Text Classification
Transformers
Safetensors
roberta
finance
central-banking
hawkish-dovish-classification
text-embeddings-inference
Instructions to use LorenzoAleCon29/roberta-base-ecb-hawkish-dovish with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LorenzoAleCon29/roberta-base-ecb-hawkish-dovish with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LorenzoAleCon29/roberta-base-ecb-hawkish-dovish")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LorenzoAleCon29/roberta-base-ecb-hawkish-dovish") model = AutoModelForSequenceClassification.from_pretrained("LorenzoAleCon29/roberta-base-ecb-hawkish-dovish", device_map="auto") - Notebooks
- Google Colab
- Kaggle
roberta-base - ECB hawkish/dovish/neutral classifier
Fine-tuned from roberta-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 |
|---|---|---|---|
| 2.0651 | 0.5984 | 0.5983 | 0.5981 |
Usage
from transformers import pipeline
clf = pipeline("text-classification", model="LorenzoAleCon29/roberta-base-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
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Model tree for LorenzoAleCon29/roberta-base-ecb-hawkish-dovish
Base model
FacebookAI/roberta-base