metadata
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-v0.1
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: 0.75
- name: F1
type: f1
value: 0.4
- name: Precision
type: precision
value: 0.6666666666666666
- name: Recall
type: recall
value: 0.2857142857142857
bert-azahead-v0.1
This model is a fine-tuned version of 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