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
- Xet hash:
- 66244d94d7c94f1a5a53feb60dc8df63c01ce972a387b24939ecb2219239e4f7
- Size of remote file:
- 670 MB
- SHA256:
- 8e975a4a288b4d8699e53fb96565e78386efde627dfe28f28914d47d074b1966
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