Text Classification
Transformers
Safetensors
English
bert
prompt-compression
dependency-detection
referential-dangling
research
text-embeddings-inference
Instructions to use JusperLee/referential-dangling-detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JusperLee/referential-dangling-detector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JusperLee/referential-dangling-detector")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JusperLee/referential-dangling-detector") model = AutoModelForSequenceClassification.from_pretrained("JusperLee/referential-dangling-detector", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from JusperLee/referential-dangling-detector: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/JusperLee/referential-dangling-detector/resolve/main/model.safetensors
- Command line
-
hf download hf://JusperLee/referential-dangling-detector/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/JusperLee/referential-dangling-detector/resolve/main/model.safetensors
438 MB
- Xet hash:
- 9974b30f2e8779e5540bbb0622ac9497bf20746f286f7cfdcdf03d1e05c822d3
- Size of remote file:
- 438 MB
- SHA256:
- 975c25527e583c602b92e063deca8f5c1b0e04bee4843bd8bdbb30b76183560f
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