---
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 was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0768
- Precision: 0.9783
- Recall: 0.9739
- F1: 0.9761
- Accuracy: 0.9865

## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 11

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0109        | 1.0   | 5372  | 0.0469          | 0.9828    | 0.9799 | 0.9813 | 0.9901   |
| 0.0068        | 2.0   | 10744 | 0.0598          | 0.9790    | 0.9761 | 0.9776 | 0.9885   |
| 0.0032        | 3.0   | 16116 | 0.1658          | 0.9715    | 0.9679 | 0.9697 | 0.9839   |
| 0.0048        | 4.0   | 21488 | 0.1680          | 0.9774    | 0.9679 | 0.9726 | 0.9835   |
| 0.0055        | 5.0   | 26860 | 0.0526          | 0.9835    | 0.9806 | 0.9821 | 0.9911   |
| 0.0034        | 6.0   | 32232 | 0.0569          | 0.9813    | 0.9776 | 0.9794 | 0.9906   |
| 0.0024        | 7.0   | 37604 | 0.0671          | 0.9820    | 0.9784 | 0.9802 | 0.9906   |
| 0.0021        | 8.0   | 42976 | 0.0606          | 0.9835    | 0.9776 | 0.9805 | 0.9908   |
| 0.0022        | 9.0   | 48348 | 0.0658          | 0.9857    | 0.9806 | 0.9832 | 0.9911   |
| 0.002         | 10.0  | 53720 | 0.1342          | 0.9767    | 0.9716 | 0.9742 | 0.9857   |
| 0.0017        | 11.0  | 59092 | 0.0768          | 0.9783    | 0.9739 | 0.9761 | 0.9865   |


### Framework versions

- Transformers 4.30.2
- Pytorch 1.13.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3