pytorch tensorboard distilbert text-classification dataset:health_fact transformers generated_from_trainer license:apache-2.0 model-index
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- health_fact
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-health_facts
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: health_fact
      type: health_fact
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.628500823723229
    - name: F1
      type: f1
      value: 0.6544946803476833
---

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# distilbert-base-uncased-finetuned-health_facts

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the health_fact dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1227
- Accuracy: 0.6285
- F1: 0.6545

## 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: 64
- eval_batch_size: 64
- seed: 42
- 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.1367        | 1.0   | 154  | 0.9423          | 0.5560   | 0.6060 |
| 0.9444        | 2.0   | 308  | 0.9267          | 0.5733   | 0.6170 |
| 0.8248        | 3.0   | 462  | 0.9483          | 0.5832   | 0.6256 |
| 0.7213        | 4.0   | 616  | 1.0119          | 0.5815   | 0.6219 |
| 0.608         | 5.0   | 770  | 1.1227          | 0.6285   | 0.6545 |


### Framework versions

- Transformers 4.11.3
- Pytorch 1.10.0
- Datasets 1.16.1
- Tokenizers 0.10.3
