Instructions to use connectivity/bert_ft_qqp-28 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use connectivity/bert_ft_qqp-28 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="connectivity/bert_ft_qqp-28")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("connectivity/bert_ft_qqp-28") model = AutoModelForSequenceClassification.from_pretrained("connectivity/bert_ft_qqp-28", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from connectivity/bert_ft_qqp-28: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/connectivity/bert_ft_qqp-28/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://connectivity/bert_ft_qqp-28/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/connectivity/bert_ft_qqp-28/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 040a0d59986eb674ac1a9e0ea84030f12b31fb275379f1f53bb245b24c4e0604
- Size of remote file:
- 438 MB
- SHA256:
- a5cff0cae345902775e151b8a507cd5b981e631268ac25fd6a4f132e58a913d9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.