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deed-v01
hanifsajid/deed-v01
deed-v01 is a text classification model from hanifsajid. Use it when you need a label for a piece of text. The card lists the license as mit.
This model is fine-tuned from xlm-roberta-large for multi-class classification of Democratic Erosion Event Dataset (DEED) events. It predicts one of four classes:
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At a glance
- Task
- Text Classification
- License
- mit
- Model type
- xlm-roberta
- Access
- Public
- Created
- Dec 2, 2025
- Updated
- Dec 2, 2025
- SHA
- 73cdc0c9
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Base models
Model card
From the Hugging Face model README
XLM-RoBERTa Large Fine-Tuned for DEED Multi-Class Classification
Model Description
This model is fine-tuned from xlm-roberta-large for multi-class classification of Democratic Erosion Event Dataset (DEED) events.
It predicts one of four classes:
- 0: Destabilizing Event
- 1: Precursor
- 2: Resistance
- 3: Symptom
Intended Use
- Classify DEED-related textual data into four event types.
- Academic research or internal NLP pipelines.
- Not intended for critical real-world decision-making without further validation.
Metrics
| Label | Precision | Recall | F1-Score | Support |
|---|---|---|---|---|
| Destabilizing Event (0) | 0.598 | 0.392 | 0.473 | 125 |
| Precursor (1) | 0.659 | 0.716 | 0.686 | 675 |
| Resistance (2) | 0.846 | 0.845 | 0.846 | 645 |
| Symptom (3) | 0.800 | 0.786 | 0.793 | 805 |
| Accuracy | 0.760 | 0.760 | 0.760 | 2250 |
| Macro Avg | 0.726 | 0.685 | 0.700 | 2250 |
| Weighted Avg | 0.760 | 0.760 | 0.758 | 2250 |
Usage
from transformers import pipeline
classifier = pipeline("text-classification", model="hanifsajid/deed-v01")
result = classifier("Example text here")
print(result)
- Task
- Text Classification
- Type
- xlm-roberta
- License
- mit
- Languages
- en
- Created
- Dec 2, 2025
- Updated
- Dec 2, 2025