Spaces:
Running
Running
File size: 2,550 Bytes
db0f499 8ed0852 f4f3763 63a8066 8ed0852 db0f499 6a0b78f f4f3763 6a0b78f 4dfc57d 6a0b78f 09e7c03 6a0b78f 09e7c03 db0f499 8ed0852 63a8066 8ed0852 f4f3763 6a0b78f 8ed0852 6a0b78f f4f3763 e9043bf db0f499 6a0b78f |
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 |
import gradio as gr
from transformers import pipeline
# Define model names
models = {
"ModernBERT Slop Classifier v1": "underscore2/modernbert_base_slop_classifier",
"ModernBERT Slop Classifier v2": "underscore2/modernbert_base_slop_classifier_v2",
"ModernBert Large Slop Classifier v3 (Best accuracy)": "underscore2/modernbert_large_slop_classifier_v3"
}
# Define the mapping for user-friendly labels
# Note: Transformers pipelines often output 'LABEL_0', 'LABEL_1'.
# We handle potential variations like just '0', '1'.
label_map = {
"LABEL_0": "Human (0)",
"0": "Human (0)",
"LABEL_1": "LLM (1)",
"1": "LLM (1)"
}
# Function to load the selected model and classify text
def classify_text(model_name, text):
try:
classifier = pipeline("text-classification", model=models[model_name], top_k=None)
predictions = classifier(text)
# Process predictions to use friendly labels
processed_results = {}
if predictions and isinstance(predictions, list) and predictions[0]:
# predictions[0] should be a list of label dicts like [{'label': 'LABEL_1', 'score': 0.9...}, ...]
for pred in predictions[0]:
raw_label = pred["label"]
score = pred["score"]
# Use the map to get a friendly name, fallback to the raw label if not found
friendly_label = label_map.get(raw_label, raw_label)
processed_results[friendly_label] = score
return processed_results
except Exception as e:
# Handle potential errors during model loading or inference
print(f"Error: {e}")
# Return an error message suitable for gr.Label
return {"Error": f"Failed to process: {e}"}
# Create the Gradio interface
interface = gr.Interface(
fn=classify_text,
inputs=[
gr.Dropdown(
list(models.keys()),
label="Select Model",
value=models[2] # Default model
),
gr.Textbox(
lines=2,
placeholder="Enter text to classify", # Corrected placeholder
value="This is an example sentence." # Changed example text
)
],
# The gr.Label component works well for showing classification scores
outputs=gr.Label(num_top_classes=2), # Show both classes explicitly
title="ModernBERT Slop Classifier",
description="Enter a sentence to see the slop and confidence scores", # Updated description
)
# Launch the app
if __name__ == "__main__":
interface.launch()
|