Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use hucruz/consejo-textcat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hucruz/consejo-textcat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hucruz/consejo-textcat")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hucruz/consejo-textcat") model = AutoModelForSequenceClassification.from_pretrained("hucruz/consejo-textcat", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3d59f57c124ed62e265c65fd6327d6d52d9645913fe400b5f4888a30f372429f
- Size of remote file:
- 269 MB
- SHA256:
- a1b5e767a9fbba08e2ef2185925dd6a134bb5257aecbf8cf08058e939f94181f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.