Text Classification
Transformers
Safetensors
Portuguese
English
bert
aes
Eval Results (legacy)
text-embeddings-inference
Instructions to use kamel-usp/jbcs2025_bert-base-portuguese-cased-encoder_classification-C2-essay_only with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kamel-usp/jbcs2025_bert-base-portuguese-cased-encoder_classification-C2-essay_only with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kamel-usp/jbcs2025_bert-base-portuguese-cased-encoder_classification-C2-essay_only")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kamel-usp/jbcs2025_bert-base-portuguese-cased-encoder_classification-C2-essay_only") model = AutoModelForSequenceClassification.from_pretrained("kamel-usp/jbcs2025_bert-base-portuguese-cased-encoder_classification-C2-essay_only", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fb437f31d538c7b53ff2102289ac17475ccbce968e57ab11dec5df8fbd85f274
- Size of remote file:
- 436 MB
- SHA256:
- 82cf240e9af36a107c9e8ad3f6a630ed12113f2548f820af2331c86eff1d0b44
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