Instructions to use ModelTC/bert-base-uncased-qqp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ModelTC/bert-base-uncased-qqp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ModelTC/bert-base-uncased-qqp")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ModelTC/bert-base-uncased-qqp") model = AutoModelForSequenceClassification.from_pretrained("ModelTC/bert-base-uncased-qqp") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7bcf36e791ae4288b60b58b838d2d4582a01f00f82be0d46ea8fc06824d10f11
- Size of remote file:
- 438 MB
- SHA256:
- 5b1d1c7a1ccf16fdeb05c21b29ef04144c4a6f0130eae725ec063f0b244f22ff
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