---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: token_classification_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. -->

# token_classification_NER

This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2930
- Precision: 0.5538
- Recall: 0.3577
- F1: 0.4347
- Accuracy: 0.9460

## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 213  | 0.2876          | 0.5724    | 0.2419 | 0.3401 | 0.9382   |
| No log        | 2.0   | 426  | 0.2626          | 0.5434    | 0.3133 | 0.3974 | 0.9431   |
| 0.1852        | 3.0   | 639  | 0.2846          | 0.5399    | 0.3262 | 0.4067 | 0.9446   |
| 0.1852        | 4.0   | 852  | 0.2875          | 0.5536    | 0.3494 | 0.4284 | 0.9458   |
| 0.0547        | 5.0   | 1065 | 0.2930          | 0.5538    | 0.3577 | 0.4347 | 0.9460   |


### Framework versions

- Transformers 4.53.3
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.21.4