Text Classification
Transformers
Safetensors
English
bert
finance
multi-label
optuna
classification
social-science
communication-science
Instructions to use nurdyansa/framing-bert-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nurdyansa/framing-bert-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nurdyansa/framing-bert-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nurdyansa/framing-bert-model") model = AutoModelForSequenceClassification.from_pretrained("nurdyansa/framing-bert-model") - Notebooks
- Google Colab
- Kaggle
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- Improving label consistency or adding nuance.
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- Suggesting improvements to model architecture or training methods.
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- Improving label consistency or adding nuance.
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If you are interested in collaborating, sharing insights, or further developing this model, feel free to reach out:
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📧 **Email**: nurdyansa@gmail.com
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