transformers pytorch bert text-classification generated_from_trainer dataset:azaheadhealth base_model:google-bert/bert-base-uncased base_model:finetune:google-bert/bert-base-uncased license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: bert-base-uncased
datasets:
- azaheadhealth
license: apache-2.0
metrics:
- accuracy
- f1
- precision
- recall
tags:
- generated_from_trainer
model-index:
- name: bert-azahead-v0.1
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: azaheadhealth
      type: azaheadhealth
      config: small
      split: test
      args: small
    metrics:
    - type: accuracy
      value: 0.75
      name: Accuracy
    - type: f1
      value: 0.4
      name: F1
    - type: precision
      value: 0.6666666666666666
      name: Precision
    - type: recall
      value: 0.2857142857142857
      name: Recall
---

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# bert-azahead-v0.1

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: 0.4710
- Accuracy: 0.75
- F1: 0.4
- Precision: 0.6667
- Recall: 0.2857

## 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: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6325        | 0.5   | 20   | 0.5001          | 0.7917   | 0.7059 | 0.6       | 0.8571 |
| 0.5346        | 1.0   | 40   | 0.4710          | 0.75     | 0.4    | 0.6667    | 0.2857 |


### Framework versions

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