---
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 the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2988
- Precision: 0.8066
- Recall: 0.7644
- F1: 0.7849
- Accuracy: 0.9432

## 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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 13   | 1.7619          | 0.0       | 0.0    | 0.0    | 0.6185   |
| No log        | 2.0   | 26   | 1.4482          | 0.0526    | 0.0052 | 0.0095 | 0.6985   |
| No log        | 3.0   | 39   | 1.0747          | 0.0417    | 0.0105 | 0.0167 | 0.7429   |
| No log        | 4.0   | 52   | 0.8462          | 0.2262    | 0.0995 | 0.1382 | 0.7821   |
| No log        | 5.0   | 65   | 0.6852          | 0.3290    | 0.2670 | 0.2948 | 0.8172   |
| No log        | 6.0   | 78   | 0.5970          | 0.4346    | 0.4869 | 0.4593 | 0.8684   |
| No log        | 7.0   | 91   | 0.5108          | 0.5072    | 0.5497 | 0.5276 | 0.8880   |
| No log        | 8.0   | 104  | 0.4515          | 0.5882    | 0.6283 | 0.6076 | 0.9086   |
| No log        | 9.0   | 117  | 0.4105          | 0.6305    | 0.6702 | 0.6497 | 0.9169   |
| No log        | 10.0  | 130  | 0.3755          | 0.7120    | 0.6859 | 0.6987 | 0.9293   |
| No log        | 11.0  | 143  | 0.3661          | 0.7243    | 0.7016 | 0.7128 | 0.9293   |
| No log        | 12.0  | 156  | 0.3460          | 0.7273    | 0.7120 | 0.7196 | 0.9313   |
| No log        | 13.0  | 169  | 0.3287          | 0.7609    | 0.7330 | 0.7467 | 0.9355   |
| No log        | 14.0  | 182  | 0.3177          | 0.7701    | 0.7539 | 0.7619 | 0.9370   |
| No log        | 15.0  | 195  | 0.3133          | 0.7705    | 0.7382 | 0.7540 | 0.9360   |
| No log        | 16.0  | 208  | 0.3028          | 0.7826    | 0.7539 | 0.7680 | 0.9406   |
| No log        | 17.0  | 221  | 0.3062          | 0.7944    | 0.7487 | 0.7709 | 0.9391   |
| No log        | 18.0  | 234  | 0.3015          | 0.8011    | 0.7592 | 0.7796 | 0.9411   |
| No log        | 19.0  | 247  | 0.2997          | 0.7935    | 0.7644 | 0.7787 | 0.9432   |
| No log        | 20.0  | 260  | 0.2988          | 0.8066    | 0.7644 | 0.7849 | 0.9432   |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.3.0+cpu
- Datasets 2.19.1
- Tokenizers 0.19.1