Instructions to use harish/AStitchInLanguageModels-Task2_EN_BERTTokenizedNoPreTrain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use harish/AStitchInLanguageModels-Task2_EN_BERTTokenizedNoPreTrain with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="harish/AStitchInLanguageModels-Task2_EN_BERTTokenizedNoPreTrain")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("harish/AStitchInLanguageModels-Task2_EN_BERTTokenizedNoPreTrain") model = AutoModelForMaskedLM.from_pretrained("harish/AStitchInLanguageModels-Task2_EN_BERTTokenizedNoPreTrain", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from harish/AStitchInLanguageModels-Task2_EN_BERTTokenizedNoPreTrain: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/harish/AStitchInLanguageModels-Task2_EN_BERTTokenizedNoPreTrain/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://harish/AStitchInLanguageModels-Task2_EN_BERTTokenizedNoPreTrain/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/harish/AStitchInLanguageModels-Task2_EN_BERTTokenizedNoPreTrain/resolve/main/pytorch_model.bin
433 MB
- Xet hash:
- c9b44e618f7248ce323d9405b0d9f8b3a8accd1d490b82bd7410a727e4267171
- Size of remote file:
- 433 MB
- SHA256:
- c9377fbdc75e1ac1f03a62c26d2d866f8811234e695bc49c6e250b6501bbbfea
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.