---
license: apache-2.0
base_model: distilbert-base-cased
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 [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1130
- Precision: 0.9327
- Recall: 0.9467
- F1: 0.9396
- Accuracy: 0.9848

## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0915        | 1.0   | 1756  | 0.0935          | 0.8678    | 0.9014 | 0.8843 | 0.9731   |
| 0.0497        | 2.0   | 3512  | 0.0815          | 0.9070    | 0.9295 | 0.9181 | 0.9815   |
| 0.0324        | 3.0   | 5268  | 0.0798          | 0.9027    | 0.9320 | 0.9171 | 0.9810   |
| 0.0214        | 4.0   | 7024  | 0.0802          | 0.9252    | 0.9372 | 0.9312 | 0.9836   |
| 0.012         | 5.0   | 8780  | 0.0899          | 0.9275    | 0.9413 | 0.9343 | 0.9838   |
| 0.0088        | 6.0   | 10536 | 0.1003          | 0.9253    | 0.9418 | 0.9334 | 0.9840   |
| 0.0049        | 7.0   | 12292 | 0.1064          | 0.9267    | 0.9446 | 0.9356 | 0.9842   |
| 0.0036        | 8.0   | 14048 | 0.1127          | 0.9286    | 0.9438 | 0.9361 | 0.9839   |
| 0.0012        | 9.0   | 15804 | 0.1112          | 0.9307    | 0.9455 | 0.9381 | 0.9847   |
| 0.0007        | 10.0  | 17560 | 0.1130          | 0.9327    | 0.9467 | 0.9396 | 0.9848   |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.3.0+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1