---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- azaheadhealth
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: bert-azahead-v1.1
  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: 0.6153846153846154
    - name: Precision
      type: precision
      value: 0.6666666666666666
    - name: Recall
      type: recall
      value: 0.5714285714285714
---

<!-- 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-azahead-v1.1

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: 0.4785
- Accuracy: 0.7917
- F1: 0.6154
- Precision: 0.6667
- Recall: 0.5714

## 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: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6318        | 1.0   | 10   | 0.5247          | 0.6667   | 0.0    | 0.0       | 0.0    |
| 0.5623        | 2.0   | 20   | 0.4065          | 0.7917   | 0.5455 | 0.75      | 0.4286 |
| 0.4688        | 3.0   | 30   | 0.3514          | 0.7917   | 0.5455 | 0.75      | 0.4286 |
| 0.4252        | 4.0   | 40   | 0.3224          | 0.8333   | 0.6667 | 0.8       | 0.5714 |
| 0.2409        | 5.0   | 50   | 0.4115          | 0.75     | 0.4    | 0.6667    | 0.2857 |
| 0.2196        | 6.0   | 60   | 0.3672          | 0.7917   | 0.6667 | 0.625     | 0.7143 |
| 0.1417        | 7.0   | 70   | 0.4441          | 0.7917   | 0.5455 | 0.75      | 0.4286 |
| 0.0842        | 8.0   | 80   | 0.4422          | 0.7917   | 0.6154 | 0.6667    | 0.5714 |
| 0.065         | 9.0   | 90   | 0.4556          | 0.7917   | 0.6154 | 0.6667    | 0.5714 |
| 0.0657        | 10.0  | 100  | 0.4785          | 0.7917   | 0.6154 | 0.6667    | 0.5714 |


### Framework versions

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