---
license: apache-2.0
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 the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0078
- Precision: 0.9842
- Recall: 0.9896
- F1: 0.9869
- Accuracy: 0.9984

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0043        | 1.0   | 1134 | 0.0085          | 0.9784    | 0.9857 | 0.9820 | 0.9980   |
| 0.0025        | 2.0   | 2268 | 0.0078          | 0.9828    | 0.9889 | 0.9858 | 0.9983   |
| 0.0013        | 3.0   | 3402 | 0.0078          | 0.9842    | 0.9896 | 0.9869 | 0.9984   |


### Framework versions

- Transformers 4.29.1
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.3