---
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 an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2228
- Precision: 0.8030
- Recall: 0.8093
- F1: 0.8061
- Accuracy: 0.9545

## 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: 0.0005
- 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
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3032        | 1.0   | 878  | 0.3241          | 0.6979    | 0.5912 | 0.6401 | 0.9168   |
| 0.2666        | 2.0   | 1756 | 0.2822          | 0.6475    | 0.6577 | 0.6525 | 0.9221   |
| 0.2025        | 3.0   | 2634 | 0.2402          | 0.7021    | 0.7273 | 0.7144 | 0.9369   |
| 0.1421        | 4.0   | 3512 | 0.2158          | 0.7283    | 0.7331 | 0.7307 | 0.9390   |
| 0.111         | 5.0   | 4390 | 0.2189          | 0.7442    | 0.7395 | 0.7418 | 0.9417   |
| 0.0813        | 6.0   | 5268 | 0.2196          | 0.7307    | 0.7812 | 0.7551 | 0.9442   |
| 0.0538        | 7.0   | 6146 | 0.2169          | 0.7594    | 0.8049 | 0.7815 | 0.9497   |
| 0.0389        | 8.0   | 7024 | 0.2133          | 0.7929    | 0.7991 | 0.7960 | 0.9520   |
| 0.0263        | 9.0   | 7902 | 0.2192          | 0.8002    | 0.7991 | 0.7996 | 0.9530   |
| 0.0141        | 10.0  | 8780 | 0.2224          | 0.8029    | 0.8097 | 0.8063 | 0.9546   |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.17.0
- Tokenizers 0.15.2