---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- 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 [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1793
- Precision: 0.7556
- Recall: 0.8015
- Overall F1: 0.7779
- Accuracy: 0.9669

## 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Overall F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:----------:|:--------:|
| 0.0221        | 1.0   | 1041 | 0.1840          | 0.7401    | 0.7976 | 0.7678     | 0.9662   |
| 0.0362        | 2.0   | 2082 | 0.1571          | 0.7490    | 0.8028 | 0.7750     | 0.9662   |
| 0.0187        | 3.0   | 3123 | 0.1793          | 0.7556    | 0.8015 | 0.7779     | 0.9669   |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1