Instructions to use connectivity/bert_ft_qqp-32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use connectivity/bert_ft_qqp-32 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="connectivity/bert_ft_qqp-32")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("connectivity/bert_ft_qqp-32") model = AutoModelForSequenceClassification.from_pretrained("connectivity/bert_ft_qqp-32", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from connectivity/bert_ft_qqp-32: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/connectivity/bert_ft_qqp-32/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://connectivity/bert_ft_qqp-32/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/connectivity/bert_ft_qqp-32/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 8c314204d878419d19eef66d9b3c5244183e563e40351aba29125e652f77733f
- Size of remote file:
- 438 MB
- SHA256:
- 40a5d07db53d900bf1440cba1a6c0772d0bfd8a558a38b640a6178e0f187ce8a
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