Instructions to use sinancavdar/Bertwithmaxpoolhead with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sinancavdar/Bertwithmaxpoolhead with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="sinancavdar/Bertwithmaxpoolhead")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sinancavdar/Bertwithmaxpoolhead") model = AutoModel.from_pretrained("sinancavdar/Bertwithmaxpoolhead", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from sinancavdar/Bertwithmaxpoolhead: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/sinancavdar/Bertwithmaxpoolhead/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://sinancavdar/Bertwithmaxpoolhead/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/sinancavdar/Bertwithmaxpoolhead/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 9d561abe33aa19416d3fe7c1b7967527f01ca15b0890ec6fa98dc850c2df628b
- Size of remote file:
- 438 MB
- SHA256:
- 7456edcb01849f5090bca413b690c1f6de14c09563eead2a38475a64ba35feaa
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