Model Card for Csv-AI-Cleaner-V3

Transform English instructions with data context into executable pandas code with AI

Model Description

Csv-AI-Cleaner converts natural language instructions into pandas code for data cleaning, filtering, grouping, sorting, merges, and more.
Fine-tuned on synthetic + real-world datasets using CodeT5 with LoRA for efficiency.

• Developed by: ArhanSD1
• Model type: Seq2Seq Transformer (CodeT5)
• Language(s) (NLP): English
• License: Apache 2.0
• Finetuned from model: Salesforce/CodeT5-base

Model Sources

• Repository: https://huggingface.co/arhansd1/Csv-AI-Cleaner-V3

Direct Use

• Input: Context (sample dataset) + instruction in natural language
• Output: Executable pandas code snippet

Example:

Context:
employee_id | name | salary | department
E001 | Alice | 50000 | IT
E002 | Bob | 45000 | HR

Instruction: Show IT department employees earning over 45000

Output:

df[(df['department'] == 'IT') & (df['salary'] > 45000)]

Out-of-Scope Use

• Ambiguous or poorly defined instructions without dataset context
• Complex multi-step pipelines exceeding ~500 token context limit

Bias, Risks, and Limitations

• Works best with clean and clear column names
• May generate suboptimal code if context is incomplete or contains noise
• No awareness of business logic correctness — only syntax + pattern learning

Recommendations

Users should verify generated code for correctness and safety before execution.

How to Get Started with the Model

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

MODEL_REPO = "arhansd1/Csv-AI-Cleaner-V3"
tokenizer = AutoTokenizer.from_pretrained(MODEL_REPO)
model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_REPO)

def generate_code(input_text):
    prefixed_input = "Generate pandas code: " + input_text
    inputs = tokenizer(prefixed_input, return_tensors="pt", truncation=True, max_length=512)
    with torch.no_grad():
        outputs = model.generate(
            input_ids=inputs.input_ids,
            attention_mask=inputs.attention_mask,
            max_length=128,
            num_beams=5,
            temperature=0.7,
            early_stopping=True
        )
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
input_example = """
Context:
employee_id | name | salary | department
E001 | Alice | 50000 | IT
E002 | Bob | 45000 | HR

Instruction: Show IT department employees earning over 45000
"""
print(generate_code(input_example))

Training Data

• Combination of synthetic data cleaning instructions + public dataset column contexts
• Augmented with filtered StackOverflow code snippets for pandas tasks

Preprocessing

• Normalized table format for context section
• Instruction phrasing normalized to imperative form

Training Hyperparameters

• LoRA fine-tuning
• Learning rate: 5e-5
• Epochs: 3
• Batch size: 8
• Precision: fp16 mixed precision

Testing Data

• Held-out set of 500 natural language → pandas task pairs

Metrics

Metric	Score
Exact Match	71%
Partial Match	92%
Syntax Accuracy	100%

Results Summary

High syntax accuracy, good partial match rate, slightly lower exact match on multi-condition or chained operations.

Environmental Impact

• Hardware Type: Single NVIDIA A100
• Hours used: ~4 hours
• Cloud Provider: AWS
• Compute Region: US-East
• Carbon Emitted: ~1.2 kg CO2eq (estimate)

Model Architecture and Objective

• CodeT5-base (encoder-decoder)
• Objective: Seq2Seq code generation from natural language + data context

Compute Infrastructure

• Training done on 1×A100 GPU
• Fine-tuning with Hugging Face Transformers + PEFT (LoRA)

Model Card Contact

• Author: ArhanSD1
• Hugging Face: https://huggingface.co/arhansd1
• Email: N/A