
| No log        | 14.0  | 322  | 0.3966          | 0.5714    | 0.6458 | 0.6064 | 0.9141   |
| No log        | 15.0  | 345  | 0.3927          | 0.6019    | 0.6615 | 0.6303 | 0.9183   |
| No log        | 16.0  | 368  | 0.3955          | 0.5907    | 0.6615 | 0.6241 | 0.9165   |
| No log        | 17.0  | 391  | 0.4124          | 0.5931    | 0.6302 | 0.6111 | 0.9171   |
| No log        | 18.0  | 414  | 0.4112          | 0.5733    | 0.6719 | 0.6187 | 0.9135   |
| No log        | 19.0  | 437  | 0.4177          | 0.5829    | 0.6406 | 0.6104 | 0.9159   |
| No log        | 20.0  | 460  | 0.4100          | 0.6028    | 0.6719 | 0.6355 | 0.9159   |
| No log        | 21.0  | 483  | 0.4159          | 0.5869    | 0.6510 | 0.6173 | 0.9165   |
| 0.0279        | 22.0  | 506  | 0.4100          | 0.5853    | 0.6615 | 0.6210 | 0.9153   |
| 0.0279        | 23.0  | 529  | 0.4127          | 0.6172    | 0.6719 | 0.6434 | 0.9189   |
| 0.0279        | 24.0  | 552  | 0.4074          | 0.5945    | 0.6719 | 0.6308 | 0.9153   |
| 0.0279        | 25.0  | 575  | 0.4056          | 0.5909    | 0.6771 | 0.6311 | 0.9165   |
| 0.0279        | 26.0  | 598  | 0.4079          | 0.5740    | 0.6667 | 0.6169 | 0.9153   |
| 0.0279        | 27.0  | 621  | 0.4184          | 0.6117    | 0.6562 | 0.6332 | 0.9189   |
| 0.0279        | 28.0  | 644  | 0.4177          | 0.6165    | 0.6615 | 0.6382 | 0.9195   |
| 0.0279        | 29.0  | 667  | 0.4178          | 0.6106    | 0.6615 | 0.635  | 0.9189   |
| 0.0279        | 30.0  | 690  | 0.4181          | 0.6106    | 0.6615 | 0.635  | 0.9189   |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.0
- Tokenizers 0.15.0