Instructions to use Thibeb/DrBert_generalized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Thibeb/DrBert_generalized with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Thibeb/DrBert_generalized")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Thibeb/DrBert_generalized") model = AutoModelForTokenClassification.from_pretrained("Thibeb/DrBert_generalized", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cff9a463dab0a67f21a0a89e5ee395e0722963531ca8158b0fe40094f9cd2f7a
- Size of remote file:
- 871 MB
- SHA256:
- 4bcb51253956ee559005d90bfc0b54e930e30ade85cafa5848cb9efd99890b62
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