File size: 1,519 Bytes
3203a45 4d20550 6acc0a9 4d20550 6acc0a9 4d20550 6acc0a9 4d20550 6acc0a9 4d20550 6acc0a9 4d20550 6acc0a9 9f48230 6acc0a9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 |
---
license: mit
---
## CentralBankRoBERTa
CentralBankRoBERTA is a large language model. It combines an economic agent classifier that distinguishes five basic macroeconomic agents with a binary sentiment classifier that identifies the emotional content of sentences in central bank communications.
#### Overview
The AudienceClassifier model is designed to classify the target audience of a given text. It can determine whether the text is adressing households, firms, the financial sector, the government or the central bank itself. This model is based on a state-of-the-art deep learning architecture and has been fine-tuned on a diverse and extensive dataset to provide accurate predictions.
#### Intended Use
The AudienceClassifier model is intended to be used in various applications where content categorization based on target audiences is essential.
#### Performance
- Accuracy: 93%
- F1 Score: 0.93
- Precision: 0.93
- Recall: 0.93
### Usage
You can use these models in your own applications by leveraging the Hugging Face Transformers library. Below is a Python code snippet demonstrating how to load and use the AudienceClassifier model:
```python
from transformers import pipeline
# Load the AudienceClassifier model
audience_classifier = pipeline("text-classification", model="Moritz-Pfeifer/CentralBankRoBERTa-audience-classifier")
# Perform audience classification
audience_result = audience_classifier("Your text goes here.")
print("Audience Classification:", audience_result[0]['label'])
|