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:
- c90f63d411f370a4b22a1fa7212cc83c8f7cede48d563fed10f38a006dfc04ee
- Size of remote file:
- 3.12 kB
- SHA256:
- fdb2a4ce1f581f374f17e3a4631e9341e82901a09274f6bbdabb9bc9cdd5d8fb
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