Instructions to use jcbao77/sequential_ner_to_nli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jcbao77/sequential_ner_to_nli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="jcbao77/sequential_ner_to_nli")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("jcbao77/sequential_ner_to_nli") model = AutoModelForTokenClassification.from_pretrained("jcbao77/sequential_ner_to_nli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jcbao77/sequential_ner_to_nli: direct link, hf CLI and curl.
- Browser
- Download file 431 MB
-
https://huggingface.co/jcbao77/sequential_ner_to_nli/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jcbao77/sequential_ner_to_nli/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jcbao77/sequential_ner_to_nli/resolve/main/pytorch_model.bin
431 MB
- Xet hash:
- 975b98ad01869218cd4fa85c50acd0d14e4a38d283c81f1f82707c503870bb88
- Size of remote file:
- 431 MB
- SHA256:
- c4a8764f89262a0180ad76011a5ae6e7228d08465cd868807e903e799f7f5eee
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