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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from bvanaken/clinical-assertion-negation-bert: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/bvanaken/clinical-assertion-negation-bert/resolve/refs%2Fpr%2F2/pytorch_model.bin
- Command line
-
hf download hf://bvanaken/clinical-assertion-negation-bert@refs/pr/2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/bvanaken/clinical-assertion-negation-bert/resolve/refs%2Fpr%2F2/pytorch_model.bin
433 MB
- Xet hash:
- 965341f680db7c2e011316b961c5dde631fadd0e93ce84d2d00c399b82a8d274
- Size of remote file:
- 433 MB
- SHA256:
- a5eb2077bb4192ba2ef24496c24b6c15fd2c7cc6d332fdb07170f4d602658221
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