Instructions to use connectivity/bert_ft_qqp-95 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use connectivity/bert_ft_qqp-95 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="connectivity/bert_ft_qqp-95")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("connectivity/bert_ft_qqp-95") model = AutoModelForSequenceClassification.from_pretrained("connectivity/bert_ft_qqp-95", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from connectivity/bert_ft_qqp-95: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/connectivity/bert_ft_qqp-95/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://connectivity/bert_ft_qqp-95/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/connectivity/bert_ft_qqp-95/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- d518d3865991a6b0bcd599bbb74a487dbfa4cdf2f1df707e859708bbfe794705
- Size of remote file:
- 438 MB
- SHA256:
- 5e373c76697865df8275c9c70450f26c776613cf9d5d43fd45d6a9472e171ebe
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.