Instructions to use judithrosell/BC5CDR_BlueBERT_NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use judithrosell/BC5CDR_BlueBERT_NER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="judithrosell/BC5CDR_BlueBERT_NER")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("judithrosell/BC5CDR_BlueBERT_NER") model = AutoModelForTokenClassification.from_pretrained("judithrosell/BC5CDR_BlueBERT_NER") - Notebooks
- Google Colab
- Kaggle
BC5CDR_BlueBERT_NER
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: 0.0944
Seqeval classification report: precision recall f1-score support
Chemical 0.84 0.89 0.87 7079 Disease 0.82 0.85 0.83 4968
micro avg 0.83 0.87 0.85 12047 macro avg 0.83 0.87 0.85 12047
weighted avg 0.83 0.87 0.85 12047
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
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- 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 | Seqeval classification report |
|---|---|---|---|---|
| No log | 1.0 | 143 | 0.1111 | precision recall f1-score support |
Chemical 0.82 0.86 0.84 7079
Disease 0.76 0.83 0.80 4968
micro avg 0.79 0.85 0.82 12047 macro avg 0.79 0.85 0.82 12047 weighted avg 0.79 0.85 0.82 12047 | | No log | 2.0 | 286 | 0.0987 | precision recall f1-score support
Chemical 0.83 0.89 0.86 7079
Disease 0.78 0.86 0.82 4968
micro avg 0.81 0.88 0.84 12047 macro avg 0.80 0.87 0.84 12047 weighted avg 0.81 0.88 0.84 12047 | | No log | 3.0 | 429 | 0.0944 | precision recall f1-score support
Chemical 0.84 0.89 0.87 7079
Disease 0.82 0.85 0.83 4968
micro avg 0.83 0.87 0.85 12047 macro avg 0.83 0.87 0.85 12047 weighted avg 0.83 0.87 0.85 12047 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
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