Text Classification
Transformers
Safetensors
English
bert
CBDC
Central Bank Digital Currencies
Central Bank Digital Currency
Sentiment Analysis
Central Bank
Tone
Finance
NLP
Finance NLP
BERT
Transformers
Digital Currency
text-embeddings-inference
Instructions to use bilalzafar/CBDC-Sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bilalzafar/CBDC-Sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bilalzafar/CBDC-Sentiment")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bilalzafar/CBDC-Sentiment") model = AutoModelForSequenceClassification.from_pretrained("bilalzafar/CBDC-Sentiment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Update README.md
Verified signaturea4d9da4
-
Update README.md
Verified signature8c7b9ad
-
Update README.md
Verified signature56b0e95 -
Model file
Verified signatureae2a1d5
-
Update README.md
Verified signature018f60d -
Update README.md
Verified signaturec01211c -
Update README.md
Verified signature902bf73 -
Update README.md
Verified signature407ba84
-
Update README.md
Verified signature8509851 -
Update README.md
Verified signature2cda2db -
Update README.md
Verified signatured1651dd -
Update README.md
Verified signatured67e401 -
Update README.md
Verified signature741d011 -
Update README.md
Verified signaturee586797 -
initial commit
Verified signature61db00e