Instructions to use rcds/MiniLM-swiss_citation_extraction-de-fr-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rcds/MiniLM-swiss_citation_extraction-de-fr-it with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="rcds/MiniLM-swiss_citation_extraction-de-fr-it")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("rcds/MiniLM-swiss_citation_extraction-de-fr-it") model = AutoModelForTokenClassification.from_pretrained("rcds/MiniLM-swiss_citation_extraction-de-fr-it", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b638180715ec08deb7d31d837f331acb4ad222cf3ef4acb016ced6f3e3f6a844
- Size of remote file:
- 138 MB
- SHA256:
- a5189f91e0ed539a87b24b370cbe66c7f00336dc4d4a734cc66b2cca812580d1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.