Spaces:
Runtime error
Runtime error
File size: 12,304 Bytes
c4aca3b |
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 |
def validate_dataset(self, file_path, format_type):
"""
Validate and analyze the dataset format, providing detailed feedback
Parameters:
file_path (str): Path to the dataset file
format_type (str): File format (csv, jsonl, text)
Returns:
dict: Validation results including format, structure, and statistics
"""
import pandas as pd
import json
import os
import re
validation_results = {
"is_valid": False,
"format": format_type,
"detected_structure": None,
"statistics": {},
"issues": [],
"recommendations": []
}
try:
# Check if file exists
if not os.path.exists(file_path):
validation_results["issues"].append(f"File not found: {file_path}")
return validation_results
# Check file size
file_size = os.path.getsize(file_path)
validation_results["statistics"]["file_size_bytes"] = file_size
validation_results["statistics"]["file_size_mb"] = round(file_size / (1024 * 1024), 2)
if file_size == 0:
validation_results["issues"].append("File is empty")
return validation_results
if format_type == "csv":
# Load CSV file
try:
df = pd.read_csv(file_path)
validation_results["statistics"]["total_rows"] = len(df)
validation_results["statistics"]["total_columns"] = len(df.columns)
validation_results["statistics"]["column_names"] = list(df.columns)
# Check for null values
null_counts = df.isnull().sum().to_dict()
validation_results["statistics"]["null_counts"] = null_counts
if validation_results["statistics"]["total_rows"] == 0:
validation_results["issues"].append("CSV file has no rows")
return validation_results
# Detect structure
if "instruction" in df.columns and "response" in df.columns:
validation_results["detected_structure"] = "instruction-response"
validation_results["is_valid"] = True
elif "input" in df.columns and "output" in df.columns:
validation_results["detected_structure"] = "input-output"
validation_results["is_valid"] = True
elif "prompt" in df.columns and "completion" in df.columns:
validation_results["detected_structure"] = "prompt-completion"
validation_results["is_valid"] = True
elif "text" in df.columns:
validation_results["detected_structure"] = "text-only"
validation_results["is_valid"] = True
else:
# Look for text columns
text_columns = [col for col in df.columns if df[col].dtype == 'object']
if text_columns:
validation_results["detected_structure"] = "custom"
validation_results["statistics"]["potential_text_columns"] = text_columns
validation_results["is_valid"] = True
validation_results["recommendations"].append(
f"Consider renaming columns to match standard formats: instruction/response, input/output, prompt/completion, or text"
)
else:
validation_results["issues"].append("No suitable text columns found in CSV")
# Check for short text
if validation_results["detected_structure"] == "instruction-response":
short_instructions = (df["instruction"].str.len() < 10).sum()
short_responses = (df["response"].str.len() < 10).sum()
validation_results["statistics"]["short_instructions"] = short_instructions
validation_results["statistics"]["short_responses"] = short_responses
if short_instructions > 0:
validation_results["issues"].append(f"Found {short_instructions} instructions shorter than 10 characters")
if short_responses > 0:
validation_results["issues"].append(f"Found {short_responses} responses shorter than 10 characters")
except Exception as e:
validation_results["issues"].append(f"Error parsing CSV: {str(e)}")
return validation_results
elif format_type == "jsonl":
try:
# Load JSONL file
data = []
with open(file_path, 'r', encoding='utf-8') as f:
for line_num, line in enumerate(f, 1):
line = line.strip()
if not line:
continue
try:
json_obj = json.loads(line)
data.append(json_obj)
except json.JSONDecodeError:
validation_results["issues"].append(f"Invalid JSON at line {line_num}")
validation_results["statistics"]["total_examples"] = len(data)
if len(data) == 0:
validation_results["issues"].append("No valid JSON objects found in file")
return validation_results
# Get sample of keys from first object
if data:
validation_results["statistics"]["sample_keys"] = list(data[0].keys())
# Detect structure
structures = []
for item in data:
if "instruction" in item and "response" in item:
structures.append("instruction-response")
elif "input" in item and "output" in item:
structures.append("input-output")
elif "prompt" in item and "completion" in item:
structures.append("prompt-completion")
elif "text" in item:
