Text Classification
Transformers
PyTorch
TensorFlow
Safetensors
English
bert
medical
clinical
assertion
negation
Instructions to use bvanaken/clinical-assertion-negation-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bvanaken/clinical-assertion-negation-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bvanaken/clinical-assertion-negation-bert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bvanaken/clinical-assertion-negation-bert") model = AutoModelForSequenceClassification.from_pretrained("bvanaken/clinical-assertion-negation-bert") - Inference
- Notebooks
- Google Colab
- Kaggle
fix_labels
#1
by kamalkraj - opened
No description provided.
- Update label2id and id2label mapping
- Updated model inference with pipeline
really cool!
I'm assuming the fine-tuning dataset isn't present on hf.co (biomedical datasets are sensitive so sometimes hard to move around) otherwise you could link the model to the dataset using datasets: in the metadata
bvanaken changed pull request status to merged
Got it! For context, we are in the process of building more tools/workflows around access to datasets, which we hope will be useful to the community – but I recognize that biomedical datasets are particularly sensitive!