Instructions to use kbulutozler/distilbert-base-uncased-FT-ner-NCBI-disease with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kbulutozler/distilbert-base-uncased-FT-ner-NCBI-disease with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="kbulutozler/distilbert-base-uncased-FT-ner-NCBI-disease")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("kbulutozler/distilbert-base-uncased-FT-ner-NCBI-disease") model = AutoModelForTokenClassification.from_pretrained("kbulutozler/distilbert-base-uncased-FT-ner-NCBI-disease") - Notebooks
- Google Colab
- Kaggle
Model Card for Model ID
Fine-tuned distilbert model. Trained on train set of NCBI-disease dataset taken from BLURB.
Model Details
Model Sources [optional]
Training Details
Training Data
Train set of NCBI-disease dataset.
Training Procedure
Classical fine-tuning.
Training Hyperparameters
- Training regime: [More Information Needed]
learning_rate=5e-5 per_device_train_batch_size=16 per_device_eval_batch_size=16 num_train_epochs=3 weight_decay=0.01
Evaluation
Testing Data
Test set of NCBI-disease dataset.
Results
Precision: 0.81 Recall: 0.86 Micro-F1: 0.84
Environmental Impact
- Hardware Type: 1xRTX A4000
- Hours used: 00:06:00
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Model tree for kbulutozler/distilbert-base-uncased-FT-ner-NCBI-disease
Base model
distilbert/distilbert-base-uncased