Instructions to use praneethvasarla/bio-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use praneethvasarla/bio-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="praneethvasarla/bio-bert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("praneethvasarla/bio-bert") model = AutoModelForTokenClassification.from_pretrained("praneethvasarla/bio-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
bio-bert
This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0000
- Precision: 1.0
- Recall: 1.0
- F1: 1.0
- Accuracy: 1.0
- All Metrics: {'CELL': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6270}, 'CHEMICAL': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 34547}, 'CORONAVIRUS': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 4777}, 'DISEASE_OR_SYNDROME': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13422}, 'EUKARYOTE': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2501}, 'GENE_OR_GENOME': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 32504}, 'ORGANISM': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 4620}, 'THERAPEUTIC_OR_PREVENTIVE_PROCEDURE': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 3186}, 'TISSUE': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2333}, 'VIRUS': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1596}, 'overall_precision': 1.0, 'overall_recall': 1.0, 'overall_f1': 1.0, 'overall_accuracy': 1.0}
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: 8
- eval_batch_size: 8
- 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 | Precision | Recall | F1 | Accuracy | All Metrics |
|---|---|---|---|---|---|---|---|---|
| 0.0003 | 1.0 | 3375 | 0.0001 | 0.9999 | 1.0000 | 0.9999 | 1.0000 | {'CELL': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6270}, 'CHEMICAL': {'precision': 0.9999131693198263, 'recall': 1.0, 'f1': 0.9999565827749395, 'number': 34547}, 'CORONAVIRUS': {'precision': 0.9989539748953975, 'recall': 0.9995813271927988, 'f1': 0.9992675525792613, 'number': 4777}, 'DISEASE_OR_SYNDROME': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13422}, 'EUKARYOTE': {'precision': 0.9992006394884093, 'recall': 0.9996001599360256, 'f1': 0.9994003597841296, 'number': 2501}, 'GENE_OR_GENOME': {'precision': 0.9998769457946225, 'recall': 0.999938469111494, 'f1': 0.9999077065066913, 'number': 32504}, 'ORGANISM': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 4620}, 'THERAPEUTIC_OR_PREVENTIVE_PROCEDURE': {'precision': 0.9996862252902416, 'recall': 1.0, 'f1': 0.9998430880276166, 'number': 3186}, 'TISSUE': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2333}, 'VIRUS': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1596}, 'overall_precision': 0.9998581774861487, 'overall_recall': 0.999952721358599, 'overall_f1': 0.9999054471875266, 'overall_accuracy': 0.9999971807367637} |
| 0.0001 | 2.0 | 6750 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | {'CELL': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6270}, 'CHEMICAL': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 34547}, 'CORONAVIRUS': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 4777}, 'DISEASE_OR_SYNDROME': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13422}, 'EUKARYOTE': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2501}, 'GENE_OR_GENOME': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 32504}, 'ORGANISM': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 4620}, 'THERAPEUTIC_OR_PREVENTIVE_PROCEDURE': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 3186}, 'TISSUE': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2333}, 'VIRUS': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1596}, 'overall_precision': 1.0, 'overall_recall': 1.0, 'overall_f1': 1.0, 'overall_accuracy': 1.0} |
| 0.0 | 3.0 | 10125 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | {'CELL': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6270}, 'CHEMICAL': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 34547}, 'CORONAVIRUS': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 4777}, 'DISEASE_OR_SYNDROME': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13422}, 'EUKARYOTE': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2501}, 'GENE_OR_GENOME': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 32504}, 'ORGANISM': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 4620}, 'THERAPEUTIC_OR_PREVENTIVE_PROCEDURE': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 3186}, 'TISSUE': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2333}, 'VIRUS': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1596}, 'overall_precision': 1.0, 'overall_recall': 1.0, 'overall_f1': 1.0, 'overall_accuracy': 1.0} |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
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Model tree for praneethvasarla/bio-bert
Base model
google-bert/bert-base-uncased