structures.append("text-only")
else:
structures.append("custom")
# Count structure types
from collections import Counter
structure_counts = Counter(structures)
validation_results["statistics"]["structure_counts"] = structure_counts
# Set detected structure to most common
if structures:
most_common = structure_counts.most_common(1)[0][0]
validation_results["detected_structure"] = most_common
validation_results["is_valid"] = True
# Check if mixed
if len(structure_counts) > 1:
validation_results["issues"].append(f"Mixed structures detected: {dict(structure_counts)}")
validation_results["recommendations"].append("Consider standardizing all records to the same structure")
except Exception as e:
validation_results["issues"].append(f"Error parsing JSONL: {str(e)}")
return validation_results
elif format_type == "text":
try:
# Read text file
with open(file_path, 'r', encoding='utf-8') as f:
content = f.read()
# Get basic stats
total_chars = len(content)
total_words = len(content.split())
total_lines = content.count('\n') + 1
validation_results["statistics"]["total_characters"] = total_chars
validation_results["statistics"]["total_words"] = total_words
validation_results["statistics"]["total_lines"] = total_lines
# Check if it's a single large document or multiple examples
paragraphs = [p.strip() for p in content.split('\n\n') if p.strip()]
validation_results["statistics"]["total_paragraphs"] = len(paragraphs)
# Try to detect structure
# Look for common patterns like "Q: ... A: ...", "Input: ... Output: ..."
has_qa_pattern = re.search(r"Q:.*?A:", content, re.DOTALL) is not None
has_input_output = re.search(r"Input:.*?Output:", content, re.DOTALL) is not None
has_prompt_completion = re.search(r"Prompt:.*?Completion:", content, re.DOTALL) is not None
if has_qa_pattern:
validation_results["detected_structure"] = "Q&A-format"
elif has_input_output:
validation_results["detected_structure"] = "input-output-format"
elif has_prompt_completion:
validation_results["detected_structure"] = "prompt-completion-format"
elif len(paragraphs) > 1:
validation_results["detected_structure"] = "paragraphs"
else:
validation_results["detected_structure"] = "continuous-text"
validation_results["is_valid"] = True
if validation_results["detected_structure"] == "continuous-text" and total_chars < 1000:
validation_results["issues"].append("Text file is very short for fine-tuning")
validation_results["recommendations"].append("Consider adding more content or examples")
except Exception as e:
validation_results["issues"].append(f"Error parsing text file: {str(e)}")
return validation_results
else:
validation_results["issues"].append(f"Unsupported file format: {format_type}")
return validation_results
# General recommendations
if validation_results["is_valid"]:
if not validation_results["issues"]:
validation_results["recommendations"].append("Dataset looks good and ready for fine-tuning!")
else:
validation_results["recommendations"].append("Address the issues above before proceeding with fine-tuning")
return validation_results
except Exception as e:
validation_results["issues"].append(f"Unexpected error: {str(e)}")
return validation_results
def generate_dataset_report(validation_results):
"""
Generate a user-friendly report from validation results
Parameters:
validation_results (dict): Results from validate_dataset
Returns:
str: Formatted report
"""
report = []
# Add header
report.append("# Dataset Validation Report")
report.append("")
# Add validation status
if validation_results["is_valid"]:
report.append("✅ Dataset is valid and can be used for fine-tuning")
else:
report.append("❌ Dataset has issues that need to be addressed")
report.append("")
# Add format info
report.append(f"**File Format:** {validation_results['format']}")
report.append(f"**Detected Structure:** {validation_results['detected_structure']}")
report.append("")
# Add statistics
report.append("## Statistics")
for key, value in validation_results["statistics"].items():
# Format the key for better readability
formatted_key = key.replace("_", " ").title()
report.append(f"- {formatted_key}: {value}")
report.append("")
# Add issues
if validation_results["issues"]:
report.append("## Issues")
for issue in validation_results["issues"]:
report.append(f"- ⚠️ {issue}")
report.append("")
# Add recommendations
if validation_results["recommendations"]:
report.append("## Recommendations")
for recommendation in validation_results["recommendations"]:
report.append(f"- 💡 {recommendation}")
return "\n".join(report) |