Spaces:
Running
Running
File size: 11,906 Bytes
0f03dd5 379daf7 0f03dd5 a1ce4b0 0f03dd5 1afd410 c0eef1e 1afd410 c0eef1e 1afd410 c0eef1e 0f03dd5 c0eef1e 0f03dd5 1afd410 0f03dd5 1afd410 0f03dd5 c0eef1e 5ad87e4 1afd410 c0eef1e 1afd410 c0eef1e 1afd410 0f03dd5 1afd410 0f03dd5 1afd410 0f03dd5 1afd410 c0eef1e 0f03dd5 1afd410 0f03dd5 a1ce4b0 0f03dd5 1afd410 0f03dd5 1afd410 a1ce4b0 1afd410 0f03dd5 1afd410 8663fbd c0eef1e 1afd410 0f03dd5 1afd410 0f03dd5 1afd410 0f03dd5 1afd410 0f03dd5 a1ce4b0 0f03dd5 1afd410 8663fbd c0eef1e 1afd410 0f03dd5 1afd410 0f03dd5 1afd410 0f03dd5 a1ce4b0 1afd410 a1ce4b0 0f03dd5 1afd410 0f03dd5 1afd410 0f03dd5 1afd410 0f03dd5 a1ce4b0 0f03dd5 a1ce4b0 0f03dd5 1afd410 0f03dd5 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 |
import os
import json
import numpy as np
from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForQuestionAnswering, pipeline
import torch
from sklearn.metrics import f1_score
import re
from collections import Counter
import string
from huggingface_hub import login
import gradio as gr
import pandas as pd
from datetime import datetime
def normalize_answer(s):
"""Normalize answer for evaluation"""
def remove_articles(text):
return re.sub(r'\b(a|an|the)\b', ' ', text)
def white_space_fix(text):
return ' '.join(text.split())
def remove_punc(text):
exclude = set(string.punctuation)
return ''.join(ch for ch in text if ch not in exclude)
def lower(text):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(s))))
def f1_score_qa(prediction, ground_truth):
"""Calculate F1 score for QA"""
prediction_tokens = normalize_answer(prediction).split()
ground_truth_tokens = normalize_answer(ground_truth).split()
if len(prediction_tokens) == 0 or len(ground_truth_tokens) == 0:
return int(prediction_tokens == ground_truth_tokens)
common = Counter(prediction_tokens) & Counter(ground_truth_tokens)
num_same = sum(common.values())
if num_same == 0:
return 0
precision = 1.0 * num_same / len(prediction_tokens)
recall = 1.0 * num_same / len(ground_truth_tokens)
f1 = (2 * precision * recall) / (precision + recall)
return f1
def exact_match_score(prediction, ground_truth):
"""Calculate exact match score"""
return normalize_answer(prediction) == normalize_answer(ground_truth)
def evaluate_model():
# Authenticate with Hugging Face using the token
hf_token = os.getenv("EVAL_TOKEN")
if hf_token:
try:
login(token=hf_token)
print("β Authenticated with Hugging Face")
except Exception as e:
print(f"β Warning: Could not authenticate with HF token: {e}")
else:
print("β Warning: EVAL_TOKEN not found in environment variables")
print("Loading model and tokenizer...")
model_name = "AvocadoMuffin/roberta-cuad-qa-v2"
try:
tokenizer = AutoTokenizer.from_pretrained(model_name, token=hf_token)
model = AutoModelForQuestionAnswering.from_pretrained(model_name, token=hf_token)
qa_pipeline = pipeline("question-answering", model=model, tokenizer=tokenizer)
print("β Model loaded successfully")
return qa_pipeline, hf_token
except Exception as e:
print(f"β Error loading model: {e}")
return None, None
def run_evaluation(num_samples, progress=gr.Progress()):
"""Run evaluation and return results for Gradio interface"""
# Load model
qa_pipeline, hf_token = evaluate_model()
if qa_pipeline is None:
return "β Failed to load model", pd.DataFrame(), None
progress(0.1, desc="Loading CUAD dataset...")
