Instructions to use Granoladata/biobert_modality with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Granoladata/biobert_modality with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Granoladata/biobert_modality")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Granoladata/biobert_modality") model = AutoModelForSequenceClassification.from_pretrained("Granoladata/biobert_modality", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| base_model: dmis-lab/biobert-v1.1 | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - f1 | |
| model-index: | |
| - name: biobert_modality | |
| results: [] | |
| <!-- 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. --> | |
| # biobert_modality | |
| This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.2753 | |
| - F1: 0.9368 | |
| ## 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: 2 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | F1 | | |
| |:-------------:|:-----:|:----:|:---------------:|:------:| | |
| | 0.3771 | 1.0 | 241 | 0.3110 | 0.9321 | | |
| | 0.1805 | 2.0 | 482 | 0.2753 | 0.9368 | | |
| ### Framework versions | |
| - Transformers 4.42.3 | |
| - Pytorch 2.3.1+cu121 | |
| - Datasets 2.20.0 | |
| - Tokenizers 0.19.1 | |