Instructions to use Mesay/Odio-BERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mesay/Odio-BERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Mesay/Odio-BERT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Mesay/Odio-BERT") model = AutoModelForSequenceClassification.from_pretrained("Mesay/Odio-BERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Mesay/Odio-BERT: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/Mesay/Odio-BERT/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Mesay/Odio-BERT/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Mesay/Odio-BERT/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 85c5c79cf26567f6afa1bf274c953621dbdea0b31bf80d7203a47ee9754f71eb
- Size of remote file:
- 438 MB
- SHA256:
- 4a1eef087a59441a6f54938eb2d38b0dae1dd01f8e74b7fce30aafdcb9306c02
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