Text Classification
Transformers
Safetensors
modernbert
feature-extraction
eurovoc
multi-label
multilingual
mmbert
text-embeddings-inference
Instructions to use EuropeanParliament/EuroVoc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EuropeanParliament/EuroVoc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="EuropeanParliament/EuroVoc")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("EuropeanParliament/EuroVoc") model = AutoModel.from_pretrained("EuropeanParliament/EuroVoc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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predict.py: skip content-free input (empty/zero-width/emoji) to prevent unrelated tags on empty input (#3)
- predict.py: skip content-free input (empty/zero-width/emoji) to prevent unrelated tags on empty input (d899566c81cce70441b33c5a0a5a8e2016249d37) Co-authored-by: Harun <Prgrmmer@users.noreply.huggingface.co>eea3ce4
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Multilingual mmBERT EuroVoc tagger (supersedes English RoBERTa) (#2)
- Multilingual mmBERT EuroVoc tagger (supersedes English RoBERTa) (52019f57d44a21e7dfb03634dd81d3d8ba6a38c4) Co-authored-by: Harun <Prgrmmer@users.noreply.huggingface.co>640f88e
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