# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Granoladata/contrast_classifier_biobert_v3")
model = AutoModelForSequenceClassification.from_pretrained("Granoladata/contrast_classifier_biobert_v3")Quick Links
contrast_classifier_biobert_v3
This model is a fine-tuned version of dmis-lab/biobert-v1.1 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2394
- Accuracy: 0.9556
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.045 | 1.0 | 622 | 0.2081 | 0.9524 |
| 0.0009 | 2.0 | 1244 | 0.2234 | 0.9522 |
| 0.001 | 3.0 | 1866 | 0.2394 | 0.9556 |
Framework versions
- Transformers 4.40.0
- Pytorch 2.2.2+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
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Model tree for Granoladata/contrast_classifier_biobert_v3
Base model
dmis-lab/biobert-v1.1
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Granoladata/contrast_classifier_biobert_v3")