Instructions to use xeventminer/mbert-en-pr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xeventminer/mbert-en-pr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="xeventminer/mbert-en-pr")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("xeventminer/mbert-en-pr") model = AutoModelForSequenceClassification.from_pretrained("xeventminer/mbert-en-pr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from xeventminer/mbert-en-pr: direct link, hf CLI and curl.
- Browser
- Download file 712 MB
-
https://huggingface.co/xeventminer/mbert-en-pr/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://xeventminer/mbert-en-pr/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/xeventminer/mbert-en-pr/resolve/main/pytorch_model.bin
712 MB
- Xet hash:
- 0148d073cd4551de64e69d5856cf29e1a37d3b54feb686dabc4a2756f87f7a45
- Size of remote file:
- 712 MB
- SHA256:
- 667ebd121c4e8e93bf155f29b5a9312e1eb0752b030c2765f20db265c032480a
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