Instructions to use emanjavacas/MacBERTh-ing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use emanjavacas/MacBERTh-ing with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="emanjavacas/MacBERTh-ing")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("emanjavacas/MacBERTh-ing") model = AutoModelForSequenceClassification.from_pretrained("emanjavacas/MacBERTh-ing") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 74d9da49af2ade916f50d83354c8d6df8d5c1088cce9a690abaac98e16cce3a3
- Size of remote file:
- 873 MB
- SHA256:
- 303d966ae523b7ce5bc505a2bdfb8325e5d9db8d9ebdaf211bac45e610050892
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