Instructions to use connectivity/bert_ft_qqp-12 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use connectivity/bert_ft_qqp-12 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="connectivity/bert_ft_qqp-12")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("connectivity/bert_ft_qqp-12") model = AutoModelForSequenceClassification.from_pretrained("connectivity/bert_ft_qqp-12", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from connectivity/bert_ft_qqp-12: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/connectivity/bert_ft_qqp-12/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://connectivity/bert_ft_qqp-12/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/connectivity/bert_ft_qqp-12/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 7283c885bdb791f012deed3132041a1581799d9162e9d628eea819c30cdd4527
- Size of remote file:
- 438 MB
- SHA256:
- e7fd97a94a8560694c2061a6771de4d8be6470bed3c7f9eaa53b2dd5fe13774e
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