Instructions to use connectivity/bert_ft_qqp-18 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use connectivity/bert_ft_qqp-18 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="connectivity/bert_ft_qqp-18")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("connectivity/bert_ft_qqp-18") model = AutoModelForSequenceClassification.from_pretrained("connectivity/bert_ft_qqp-18", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from connectivity/bert_ft_qqp-18: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/connectivity/bert_ft_qqp-18/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://connectivity/bert_ft_qqp-18/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/connectivity/bert_ft_qqp-18/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- e1fe363929db5ac4b41c249bec0852214a0ff37a83309bada0ac22d821ae4e0d
- Size of remote file:
- 438 MB
- SHA256:
- dc7f0036497f2df8c6207dc156d806f8f71187f1d4219865cb1b7553f9ddc15a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.