Instructions to use connectivity/bert_ft_qqp-34 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use connectivity/bert_ft_qqp-34 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="connectivity/bert_ft_qqp-34")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("connectivity/bert_ft_qqp-34") model = AutoModelForSequenceClassification.from_pretrained("connectivity/bert_ft_qqp-34", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from connectivity/bert_ft_qqp-34: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/connectivity/bert_ft_qqp-34/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://connectivity/bert_ft_qqp-34/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/connectivity/bert_ft_qqp-34/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- f08fe341fc61db8634ef57930ffa78b241d8cbdccd6258f41c4365c8cc463eb4
- Size of remote file:
- 438 MB
- SHA256:
- ba8b0e2087291d72cfbe64e72b33791b884268ba6853b12aa6087e0b8e6a27a6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.