Instructions to use muhtasham/TajBERTo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use muhtasham/TajBERTo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="muhtasham/TajBERTo")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("muhtasham/TajBERTo") model = AutoModelForMaskedLM.from_pretrained("muhtasham/TajBERTo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from muhtasham/TajBERTo: direct link, hf CLI and curl.
- Browser
- Download file 334 MB
-
https://huggingface.co/muhtasham/TajBERTo/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://muhtasham/TajBERTo/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/muhtasham/TajBERTo/resolve/main/pytorch_model.bin
334 MB
- Xet hash:
- be0c141928f261ed526182e2d34b5afcf22f3479b38cd54145a7396e2c9fe975
- Size of remote file:
- 334 MB
- SHA256:
- 45b82e72e1325d118a9aef84a76a535a463d11a0fd2c205aae0c1c3d8f53960c
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