Instructions to use deepset/quora_dedup_bert_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepset/quora_dedup_bert_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="deepset/quora_dedup_bert_base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("deepset/quora_dedup_bert_base") model = AutoModel.from_pretrained("deepset/quora_dedup_bert_base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from deepset/quora_dedup_bert_base: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/deepset/quora_dedup_bert_base/resolve/4eee9b697b87658d6a560cb2e94f883eb82a400a/pytorch_model.bin
- Command line
-
hf download hf://deepset/quora_dedup_bert_base@4eee9b697b87658d6a560cb2e94f883eb82a400a/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/deepset/quora_dedup_bert_base/resolve/4eee9b697b87658d6a560cb2e94f883eb82a400a/pytorch_model.bin
438 MB
- Xet hash:
- b6078e401735ca61f53c080337983e2bf1807c13b8e8ee3c54a1a56cc2d66f86
- Size of remote file:
- 438 MB
- SHA256:
- b0dc82a78ffdd829440af87129e063b476de972c98c5f5aafe426b2331878dac
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