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:
- fffe1f6c5b28a23fb59cc315bc68a5613906194de9fbe4e7e22448f21beb1bbe
- Size of remote file:
- 1.06 kB
- SHA256:
- 0a6c1b7857b3c0366487f81785fcbbe7c765e96aafca73bc91e3b556009cc43b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.