Instructions to use sackoh/bert-base-multilingual-cased-nsmc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sackoh/bert-base-multilingual-cased-nsmc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sackoh/bert-base-multilingual-cased-nsmc")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sackoh/bert-base-multilingual-cased-nsmc") model = AutoModelForSequenceClassification.from_pretrained("sackoh/bert-base-multilingual-cased-nsmc") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0af83fb7f480908f4299f5de3754b1bd16bb9f38524b8170521ee8b83c6a091d
- Size of remote file:
- 711 MB
- SHA256:
- 6a69d1dcc1fd2c1c03b6f77cdc9b621a02ffc667ec4803bf7b597199c846360d
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