README.md

metadata

dataset_info:
  features:
    - name: instruction
      dtype: string
    - name: input
      dtype: float64
    - name: output
      dtype: string
  splits:
    - name: train
      num_bytes: 743535
      num_examples: 800
    - name: validation
      num_bytes: 87504
      num_examples: 100
    - name: test
      num_bytes: 92879
      num_examples: 100
  download_size: 360936
  dataset_size: 923918
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*

Dataset Analysis Report

Dataset Format

The dataset is provided in Parquet format with the following structure:

Field	Description
instruction	Instruction or question provided to the model
input	Additional input or contextual information
output	Expected response generated for the instruction

Dataset Statistics

Metric	Result
Total rows	1,000
Valid rows	1,000
Invalid rows	0
Columns	3
Expected fields per row	3
Rows with missing fields	0
Rows with unexpected fields	0

Fields

['input', 'instruction', 'output']

Duplicate Analysis

Check	Result
Exact duplicate rows	0
Duplicate groups	0
Duplicate instructions	215
Duplicate responses	2
Near-duplicate instruction pairs	69

No exact duplicate rows were detected. However, 215 duplicate instructions, 2 duplicate responses, and 69 near-duplicate instruction pairs were identified.

Quality Analysis

The automated analysis identified the following quality flags:

Quality Issue	Rows	Percentage
English words detected	1,000	100.00%
Missing values	1,000	100.00%
Low Devanagari ratio	93	9.30%
HTML noise	21	2.10%
URLs detected	16	1.60%
Potentially incomplete responses	10	1.00%

Important: The missing_values check flagged all 1,000 rows. This means the automated analysis identified at least one field as missing according to its configured missing-value rules. The result should be manually reviewed, particularly for the input field, because an intentionally empty input may be a valid characteristic of an instruction/input/output dataset rather than an actual data-quality error.

Analysis Summary

Total rows          : 1,000
Flagged rows        : 1,000
Flagged percentage  : 100.00%

Data status         : ANALYSIS ONLY
Cleaning performed  : NO

All 1,000 rows were flagged by at least one automated quality check. Being flagged does not necessarily mean that every row is unusable; it means that every row requires review according to the configured analysis rules.

Overall Dataset Quality

Metric	Result
Overall Quality Score	66.40 / 100
Quality Grade	D
Dataset Status	NEEDS CLEANING

Strengths

• No exact duplicate rows detected.
• No possible PII detected.
• All rows follow the expected instruction / input / output schema.
• All 1,000 rows are valid records according to the analysis.

Identified Issues

• 215 duplicate instructions detected.
• 2 duplicate responses detected.
• English words were detected in all 1,000 rows.
• 21 rows contain HTML noise.
• 16 rows contain URLs.
• 10 responses were potentially incomplete.
• 93 rows have a low Devanagari ratio.
• The automated analysis flagged missing values in all 1,000 rows.
• 69 near-duplicate instruction pairs were identified.

Recommendation

Significant cleaning and manual review are recommended before using this dataset for model training.

Priority should be given to reviewing:

1. Duplicate and near-duplicate instructions.
2. The 100% missing-value flag, especially whether empty input fields are intentional.
3. English-word detection across the dataset.
4. Low-Devanagari-ratio records.
5. HTML noise and URLs.
6. Potentially incomplete responses.

Analysis Method

The dataset was analyzed without modifying the original data.

The analysis included:

• Dataset structure and schema validation
• Missing-value detection
• Exact duplicate detection
• Duplicate instruction detection
• Duplicate response detection
• Near-duplicate instruction detection
• English-word detection
• Devanagari-ratio analysis
• HTML-noise detection
• URL detection
• Potential incomplete-response detection
• Possible PII detection
• Overall dataset quality scoring and grading

Cleaning Status

No cleaning, deletion, modification, or automatic correction was performed during this analysis.

The analysis script only identified and flagged potential quality issues for subsequent manual review and cleaning.

Analysis Outputs

The analysis generated the following files:

analysis_report(train).json
flagged_rows(train).jsonl

analysis_report(train).json contains the complete dataset-level analysis results, including dataset structure, schema consistency, duplicate statistics, language analysis, quality flags, incomplete-response analysis, and the overall quality score.

flagged_rows(train).jsonl contains the individual records flagged by the automated quality checks for manual review.

Final Assessment

Overall Quality Score: 66.40 / 100 Grade: D Status: NEEDS CLEANING

The dataset has a valid and consistent three-column instruction / input / output structure with 1,000 valid rows and no exact duplicate rows. However, the automated analysis identified duplicate instructions, near-duplicate instructions, duplicate responses, English-word occurrences, missing-value flags, low Devanagari ratios, HTML noise, URLs, and potentially incomplete responses.

The dataset should therefore undergo manual review and targeted cleaning before being considered ready for model training.