# Load dataset - use QA format version (JSON, no PDFs)
try:
# Try the QA-specific version first (much faster, JSON format)
dataset = load_dataset("theatticusproject/cuad-qa", trust_remote_code=True, token=hf_token)
test_data = dataset["test"]
print(f"β Loaded CUAD-QA dataset with {len(test_data)} samples")
except Exception as e:
try:
# Fallback to original but limit to avoid PDF downloads
dataset = load_dataset("cuad", split="test[:1000]", trust_remote_code=True, token=hf_token)
test_data = dataset
print(f"β Loaded CUAD dataset with {len(test_data)} samples")
except Exception as e2:
return f"β Error loading dataset: {e2}", pd.DataFrame(), None
# Limit samples
num_samples = min(num_samples, len(test_data))
test_subset = test_data.select(range(num_samples))
progress(0.2, desc=f"Starting evaluation on {num_samples} samples...")
# Initialize metrics
exact_matches = []
f1_scores = []
predictions = []
# Run evaluation
for i, example in enumerate(test_subset):
progress((0.2 + 0.7 * i / num_samples), desc=f"Processing sample {i+1}/{num_samples}")
try:
context = example["context"]
question = example["question"]
answers = example["answers"]
# Get model prediction
result = qa_pipeline(question=question, context=context)
predicted_answer = result["answer"]
# Get ground truth answers
if answers["text"] and len(answers["text"]) > 0:
ground_truth = answers["text"][0] if isinstance(answers["text"], list) else answers["text"]
else:
ground_truth = ""
# Calculate metrics
em = exact_match_score(predicted_answer, ground_truth)
f1 = f1_score_qa(predicted_answer, ground_truth)
exact_matches.append(em)
f1_scores.append(f1)
predictions.append({
"Sample_ID": i+1,
"Question": question[:100] + "..." if len(question) > 100 else question,
"Predicted_Answer": predicted_answer,
"Ground_Truth": ground_truth,
"Exact_Match": em,
"F1_Score": round(f1, 3),
"Confidence": round(result["score"], 3)
})
except Exception as e:
print(f"Error processing sample {i}: {e}")
continue
progress(0.9, desc="Calculating final metrics...")
# Calculate final metrics
if len(exact_matches) == 0:
return "β No samples were successfully processed", pd.DataFrame(), None
avg_exact_match = np.mean(exact_matches) * 100
avg_f1_score = np.mean(f1_scores) * 100
# Create results summary
results_summary = f"""
# π CUAD Model Evaluation Results
## π― Overall Performance
- **Model**: AvocadoMuffin/roberta-cuad-qa-v3
- **Dataset**: CUAD (Contract Understanding Atticus Dataset)
- **Samples Evaluated**: {len(exact_matches)}
- **Evaluation Date**: {datetime.now().strftime("%Y-%m-%d %H:%M:%S")}
## π Metrics
- **Exact Match Score**: {avg_exact_match:.2f}%
- **F1 Score**: {avg_f1_score:.2f}%
## π Performance Analysis
- **High Confidence Predictions**: {len([p for p in predictions if p['Confidence'] > 0.8])} ({len([p for p in predictions if p['Confidence'] > 0.8])/len(predictions)*100:.1f}%)
- **Perfect Matches**: {len([p for p in predictions if p['Exact_Match'] == 1])} ({len([p for p in predictions if p['Exact_Match'] == 1])/len(predictions)*100:.1f}%)
- **High F1 Scores (>0.8)**: {len([p for p in predictions if p['F1_Score'] > 0.8])} ({len([p for p in predictions if p['F1_Score'] > 0.8])/len(predictions)*100:.1f}%)
"""
# Create detailed results DataFrame
df = pd.DataFrame(predictions)
# Save results to file
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
results_file = f"cuad_evaluation_results_{timestamp}.json"
detailed_results = {
"model_name": "AvocadoMuffin/roberta-cuad-qa-v3",
"dataset": "cuad",
"num_samples": len(exact_matches),
"exact_match_score": avg_exact_match,
"f1_score": avg_f1_score,
"evaluation_date": datetime.now().isoformat(),
"predictions": predictions
}
try:
with open(results_file, "w") as f:
json.dump(detailed_results, f, indent=2)
print(f"β Results saved to {results_file}")
except Exception as e:
print(f"β Warning: Could not save results file: {e}")
results_file = None
progress(1.0, desc="β
Evaluation completed!")
