---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distillbert-base-cased-finetuned-ner4
  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. -->

# distillbert-base-cased-finetuned-ner4

This model is a fine-tuned version of [distilbert/distilbert-base-cased](https://huggingface.co/distilbert/distilbert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2788
- Precision: 0.8173
- Recall: 0.8406
- F1: 0.8288
- Accuracy: 0.9638

## 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: 8
- eval_batch_size: 8
- 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: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1556        | 1.0   | 4750  | 0.1489          | 0.7509    | 0.7998 | 0.7745 | 0.9536   |
| 0.1231        | 2.0   | 9500  | 0.1393          | 0.7914    | 0.7830 | 0.7872 | 0.9562   |
| 0.1025        | 3.0   | 14250 | 0.1175          | 0.8139    | 0.8291 | 0.8214 | 0.9624   |
| 0.0783        | 4.0   | 19000 | 0.1285          | 0.8101    | 0.8272 | 0.8186 | 0.9630   |
| 0.0642        | 5.0   | 23750 | 0.1500          | 0.8148    | 0.8320 | 0.8233 | 0.9615   |
| 0.0458        | 6.0   | 28500 | 0.1545          | 0.8010    | 0.8388 | 0.8195 | 0.9619   |
| 0.038         | 7.0   | 33250 | 0.1730          | 0.8138    | 0.8343 | 0.8239 | 0.9616   |
| 0.0295        | 8.0   | 38000 | 0.1848          | 0.8110    | 0.8331 | 0.8219 | 0.9615   |
| 0.025         | 9.0   | 42750 | 0.1916          | 0.8063    | 0.8370 | 0.8213 | 0.9619   |
| 0.0171        | 10.0  | 47500 | 0.2054          | 0.8089    | 0.8352 | 0.8218 | 0.9630   |
| 0.0138        | 11.0  | 52250 | 0.2249          | 0.8107    | 0.8352 | 0.8228 | 0.9624   |
| 0.0107        | 12.0  | 57000 | 0.2307          | 0.8197    | 0.8379 | 0.8287 | 0.9636   |
| 0.0081        | 13.0  | 61750 | 0.2470          | 0.8080    | 0.8352 | 0.8214 | 0.9630   |
| 0.0048        | 14.0  | 66500 | 0.2555          | 0.8109    | 0.8361 | 0.8233 | 0.9629   |
| 0.0041        | 15.0  | 71250 | 0.2640          | 0.8130    | 0.8400 | 0.8263 | 0.9634   |
| 0.0027        | 16.0  | 76000 | 0.2728          | 0.8171    | 0.8409 | 0.8288 | 0.9635   |
| 0.002         | 17.0  | 80750 | 0.2753          | 0.8154    | 0.8395 | 0.8273 | 0.9634   |
| 0.0016        | 18.0  | 85500 | 0.2780          | 0.8155    | 0.8409 | 0.8280 | 0.9637   |
| 0.0016        | 19.0  | 90250 | 0.2786          | 0.8180    | 0.8415 | 0.8296 | 0.9635   |
| 0.0012        | 20.0  | 95000 | 0.2788          | 0.8173    | 0.8406 | 0.8288 | 0.9638   |


### Framework versions

- Transformers 4.50.1
- Pytorch 2.5.1+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1