---
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

```text
['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

```text
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:

```text
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.