Text Classification
Transformers
Joblib
Safetensors
Portuguese
bert
multi-label-classification
bertimbau
portuguese
municipal-documents
meeting-minutes
fine-tuned
text-embeddings-inference
Instructions to use inesctec/CitiLink-BERTimbau-large-Topic-Classification-pt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use inesctec/CitiLink-BERTimbau-large-Topic-Classification-pt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="inesctec/CitiLink-BERTimbau-large-Topic-Classification-pt")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("inesctec/CitiLink-BERTimbau-large-Topic-Classification-pt") model = AutoModelForSequenceClassification.from_pretrained("inesctec/CitiLink-BERTimbau-large-Topic-Classification-pt") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 34791b8df22d1329d64c86272d1c85b2f808efdba70eb61e95a9c27d2e5bde34
- Size of remote file:
- 5.71 kB
- SHA256:
- 89cd745932f93d49b54e107e96c1d65e80a44fe4e230694f14f23867d70156a2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.