---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- azaheadhealth
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: microtest-2.0
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: azaheadhealth
      type: azaheadhealth
      config: micro
      split: test
      args: micro
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.75
    - name: F1
      type: f1
      value: 0.8
    - name: Precision
      type: precision
      value: 0.6666666666666666
    - name: Recall
      type: recall
      value: 1.0
---

<!-- 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. -->

# microtest-2.0

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: 0.3672
- Accuracy: 0.75
- F1: 0.8
- Precision: 0.6667
- Recall: 1.0

## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1  | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---:|:---------:|:------:|
| 0.8113        | 0.5   | 1    | 0.4486          | 0.75     | 0.8 | 0.6667    | 1.0    |
| 0.7227        | 1.0   | 2    | 0.3672          | 0.75     | 0.8 | 0.6667    | 1.0    |


### Framework versions

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