Instructions to use connectivity/bert_ft_qqp-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use connectivity/bert_ft_qqp-3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="connectivity/bert_ft_qqp-3")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("connectivity/bert_ft_qqp-3") model = AutoModelForSequenceClassification.from_pretrained("connectivity/bert_ft_qqp-3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from connectivity/bert_ft_qqp-3: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/connectivity/bert_ft_qqp-3/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://connectivity/bert_ft_qqp-3/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/connectivity/bert_ft_qqp-3/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- a7af9ea48391e62147c54a801a9466c40729d05f8ff81a8119e95c09eaac9b37
- Size of remote file:
- 438 MB
- SHA256:
- 79724e363f31ae5d19977036f5a40764775d46d61a443d3737afe88a42196ff1
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