Instructions to use jasminejwebb/KeywordIdentifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jasminejwebb/KeywordIdentifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="jasminejwebb/KeywordIdentifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("jasminejwebb/KeywordIdentifier") model = AutoModelForTokenClassification.from_pretrained("jasminejwebb/KeywordIdentifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jasminejwebb/KeywordIdentifier: direct link, hf CLI and curl.
- Browser
- Download file 467 MB
-
https://huggingface.co/jasminejwebb/KeywordIdentifier/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jasminejwebb/KeywordIdentifier/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jasminejwebb/KeywordIdentifier/resolve/main/pytorch_model.bin
467 MB
- Xet hash:
- e41f50528e0275b4eec8d7e8907f3dcffd3cc0b2e2dcbb25f30dbd40a38283b2
- Size of remote file:
- 467 MB
- SHA256:
- 63d0acc13024be2086fff92427179eebe83c0cb9caf6450d9f0d012e0837bbfa
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.