---
license: apache-2.0
base_model: dslim/distilbert-NER
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-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-finetuned-ner

This model is a fine-tuned version of [dslim/distilbert-NER](https://huggingface.co/dslim/distilbert-NER) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4109
- Precision: 0.6952
- Recall: 0.7549
- F1: 0.7238
- Accuracy: 0.8724

## 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: 1e-06
- train_batch_size: 8
- eval_batch_size: 8
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.647         | 1.0   | 977   | 0.8265          | 0.4918    | 0.5741 | 0.5298 | 0.7793   |
| 0.7697        | 2.0   | 1954  | 0.6350          | 0.5801    | 0.6567 | 0.6160 | 0.8194   |
| 0.6089        | 3.0   | 2931  | 0.5591          | 0.6138    | 0.6857 | 0.6478 | 0.8352   |
| 0.534         | 4.0   | 3908  | 0.5163          | 0.6296    | 0.6955 | 0.6609 | 0.8439   |
| 0.4911        | 5.0   | 4885  | 0.4885          | 0.6436    | 0.7075 | 0.6740 | 0.8498   |
| 0.4545        | 6.0   | 5862  | 0.4683          | 0.6526    | 0.7165 | 0.6830 | 0.8557   |
| 0.4379        | 7.0   | 6839  | 0.4534          | 0.6600    | 0.7231 | 0.6901 | 0.8592   |
| 0.4124        | 8.0   | 7816  | 0.4441          | 0.6713    | 0.7274 | 0.6982 | 0.8625   |
| 0.403         | 9.0   | 8793  | 0.4345          | 0.6746    | 0.7359 | 0.7039 | 0.8658   |
| 0.394         | 10.0  | 9770  | 0.4324          | 0.6835    | 0.7445 | 0.7127 | 0.8667   |
| 0.3782        | 11.0  | 10747 | 0.4256          | 0.6820    | 0.7465 | 0.7128 | 0.8678   |
| 0.3706        | 12.0  | 11724 | 0.4213          | 0.6873    | 0.7460 | 0.7155 | 0.8691   |
| 0.3712        | 13.0  | 12701 | 0.4197          | 0.6873    | 0.7518 | 0.7181 | 0.8703   |
| 0.3626        | 14.0  | 13678 | 0.4163          | 0.6882    | 0.7523 | 0.7188 | 0.8713   |
| 0.351         | 15.0  | 14655 | 0.4142          | 0.6905    | 0.7528 | 0.7203 | 0.8717   |
| 0.3528        | 16.0  | 15632 | 0.4142          | 0.6932    | 0.7538 | 0.7222 | 0.8718   |
| 0.3523        | 17.0  | 16609 | 0.4123          | 0.6949    | 0.7533 | 0.7229 | 0.8722   |
| 0.3464        | 18.0  | 17586 | 0.4107          | 0.6936    | 0.7538 | 0.7224 | 0.8727   |
| 0.342         | 19.0  | 18563 | 0.4115          | 0.6954    | 0.7560 | 0.7244 | 0.8726   |
| 0.3496        | 20.0  | 19540 | 0.4109          | 0.6952    | 0.7549 | 0.7238 | 0.8724   |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1