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")# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from inesctec/CitiLink-BERTimbau-large-Topic-Classification-pt: direct link, hf CLI and curl.
- Browser
- Download file 678 kB
-
https://huggingface.co/inesctec/CitiLink-BERTimbau-large-Topic-Classification-pt/resolve/main/tokenizer.json
- Command line
-
hf download hf://inesctec/CitiLink-BERTimbau-large-Topic-Classification-pt/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/inesctec/CitiLink-BERTimbau-large-Topic-Classification-pt/resolve/main/tokenizer.json
678 kB
File too large to display, you can check the raw version instead.