Instructions to use WesScivetti/SNACS_Multilingual with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WesScivetti/SNACS_Multilingual with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="WesScivetti/SNACS_Multilingual")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("WesScivetti/SNACS_Multilingual") model = AutoModelForTokenClassification.from_pretrained("WesScivetti/SNACS_Multilingual", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8f228359f98c21cac9a63b8365333944d8a66166b49f4b616392768859f97b16
- Size of remote file:
- 2.24 GB
- SHA256:
- dc648e18cf277c3d964239dea169fa87e7c6a106d1d5c732605cdaa03a060592
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