Instructions to use connectivity/bert_ft_qqp-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use connectivity/bert_ft_qqp-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="connectivity/bert_ft_qqp-2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("connectivity/bert_ft_qqp-2") model = AutoModelForSequenceClassification.from_pretrained("connectivity/bert_ft_qqp-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from connectivity/bert_ft_qqp-2: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/connectivity/bert_ft_qqp-2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://connectivity/bert_ft_qqp-2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/connectivity/bert_ft_qqp-2/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 6b9d62b48adceb5f4ffd45931750db3a6f4fa0af9ba330baa1cc2f7fe7f64575
- Size of remote file:
- 438 MB
- SHA256:
- ae412c3388eb383d0bea8586d14e55e9687789526a9c204693ee5348d997ee1f
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