Instructions to use connectivity/bert_ft_qqp-38 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use connectivity/bert_ft_qqp-38 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="connectivity/bert_ft_qqp-38")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("connectivity/bert_ft_qqp-38") model = AutoModelForSequenceClassification.from_pretrained("connectivity/bert_ft_qqp-38", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from connectivity/bert_ft_qqp-38: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/connectivity/bert_ft_qqp-38/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://connectivity/bert_ft_qqp-38/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/connectivity/bert_ft_qqp-38/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- c485ddf3d05574f99747d0c186a8ee2d6de63df54619511ae303de0fa14975e8
- Size of remote file:
- 438 MB
- SHA256:
- 52c1b1e907d25b7811b743a0cbe077ea216a30f0191086321f0d55be5c515e10
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.