Instructions to use connectivity/bert_ft_qqp-4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use connectivity/bert_ft_qqp-4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="connectivity/bert_ft_qqp-4")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("connectivity/bert_ft_qqp-4") model = AutoModelForSequenceClassification.from_pretrained("connectivity/bert_ft_qqp-4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from connectivity/bert_ft_qqp-4: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/connectivity/bert_ft_qqp-4/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://connectivity/bert_ft_qqp-4/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/connectivity/bert_ft_qqp-4/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 8d71bf6ce517bf34641034d5549ca0c750f13c5119247e3c5527754df8caf071
- Size of remote file:
- 438 MB
- SHA256:
- fce238cca280b983a825be8442cf66c7b891da441a624c6d135622270ca0c7da
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