Instructions to use anwesham/mbert_ur with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anwesham/mbert_ur with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="anwesham/mbert_ur")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("anwesham/mbert_ur") model = AutoModelForTokenClassification.from_pretrained("anwesham/mbert_ur", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from anwesham/mbert_ur: direct link, hf CLI and curl.
- Browser
- Download file 709 MB
-
https://huggingface.co/anwesham/mbert_ur/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://anwesham/mbert_ur/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/anwesham/mbert_ur/resolve/main/pytorch_model.bin
709 MB
- Xet hash:
- 7c7f3f01537e7aade1d8f0d9c93401aa3b324ff5da79c03874b4b24994f416fb
- Size of remote file:
- 709 MB
- SHA256:
- 49e8a5cb13251940c5f03bf70521274badb0015b9438fc831a23153722b000f4
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