Instructions to use leonweber/bunsen_base_best with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use leonweber/bunsen_base_best with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="leonweber/bunsen_base_best")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("leonweber/bunsen_base_best") model = AutoModelForTokenClassification.from_pretrained("leonweber/bunsen_base_best", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b8bf30157e5ab197770b33e8c25b0095ae7c9aa4266b7fc19fe00ddbae6f5423
- Size of remote file:
- 449 MB
- SHA256:
- b4bd21220bfe6b5b3e515126f625bdf4bfecc40b32e6bac26d6bf012fc00efa7
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