Text Classification
Transformers
Safetensors
Portuguese
English
bert
aes
Eval Results (legacy)
text-embeddings-inference
Instructions to use kamel-usp/jbcs2025_BERTugues-base-portuguese-cased-encoder_classification-C3-essay_only with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kamel-usp/jbcs2025_BERTugues-base-portuguese-cased-encoder_classification-C3-essay_only with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kamel-usp/jbcs2025_BERTugues-base-portuguese-cased-encoder_classification-C3-essay_only")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kamel-usp/jbcs2025_BERTugues-base-portuguese-cased-encoder_classification-C3-essay_only") model = AutoModelForSequenceClassification.from_pretrained("kamel-usp/jbcs2025_BERTugues-base-portuguese-cased-encoder_classification-C3-essay_only", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e5788d332741c2799f32106c40545d44ffee4785838882f9ba91d06e94711cec
- Size of remote file:
- 5.78 kB
- SHA256:
- 7183af4c50ca73d77ae579628b4cbec27142099763d131f4334b1614758f944a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.