Instructions to use mudes/en-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mudes/en-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="mudes/en-base")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("mudes/en-base") model = AutoModelForTokenClassification.from_pretrained("mudes/en-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b0437cf0322e06540860dca6a8e684ce50634e51babc8eb9599991fd9e95e4f2
- Size of remote file:
- 2.99 kB
- SHA256:
- 0be3276d8f96191142ad05cd4fcb8f1f6e72697465faa8dcfea168157dfee269
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