Instructions to use connectivity/bert_ft_qqp-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use connectivity/bert_ft_qqp-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="connectivity/bert_ft_qqp-1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("connectivity/bert_ft_qqp-1") model = AutoModelForSequenceClassification.from_pretrained("connectivity/bert_ft_qqp-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from connectivity/bert_ft_qqp-1: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/connectivity/bert_ft_qqp-1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://connectivity/bert_ft_qqp-1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/connectivity/bert_ft_qqp-1/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 21fcf30be727072859ba889d752ec0d6dc6ffd10103adde8d744744ecde1cce4
- Size of remote file:
- 438 MB
- SHA256:
- 8e3c16aeff882a7825cd321ae6f213573550dcfb31ffb60a79ed1c9aa52dcfcb
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