JSON Semantic Validator - MiniLM v1

Hybrid rule+ML model that detects semantic JSON issues and suggests minimal fixes.

Model Description

This model is fine-tuned from nreimers/MiniLM-L6-H384-uncased to classify semantic errors in JSON payloads and predict appropriate fix actions. It works in conjunction with a deterministic rules engine (JSON Schema validation) to provide a hybrid validation approach.

Key Features

• Error Type Classification: Detects 8 types of semantic errors
• Fix Action Prediction: Suggests appropriate corrections
• Fast Inference: ONNX export for CPU-optimized inference
• Hybrid Approach: Combines rules + ML for best results

Training Data

Dataset: thearnabsarkar/json-semval-synth-v1

• 40 synthetic training examples
• 10 synthetic test examples
• Controlled corruptions with labeled fixes

Error Types

1. wrong_type - Incorrect data type
2. alias_key - Alternative field names
3. invalid_date - Malformed dates
4. enum_near_miss - Close but incorrect enum values
5. cross_field - Logical inconsistencies across fields
6. boolean_text - Text representations of booleans
7. number_text - Text representations of numbers
8. extra_key - Unexpected additional properties

Fix Actions

• rename_key - Rename field to expected name
• cast_number - Convert text to number
• cast_bool - Convert text to boolean
• parse_date_iso - Parse and normalize dates
• map_enum - Fuzzy match to valid enum value
• swap_dates - Fix inverted date ranges
• fill_default - Use schema default value

Files

• model.safetensors - PyTorch model weights
• model.onnx - ONNX export for fast CPU inference
• config.json - Model configuration
• tokenizer.json / tokenizer_config.json / vocab.txt - Tokenizer files
• reports/metrics.json - Evaluation metrics

Evaluation

See reports/metrics.json in this repository for detailed evaluation metrics on the synthetic test set.

Performance (Synthetic Test Set)

• Rules-only pass rate: 0.0% (baseline)
• Hybrid pass rate: 60-80% (rules + ML fixes)

Usage

With Transformers

from transformers import AutoModelForSequenceClassification, AutoTokenizer
import json

model = AutoModelForSequenceClassification.from_pretrained("thearnabsarkar/json-semval-minilm-v1")
tokenizer = AutoTokenizer.from_pretrained("thearnabsarkar/json-semval-minilm-v1")

schema = {"type": "object", "properties": {"age": {"type": "integer"}}}
payload = {"age": "25"}

input_text = f"Schema: {json.dumps(schema)} JSON: {json.dumps(payload)}"
inputs = tokenizer(input_text, return_tensors="pt", truncation=True, max_length=512)
outputs = model(**inputs)

# Get predicted error type
predicted_class = outputs.logits.argmax(-1).item()
error_types = ["wrong_type", "alias_key", "invalid_date", "enum_near_miss", 
               "cross_field", "boolean_text", "number_text", "extra_key"]
print(f"Predicted error: {error_types[predicted_class]}")

With ONNX Runtime (Faster CPU Inference)

import onnxruntime as ort
import numpy as np

session = ort.InferenceSession("model.onnx")
# ... tokenize input ...
outputs = session.run(None, {"input_ids": input_ids})

Full Pipeline

For the complete validation and auto-fixing pipeline, see:

• GitHub: json-semantic-validator (if available)
• Space: thearnabsarkar/json-semantic-validator (coming soon)

Limitations

• Synthetic training data: Model may not generalize to all real-world edge cases
• Simple fixes only: Cannot handle complex schema violations
• Context window: Limited to 512 tokens (schema + JSON)
• Single error focus: Optimized for single-error scenarios

Training Details

• Base model: nreimers/MiniLM-L6-H384-uncased
• Training epochs: 3
• Batch size: 8
• Learning rate: 5e-5
• Optimizer: AdamW
• Framework: PyTorch + Transformers

Intended Use

This model is designed for:

• JSON validation with auto-fixing suggestions
• API response validation
• Configuration file validation
• Data quality checks

Citation

@misc{json-semval-minilm-v1,
  author = {Arnab Sarkar},
  title = {JSON Semantic Validator - MiniLM v1},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/thearnabsarkar/json-semval-minilm-v1}
}

Related Resources

• Dataset: thearnabsarkar/json-semval-synth-v1
• Demo Space: thearnabsarkar/json-semantic-validator (coming soon)