return results_summary, df, results_file
def create_gradio_interface():
"""Create Gradio interface for CUAD evaluation"""
with gr.Blocks(title="CUAD Model Evaluator", theme=gr.themes.Soft()) as demo:
gr.HTML("""
<div style="text-align: center; padding: 20px;">
<h1>ποΈ CUAD Model Evaluation Dashboard</h1>
<p>Evaluate your CUAD (Contract Understanding Atticus Dataset) Question Answering model</p>
<p><strong>Model:</strong> AvocadoMuffin/roberta-cuad-qa-v2</p>
</div>
""")
with gr.Row():
with gr.Column(scale=1):
gr.HTML("<h3>βοΈ Evaluation Settings</h3>")
num_samples = gr.Slider(
minimum=10,
maximum=500,
value=100,
step=10,
label="Number of samples to evaluate",
info="Choose between 10-500 samples (more samples = more accurate but slower)"
)
evaluate_btn = gr.Button(
"π Start Evaluation",
variant="primary",
size="lg"
)
gr.HTML("""
<div style="margin-top: 20px; padding: 15px; background-color: #f0f0f0; border-radius: 8px;">
<h4>π What this evaluates:</h4>
<ul>
<li><strong>Exact Match</strong>: Percentage of perfect predictions</li>
<li><strong>F1 Score</strong>: Token-level overlap between prediction and ground truth</li>
<li><strong>Confidence</strong>: Model's confidence in its predictions</li>
</ul>
</div>
""")
with gr.Column(scale=2):
gr.HTML("<h3>π Results</h3>")
results_summary = gr.Markdown(
value="Click 'π Start Evaluation' to begin...",
label="Evaluation Summary"
)
gr.HTML("<hr>")
with gr.Row():
gr.HTML("<h3>π Detailed Results</h3>")
with gr.Row():
detailed_results = gr.Dataframe(
label="Sample-by-Sample Results",
interactive=False,
wrap=True
)
with gr.Row():
download_file = gr.File(
label="π₯ Download Complete Results (JSON)",
visible=False
)
# Event handlers
def handle_evaluation(num_samples):
summary, df, file_path = run_evaluation(num_samples)
if file_path and os.path.exists(file_path):
return summary, df, gr.update(visible=True, value=file_path)
else:
return summary, df, gr.update(visible=False)
evaluate_btn.click(
fn=handle_evaluation,
inputs=[num_samples],
outputs=[results_summary, detailed_results, download_file],
show_progress=True
)
# Footer
gr.HTML("""
<div style="text-align: center; margin-top: 30px; padding: 20px; color: #666;">
<p>π€ Powered by Hugging Face Transformers & Gradio</p>
<p>π CUAD Dataset by The Atticus Project</p>
</div>
""")
return demo
if __name__ == "__main__":
print("CUAD Model Evaluation with Gradio Interface")
print("=" * 50)
# Check if CUDA is available
if torch.cuda.is_available():
print(f"β CUDA available: {torch.cuda.get_device_name(0)}")
else:
print("! Running on CPU")
# Create and launch Gradio interface
demo = create_gradio_interface()
demo.launch(
server_name="0.0.0.0",
server_port=7860,
share=True,
debug=True
) |