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 optimizer.pt from Ching/negation_detector: direct link, hf CLI and curl.
- Browser
- Download file 993 MB
-
https://huggingface.co/Ching/negation_detector/resolve/refs%2Fpr%2F1/optimizer.pt
- Command line
-
hf download hf://Ching/negation_detector@refs/pr/1/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/Ching/negation_detector/resolve/refs%2Fpr%2F1/optimizer.pt
993 MB
- Xet hash:
- 310b707abdfe0bbe55d21bc69c70d390a378dfed2aa2710e1f04bf5fba527f0a
- Size of remote file:
- 993 MB
- SHA256:
- 20162c9138519a55ee9e1892cd898e49502c0febdbb786ea523f12f54138b69c
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.