---
license: apache-2.0
base_model: bert-large-cased
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 [bert-large-cased](https://huggingface.co/bert-large-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0546
- Precision: 0.9447
- Recall: 0.9571
- F1: 0.9508
- Accuracy: 0.9880

## 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 | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0641        | 1.0   | 1756 | 0.0564          | 0.9248    | 0.9482 | 0.9363 | 0.9853   |
| 0.0317        | 2.0   | 3512 | 0.0531          | 0.9451    | 0.9562 | 0.9506 | 0.9880   |
| 0.0162        | 3.0   | 5268 | 0.0546          | 0.9447    | 0.9571 | 0.9508 | 0.9880   |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.2.2+cpu
- Datasets 2.19.0
- Tokenizers 0.19.1