Instructions to use dtorber/bert-base-spanish-wwm-cased_K2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dtorber/bert-base-spanish-wwm-cased_K2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dtorber/bert-base-spanish-wwm-cased_K2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dtorber/bert-base-spanish-wwm-cased_K2") model = AutoModelForSequenceClassification.from_pretrained("dtorber/bert-base-spanish-wwm-cased_K2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1dd7814fc23d1bd0108f722ac8f6e2b6bdabffee5953519c224df8130c353aee
- Size of remote file:
- 439 MB
- SHA256:
- 90d5b67aa350abd512b67c1d553647366ca7acbea0b10e4a8ac0977d015395fb
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