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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.
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