Text Classification
Transformers
Safetensors
English
bert
finance
cbdc
central-bank
financial-nlp
economic-policy
monetary-policy
sentence-classification
discourse-analysis
policy-analysis
centralbank-bert
bis-speeches
text-embeddings-inference
Instructions to use bilalzafar/CBDC-Discourse with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bilalzafar/CBDC-Discourse with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bilalzafar/CBDC-Discourse")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bilalzafar/CBDC-Discourse") model = AutoModelForSequenceClassification.from_pretrained("bilalzafar/CBDC-Discourse", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- aefb6364b661d0f1ac2c7c0aace4aa56cae3d37880e6e90b76430545bdbfd387
- Size of remote file:
- 5.84 kB
- SHA256:
- fc2960953bc0c06a8d4975d222d4d92d5386f9e9325832edbf5955e5c0b6de79
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