---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- azaheadhealth
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: azahead-bert-v1.0
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: azaheadhealth
      type: azaheadhealth
      config: small
      split: test
      args: small
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7916666666666666
    - name: F1
      type: f1
      value: 0.6666666666666666
    - name: Precision
      type: precision
      value: 0.625
    - name: Recall
      type: recall
      value: 0.7142857142857143
---

<!-- 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. -->

# azahead-bert-v1.0

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co./bert-base-uncased) on the azaheadhealth dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5108
- Accuracy: 0.7917
- F1: 0.6667
- Precision: 0.625
- Recall: 0.7143

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6157        | 1.0   | 20   | 0.5087          | 0.7083   | 0.0    | 0.0       | 0.0    |
| 0.4057        | 2.0   | 40   | 0.5892          | 0.7083   | 0.2222 | 0.5       | 0.1429 |
| 0.2788        | 3.0   | 60   | 0.4834          | 0.7917   | 0.4444 | 1.0       | 0.2857 |
| 0.1726        | 4.0   | 80   | 0.5108          | 0.7917   | 0.6667 | 0.625     | 0.7143 |
| 0.1271        | 5.0   | 100  | 0.6210          | 0.7917   | 0.6154 | 0.6667    | 0.5714 |
| 0.1045        | 6.0   | 120  | 0.6850          | 0.7917   | 0.6154 | 0.6667    | 0.5714 |
| 0.0108        | 7.0   | 140  | 0.7771          | 0.75     | 0.5714 | 0.5714    | 0.5714 |
| 0.0248        | 8.0   | 160  | 0.8454          | 0.7917   | 0.6154 | 0.6667    | 0.5714 |
| 0.0152        | 9.0   | 180  | 0.8667          | 0.7917   | 0.6154 | 0.6667    | 0.5714 |
| 0.0064        | 10.0  | 200  | 0.8776          | 0.75     | 0.5714 | 0.5714    | 0.5714 |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.2.0+cu121
- Datasets 2.16.1
- Tokenizers 0.13.2