Instructions to use Ching/negation_detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ching/negation_detector with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="Ching/negation_detector")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Ching/negation_detector") model = AutoModelForQuestionAnswering.from_pretrained("Ching/negation_detector", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Ching/negation_detector: direct link, hf CLI and curl.
- Browser
- Download file 496 MB
-
https://huggingface.co/Ching/negation_detector/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Ching/negation_detector/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Ching/negation_detector/resolve/main/pytorch_model.bin
496 MB
- Xet hash:
- d1c4d8ecb7a828b018b589c6bfbe11c29c67e586908a3be65c19902dbdaf436d
- Size of remote file:
- 496 MB
- SHA256:
- f790711bb8084f9a63b597483b4ab93c3b058848d1388dea4f199592c947318d
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