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
Adding `safetensors` variant of this model
#5 opened 18 days ago
by
SFconvertbot
Adding `safetensors` variant of this model
#4 opened 6 months ago
by
SFconvertbot
Adding `safetensors` variant of this model
#3 opened 11 months ago
by
SFconvertbot
Adding `safetensors` variant of this model
#2 opened 12 months ago
by
SFconvertbot
Adding `safetensors` variant of this model
#1 opened over 1 year ago
by
SFconvertbot