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:
- f348d2356641fd270cfb797875c3ab736572d1b3331e17554af1d4d9d57ff4a6
- Size of remote file:
- 438 MB
- SHA256:
- 7f493d5a1549c5e38e731596cb4ac3954cda47cd150edeb7c7821d769bf2ae4a
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