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 training_args.bin from Ching/negation_detector: direct link, hf CLI and curl.
- Browser
- Download file 2.42 kB
-
https://huggingface.co/Ching/negation_detector/resolve/main/training_args.bin
- Command line
-
hf download hf://Ching/negation_detector/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Ching/negation_detector/resolve/main/training_args.bin
2.42 kB
- Xet hash:
- 425254e6cc1b2030676b52d9ab6fb75e7b2929eab166eea5c8c6f8b351189832
- Size of remote file:
- 2.42 kB
- SHA256:
- 94115db3e32b7a7697fdda7d85380088aef2ff0a39bb5d6794e74ed77000f29a
路
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