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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- eeeea445765cc4a301ffdfcbc3e27b0899ca3cb045b6b8b0b158d85c1d562aad
- Size of remote file:
- 2.84 GB
- SHA256:
- caed3a2e472f782abccc029d73daef447beb8165aacc148d941bf3afbea8d73b
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