Instructions to use GEOcite/ReferenceParserModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GEOcite/ReferenceParserModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="GEOcite/ReferenceParserModel", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("GEOcite/ReferenceParserModel", trust_remote_code=True) model = AutoModelForTokenClassification.from_pretrained("GEOcite/ReferenceParserModel", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from GEOcite/ReferenceParserModel: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/GEOcite/ReferenceParserModel/resolve/refs%2Fpr%2F1/model.safetensors
- Command line
-
hf download hf://GEOcite/ReferenceParserModel@refs/pr/1/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/GEOcite/ReferenceParserModel/resolve/refs%2Fpr%2F1/model.safetensors
1.11 GB
- Xet hash:
- 5564bfbf70269e61a9c852d2b2d7fcf35f8eaa45eee7b5b30b575fe027a25808
- Size of remote file:
- 1.11 GB
- SHA256:
- ded8fc304d11eff8cd039e74f7200ce929b20d886ad0da252ebc06bfa738f334
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.