Text Classification
Transformers
PyTorch
Safetensors
Portuguese
Trained with AutoTrain
Eval Results (legacy)
Instructions to use inctdd/told_br_binary_sm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use inctdd/told_br_binary_sm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="inctdd/told_br_binary_sm")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("inctdd/told_br_binary_sm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6df19d25214c0a58ebab57b70b1569d6a7535cff846e60b659ef63e00c40d756
- Size of remote file:
- 678 kB
- SHA256:
- b22b95acf8d863293658d68a3996f22ee077bc792415c976e632049e1e399466
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.