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vtt-qsts-ai/multilabel-indicator-classification-roberta-large

sourceHugging Faceapache-2.0updated 8mo agoView on Hugging Face

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Model Card

RoBERTa for Multi-label Classification of Policy Instruments

This model fine-tunes roberta-large for multilabel classification of policies, targets, and themes.

Model Details

—Base model: roberta-large
—Max length: 512
—Output: 67 multilabel classes (PI - Policy Instrument, TG - Target Group, TH - Theme). There are three main classes that have further sub-categories in them.
—Threshold: 0.25

Intended Use

Classify policy documents or government program descriptions into thematic categories.

How to Use

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import numpy as np
import joblib
import requests

model_path = "vtt-qsts-ai/multilabel-indicator-classification-roberta-large"
model = AutoModelForSequenceClassification.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)

mlb_url = "https://huggingface.co/vtt-qsts-ai/multilabel-indicator-classification-roberta-large/resolve/main/mlb.pkl"
mlb_path = "mlb.pkl"

with open(mlb_path, "wb") as f:
    f.write(requests.get(mlb_url).content)
mlb = joblib.load(mlb_path)

text = "This program supports clean technology and sustainable development in industries."

inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)

model.eval()
with torch.no_grad():
    logits = model(**inputs).logits
    probs = torch.sigmoid(logits).squeeze().numpy()

# Threshold
binary_preds = (probs > 0.25).astype(int)
predicted_labels = [label for i, label in enumerate(mlb.classes_) if binary_preds[i] == 1]

print("Predicted Labels:", predicted_labels)
# Predicted Labels: ['PI008', 'TG20', 'TG21', 'TG22', 'TG25', 'TG29', 'TH31', 'TH92']