---
license: apache-2.0
base_model: google-bert/bert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-case-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-base-case-ner

This model is a fine-tuned version of [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1741
- Precision: 0.7713
- Recall: 0.8081
- F1: 0.7893
- Accuracy: 0.9675

## 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1035        | 1.0   | 1041 | 0.1460          | 0.7285    | 0.7590 | 0.7434 | 0.9614   |
| 0.0684        | 2.0   | 2082 | 0.1438          | 0.7017    | 0.7767 | 0.7373 | 0.9631   |
| 0.0423        | 3.0   | 3123 | 0.1504          | 0.7591    | 0.7978 | 0.7780 | 0.9670   |
| 0.0278        | 4.0   | 4164 | 0.1606          | 0.7683    | 0.8008 | 0.7842 | 0.9670   |
| 0.0207        | 5.0   | 5205 | 0.1741          | 0.7713    | 0.8081 | 0.7893 | 0.9675   |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.0.1+cu117
- Datasets 2.18.0
- Tokenizers 0.19.1