Instructions to use ndavid/binary-qa-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ndavid/binary-qa-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ndavid/binary-qa-bert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ndavid/binary-qa-bert") model = AutoModelForSequenceClassification.from_pretrained("ndavid/binary-qa-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1971674b90af527c25fcf745d527e039a34482e919e189e55bd5b31c06166629
- Size of remote file:
- 433 MB
- SHA256:
- b1540ec5d6d9335cf80265d46fbb70118f74cd8426a413e72f79db25dc4f3a3b
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