Text Classification
Transformers
PyTorch
bert
CAP
politics
issues
agenda
multilingual
science
comparative agendas project
text-embeddings-inference
Instructions to use z-dickson/CAP_multilingual with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use z-dickson/CAP_multilingual with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="z-dickson/CAP_multilingual")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("z-dickson/CAP_multilingual") model = AutoModelForSequenceClassification.from_pretrained("z-dickson/CAP_multilingual") - Notebooks
- Google Colab
- Kaggle
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- comparative agendas project
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Multilingual Bert base (multilingual uncased) model trained to predict [CAP issue codes](https://www.comparativeagendas.net/pages/master-codebook).
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Model training on 120,000 assorted political documents -- mostly from the [Comparative Agendas Project](https://www.comparativeagendas.net/)
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- comparative agendas project
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Multilingual Bert base (multilingual uncased) model trained to predict [CAP issue codes](https://www.comparativeagendas.net/pages/master-codebook) from text documents such as speeches, press releases, social media messages, news articles, bills, laws etc..
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Model training on 120,000 assorted political documents -- mostly from the [Comparative Agendas Project](https://www.comparativeagendas.net/)
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