Text Classification
Transformers
Safetensors
Portuguese
English
deberta-v2
aes
Eval Results (legacy)
text-embeddings-inference
Instructions to use kamel-usp/jbcs2025_albertina-1b5-portuguese-ptbr-encoder-encoder_classification-C4-essay_only with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kamel-usp/jbcs2025_albertina-1b5-portuguese-ptbr-encoder-encoder_classification-C4-essay_only with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kamel-usp/jbcs2025_albertina-1b5-portuguese-ptbr-encoder-encoder_classification-C4-essay_only")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kamel-usp/jbcs2025_albertina-1b5-portuguese-ptbr-encoder-encoder_classification-C4-essay_only") model = AutoModelForSequenceClassification.from_pretrained("kamel-usp/jbcs2025_albertina-1b5-portuguese-ptbr-encoder-encoder_classification-C4-essay_only", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1c900e617fabf740dcd2810ce7e008a67f1cf2e2e6a484049322d716b7024a25
- Size of remote file:
- 3.13 GB
- SHA256:
- afa763134fcada2c7cab056fef868e8be0370849e6a4fc0b9082b80d6048b751
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