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thearnabsarkar/json-semval-minilm-v1

sourceHugging Facemitupdated 1y agoView on Hugging Face
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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. 1.wrong_type - Incorrect data type
  2. 2.alias_key - Alternative field names
  3. 3.invalid_date - Malformed dates
  4. 4.enum_near_miss - Close but incorrect enum values
  5. 5.cross_field - Logical inconsistencies across fields
  6. 6.boolean_text - Text representations of booleans
  7. 7.number_text - Text representations of numbers
  8. 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

python
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)

python
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:

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

bibtex
@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}
}

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