Instructions to use neuropark/sahajBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use neuropark/sahajBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="neuropark/sahajBERT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForPreTraining tokenizer = AutoTokenizer.from_pretrained("neuropark/sahajBERT") model = AutoModelForPreTraining.from_pretrained("neuropark/sahajBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from neuropark/sahajBERT: direct link, hf CLI and curl.
- Browser
- Download file 72.4 MB
-
https://huggingface.co/neuropark/sahajBERT/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://neuropark/sahajBERT/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/neuropark/sahajBERT/resolve/main/pytorch_model.bin
72.4 MB
- Xet hash:
- 80756dbf23bcacc0db57c58af715aaa88135ce4d517fa51efa67507266e96e50
- Size of remote file:
- 72.4 MB
- SHA256:
- c848a508b0a103cba584b8f88eb71a8636b3fdbe4f10275291de9b8804e4a9c9
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