Instructions to use lkwate/bert-nsp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lkwate/bert-nsp with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForNextSentencePrediction tokenizer = AutoTokenizer.from_pretrained("lkwate/bert-nsp") model = AutoModelForNextSentencePrediction.from_pretrained("lkwate/bert-nsp", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from lkwate/bert-nsp: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/lkwate/bert-nsp/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://lkwate/bert-nsp/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/lkwate/bert-nsp/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 415a93a86a145e570a684be4195a439bd0802f6e7d7f0c042275cb028af6cf98
- Size of remote file:
- 438 MB
- SHA256:
- ea974733d3ea4a570b6d665b1123e0ee66b4910d3f989d5830971d5fbf2bd88e
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