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")# 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
- Xet hash:
- 85c5c79cf26567f6afa1bf274c953621dbdea0b31bf80d7203a47ee9754f71eb
- Size of remote file:
- 438 MB
- SHA256:
- 4a1eef087a59441a6f54938eb2d38b0dae1dd01f8e74b7fce30aafdcb9306c02
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.