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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 968b9a063d0b84b5d37c5eeea254774db10a480e74de88470f8f67fefbcd1f83
- Size of remote file:
- 436 MB
- SHA256:
- 06d8fb4618d2809f192a969e29c2cfd3888784325a163e5512d2e9c7471b45ff
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