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")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bilalzafar/CBDC-Sentiment") model = AutoModelForSequenceClassification.from_pretrained("bilalzafar/CBDC-Sentiment") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6052afd0a4aa8f3918229f837cc9bc349f95537a3cc0dd82d52f9fc5d376d762
- Size of remote file:
- 5.43 kB
- SHA256:
- 4ac99b78b788aade1c0f2545903ae3e45369ca46aeac54cfee5e281ea6d25e99
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