Instructions to use qinglinf/boolq2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use qinglinf/boolq2.0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="qinglinf/boolq2.0")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("qinglinf/boolq2.0") model = AutoModelForSequenceClassification.from_pretrained("qinglinf/boolq2.0") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e218e9cd695572f8ff54d4bd8b1be7681ca389582a8ee9b063e96f555ac5a095
- Size of remote file:
- 3.18 kB
- SHA256:
- 412e90cc780e84b53e2823c8fe3558398ca937786addff512d86f92a556d0b27
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