Instructions to use judithrosell/BlueBERT_CRAFT_NER_new with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use judithrosell/BlueBERT_CRAFT_NER_new with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="judithrosell/BlueBERT_CRAFT_NER_new")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("judithrosell/BlueBERT_CRAFT_NER_new") model = AutoModelForTokenClassification.from_pretrained("judithrosell/BlueBERT_CRAFT_NER_new") - Notebooks
- Google Colab
- Kaggle
BlueBERT_CRAFT_NER_new
This model is a fine-tuned version of bionlp/bluebert_pubmed_mimic_uncased_L-12_H-768_A-12 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1392
- Precision: 0.8229
- Recall: 0.7998
- F1: 0.8112
- Accuracy: 0.9659
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 | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.2722 | 1.0 | 695 | 0.1429 | 0.7839 | 0.7856 | 0.7847 | 0.9603 |
| 0.0811 | 2.0 | 1390 | 0.1351 | 0.8229 | 0.7933 | 0.8078 | 0.9654 |
| 0.0421 | 3.0 | 2085 | 0.1392 | 0.8229 | 0.7998 | 0.8112 | 0.9659 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
- Downloads last month
- 9