Instructions to use connectivity/bert_ft_qqp-30 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use connectivity/bert_ft_qqp-30 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="connectivity/bert_ft_qqp-30")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("connectivity/bert_ft_qqp-30") model = AutoModelForSequenceClassification.from_pretrained("connectivity/bert_ft_qqp-30", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from connectivity/bert_ft_qqp-30: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/connectivity/bert_ft_qqp-30/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://connectivity/bert_ft_qqp-30/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/connectivity/bert_ft_qqp-30/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 45aa156df2fcbd77fe0091121e4f0f4e725109413afe6acffd8ba66b504dd58f
- Size of remote file:
- 438 MB
- SHA256:
- cd394bb960bbfa785428f84b677df2eb1d29cd06974383150ce87927344bfc06
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.