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---
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-v1.0
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: 0.7083333333333334
- name: F1
type: f1
value: 0.46153846153846156
- name: Precision
type: precision
value: 0.5
- name: Recall
type: recall
value: 0.42857142857142855
---
<!-- 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-v1.0
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: 0.7204
- Accuracy: 0.7083
- F1: 0.4615
- Precision: 0.5
- Recall: 0.4286
## 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
- gradient_accumulation_steps: 2
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.5889 | 1.0 | 10 | 0.5438 | 0.625 | 0.0 | 0.0 | 0.0 |
| 0.4926 | 2.0 | 20 | 0.4309 | 0.75 | 0.5714 | 0.5714 | 0.5714 |
| 0.3613 | 3.0 | 30 | 0.4260 | 0.75 | 0.5714 | 0.5714 | 0.5714 |
| 0.2628 | 4.0 | 40 | 0.4989 | 0.75 | 0.5714 | 0.5714 | 0.5714 |
| 0.1658 | 5.0 | 50 | 0.5883 | 0.7083 | 0.4615 | 0.5 | 0.4286 |
| 0.1153 | 6.0 | 60 | 0.6374 | 0.6667 | 0.3333 | 0.4 | 0.2857 |
| 0.074 | 7.0 | 70 | 0.6709 | 0.6667 | 0.3333 | 0.4 | 0.2857 |
| 0.0548 | 8.0 | 80 | 0.6848 | 0.7083 | 0.4615 | 0.5 | 0.4286 |
| 0.0456 | 9.0 | 90 | 0.7322 | 0.7083 | 0.4615 | 0.5 | 0.4286 |
| 0.0439 | 10.0 | 100 | 0.7204 | 0.7083 | 0.4615 | 0.5 | 0.4286 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.2.0+cu121
- Datasets 2.16.1
- Tokenizers 0.13.2
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