Instructions to use connectivity/bert_ft_qqp-7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use connectivity/bert_ft_qqp-7 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="connectivity/bert_ft_qqp-7")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("connectivity/bert_ft_qqp-7") model = AutoModelForSequenceClassification.from_pretrained("connectivity/bert_ft_qqp-7", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from connectivity/bert_ft_qqp-7: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/connectivity/bert_ft_qqp-7/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://connectivity/bert_ft_qqp-7/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/connectivity/bert_ft_qqp-7/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 45df8241ebba4dc2039843a5e7d423003d11f6284a9313250542ad8f1a687bb0
- Size of remote file:
- 438 MB
- SHA256:
- 5357cf097751df1fa64a884f7fbff7380c496cfb972a7cb9176b0302bc1466dd
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