Instructions to use abcdda/entity_extract with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abcdda/entity_extract with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="abcdda/entity_extract")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("abcdda/entity_extract") model = AutoModelForTokenClassification.from_pretrained("abcdda/entity_extract", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from abcdda/entity_extract: direct link, hf CLI and curl.
- Browser
- Download file 265 MB
-
https://huggingface.co/abcdda/entity_extract/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://abcdda/entity_extract/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/abcdda/entity_extract/resolve/main/pytorch_model.bin
265 MB
- Xet hash:
- ace6e2962edea58350495a9572cff5894812eb3b252f93a1d0904ba5d2f43dbd
- Size of remote file:
- 265 MB
- SHA256:
- 4b98a61d4cc4505bef3640a2e94e3a368ae6b70259bc2b22d9f28ed33889d03b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.