Text Classification
Transformers
Safetensors
deberta-v2
finance
central-banking
hawkish-dovish-classification
text-embeddings-inference
Instructions to use LorenzoAleCon29/deberta-v3-ecb-hawkish-dovish with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LorenzoAleCon29/deberta-v3-ecb-hawkish-dovish with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LorenzoAleCon29/deberta-v3-ecb-hawkish-dovish")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LorenzoAleCon29/deberta-v3-ecb-hawkish-dovish") model = AutoModelForSequenceClassification.from_pretrained("LorenzoAleCon29/deberta-v3-ecb-hawkish-dovish", device_map="auto") - Notebooks
- Google Colab
- Kaggle
deberta-v3 - ECB hawkish/dovish/neutral classifier
Fine-tuned from microsoft/deberta-v3-base on annotated ECB communication (press conference
statements, speeches and monetary-policy statements).
Labels: dovish, neutral, hawkish.
Test-set performance
| loss | accuracy | f1 macro | f1 weighted |
|---|---|---|---|
| 2.1243 | 0.4868 | 0.3933 | 0.3991 |
Usage
from transformers import pipeline
clf = pipeline("text-classification", model="LorenzoAleCon29/deberta-v3-ecb-hawkish-dovish")
clf("Inflationary pressures warrant a tightening of the policy stance.")
Training curves, trainer states and full reports: https://github.com/LorenzoAlessandro/BdF-Project
- Downloads last month
- 9
Model tree for LorenzoAleCon29/deberta-v3-ecb-hawkish-dovish
Base model
microsoft/deberta-v3-base