---
license: apache-2.0
base_model: bert-base-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-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0025
- Precision: 0.6402
- Recall: 0.7307
- F1: 0.6824
- Accuracy: 0.9992

## 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 383  | 0.0032          | 0.6972    | 0.528  | 0.6009 | 0.9991   |
| 0.0292        | 2.0   | 766  | 0.0023          | 0.7590    | 0.672  | 0.7129 | 0.9994   |
| 0.0018        | 3.0   | 1149 | 0.0023          | 0.7660    | 0.7333 | 0.7493 | 0.9994   |
| 0.0009        | 4.0   | 1532 | 0.0023          | 0.7520    | 0.736  | 0.7439 | 0.9994   |
| 0.0009        | 5.0   | 1915 | 0.0025          | 0.6402    | 0.7307 | 0.6824 | 0.9992   |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.2