metadata
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
distilbert-base-uncased-finetuned-health_facts
This model is a fine-tuned version of 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