---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-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. -->

# bert-finetuned-ner

This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1622
- Precision: 0.7774
- Recall: 0.7937
- F1: 0.7854
- Accuracy: 0.9707

## 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: 8e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 131  | 0.1355          | 0.6880    | 0.7298 | 0.7083 | 0.9604   |
| No log        | 2.0   | 262  | 0.1194          | 0.7564    | 0.7727 | 0.7645 | 0.9684   |
| No log        | 3.0   | 393  | 0.1277          | 0.7731    | 0.7868 | 0.7799 | 0.9691   |
| 0.0433        | 4.0   | 524  | 0.1433          | 0.7553    | 0.7829 | 0.7688 | 0.9685   |
| 0.0433        | 5.0   | 655  | 0.1515          | 0.7734    | 0.7946 | 0.7839 | 0.9700   |
| 0.0433        | 6.0   | 786  | 0.1518          | 0.7819    | 0.8008 | 0.7912 | 0.9708   |
| 0.0433        | 7.0   | 917  | 0.1602          | 0.7752    | 0.7914 | 0.7832 | 0.9704   |
| 0.0094        | 8.0   | 1048 | 0.1622          | 0.7774    | 0.7937 | 0.7854 | 0.9707   |


### Framework versions

- Transformers 4.30.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3