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0bcc156 | 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 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 | #!/usr/bin/env python3
"""Swedish Causality Detection - HuggingFace Space"""
import gradio as gr
import numpy as np
from sentence_transformers import SentenceTransformer
from sklearn.linear_model import LogisticRegression
from datasets import load_dataset
import pickle
import os
# Global model variables
classifier = None
embedder = None
def load_models():
"""Load or train the causality classifier."""
global classifier, embedder
# Load embedding model
embedder = SentenceTransformer('paraphrase-multilingual-MiniLM-L12-v2')
model_path = "causality_classifier.pkl"
if os.path.exists(model_path):
with open(model_path, 'rb') as f:
classifier = pickle.load(f)
else:
# Train classifier on the dataset
print("Training classifier...")
dataset = load_dataset("UppsalaNLP/swedish-causality-binary")
train_texts = dataset['train']['target_sentence']
train_labels = dataset['train']['label']
# Generate embeddings
train_embeddings = embedder.encode(train_texts, show_progress_bar=True)
# Train logistic regression
classifier = LogisticRegression(max_iter=1000, random_state=42)
classifier.fit(train_embeddings, train_labels)
# Save model
with open(model_path, 'wb') as f:
pickle.dump(classifier, f)
print("Classifier trained and saved!")
def detect_causality(text: str) -> dict:
"""Detect causality in Swedish text."""
if not text.strip():
return {"Causal": 0.0, "Non-causal": 0.0}
# Generate embedding
embedding = embedder.encode([text])
# Get prediction probabilities
probs = classifier.predict_proba(embedding)[0]
return {
"Non-causal": float(probs[0]),
"Causal": float(probs[1])
}
def analyze_text(text: str) -> tuple:
"""Analyze text and return results."""
if not text.strip():
return {}, "Please enter some text to analyze."
# Get causality scores
scores = detect_causality(text)
# Determine result
is_causal = scores["Causal"] > scores["Non-causal"]
confidence = max(scores.values())
if is_causal:
result = f"**Causal relation detected** (confidence: {confidence:.1%})\n\n"
result += "This sentence appears to express a cause-effect relationship."
else:
result = f"**No causal relation detected** (confidence: {confidence:.1%})\n\n"
result += "This sentence does not appear to express a cause-effect relationship."
return scores, result
# Example sentences
EXAMPLES = [
["Den lägre produktiviteten kan bero på att kvinnor har kortare arbetslivserfarenhet än män."],
["Klimatförändringarna leder till ökade havsnivåer och extrema väderhändelser."],
["Sverige är ett land i Nordeuropa med ungefär 10 miljoner invånare."],
["Regeringen presenterade sin budget för nästa år."],
["Bristen på utbildning orsakar hög arbetslöshet bland unga."],
["Stockholm är Sveriges huvudstad och största stad."],
]
# Load models at startup
print("Loading models...")
load_models()
print("Models loaded!")
# Create Gradio interface
with gr.Blocks(title="Swedish Causality Detection", theme=gr.themes.Soft()) as demo:
gr.Markdown("""
# Swedish Causality Detection
Detect causal relations in Swedish text using machine learning.
**Author:** Birger Moëll, Uppsala NLP
This tool classifies whether a Swedish sentence expresses a cause-effect relationship.
The model is trained on the [Swedish Causality Binary Dataset](https://huggingface.co/datasets/UppsalaNLP/swedish-causality-binary).
""")
with gr.Row():
with gr.Column(scale=2):
text_input = gr.Textbox(
label="Swedish Text",
placeholder="Enter a Swedish sentence to analyze...",
lines=3
)
analyze_btn = gr.Button("Analyze", variant="primary")
with gr.Column(scale=1):
label_output = gr.Label(label="Causality Score")
result_output = gr.Markdown(label="Analysis Result")
gr.Examples(
examples=EXAMPLES,
inputs=text_input,
label="Example Sentences (click to try)"
)
analyze_btn.click(
fn=analyze_text,
inputs=text_input,
outputs=[label_output, result_output]
)
text_input.submit(
fn=analyze_text,
inputs=text_input,
outputs=[label_output, result_output]
)
gr.Markdown("""
---
## About
This classifier uses sentence embeddings from `paraphrase-multilingual-MiniLM-L12-v2`
and logistic regression trained on Swedish government reports annotated for causality.
**Dataset:** [UppsalaNLP/swedish-causality-binary](https://huggingface.co/datasets/UppsalaNLP/swedish-causality-binary)
**Citation:**
```
Dürlich et al. (2022). Cause and Effect in Governmental Reports:
Two Data Sets for Causality Detection in Swedish.
```
**Links:**
- [Uppsala NLP](https://huggingface.co/UppsalaNLP)
- [GitHub Repository](https://github.com/UppsalaNLP/Swedish-Causality-Datasets)
""")
if __name__ == "__main__":
demo.launch()
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