Instructions to use nikitam/mbert-xdm-en-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nikitam/mbert-xdm-en-it with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="nikitam/mbert-xdm-en-it")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("nikitam/mbert-xdm-en-it") model = AutoModelForMaskedLM.from_pretrained("nikitam/mbert-xdm-en-it", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from nikitam/mbert-xdm-en-it: direct link, hf CLI and curl.
- Browser
- Download file 670 MB
-
https://huggingface.co/nikitam/mbert-xdm-en-it/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://nikitam/mbert-xdm-en-it/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/nikitam/mbert-xdm-en-it/resolve/main/pytorch_model.bin
670 MB
- Xet hash:
- b1009626c2379427810129e3a4a568e851f3a5ad8b2cd9c5310adf876b9f2c08
- Size of remote file:
- 670 MB
- SHA256:
- 23025dec0c5762ae7a9b1058600d53af9cda77777bbd42e7137856d774e43e3e
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