Datasets:

Somtharu181coder
/

test_dataset

   - 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.