Instructions to use connectivity/bert_ft_qqp-10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use connectivity/bert_ft_qqp-10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="connectivity/bert_ft_qqp-10")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("connectivity/bert_ft_qqp-10") model = AutoModelForSequenceClassification.from_pretrained("connectivity/bert_ft_qqp-10", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from connectivity/bert_ft_qqp-10: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/connectivity/bert_ft_qqp-10/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://connectivity/bert_ft_qqp-10/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/connectivity/bert_ft_qqp-10/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- e0ca23ced6c5bc605086505217e5d3253708a5904d579d6ce95d4f510d1323ce
- Size of remote file:
- 438 MB
- SHA256:
- d0547f228cd565dcb5da769c15fb5082aa89f869e40984038e48a713a5910276
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