Blame for README.md

---
pretty_name: "CodeMixCheck: Tanglish Fact-Checking and Auxiliary NLP Dataset"
language:
- ta
- en
language_bcp47:
- ta-Latn
- ta-Taml
- en-IN
multilinguality: multilingual
task_categories:
- text-classification
task_ids:
- fact-checking
- sentiment-classification
- hate-speech-detection
size_categories:
- 1K<n<10K
source_datasets:
- original
annotations_creators:
- machine-generated
- expert-generated
language_creators:
- machine-generated
- expert-generated
license: cc-by-nc-4.0
tags:
- code-mixing
- code-switching
- tanglish
- tamil
- romanized-tamil
- misinformation
- fact-verification
- synthetic
- low-resource
configs:
- config_name: fact_verification
  default: true
  data_files:
  - split: train
    path: data/fact_verification/train.parquet
  - split: validation
    path: data/fact_verification/validation.parquet
  - split: test_synthetic
    path: data/fact_verification/test_synthetic.parquet
  - split: test_curated
    path: data/fact_verification/test_curated.parquet
- config_name: sentiment
  data_files:
  - split: train
    path: data/sentiment/train.parquet
  - split: validation
    path: data/sentiment/validation.parquet
- config_name: abuse_detection
  data_files:
  - split: train
    path: data/abuse_detection/train.parquet
  - split: validation
    path: data/abuse_detection/validation.parquet
  - split: test_synthetic
    path: data/abuse_detection/test_synthetic.parquet
---

# CodeMixCheck v2.2.1: Tanglish Fact-Checking and Auxiliary NLP Dataset

> **Read before use.** Most of this dataset is **synthetic**. Labels on synthetic rows describe a *simulated* world: a `True` or `False` claim that names a real district, festival or scheme does **not** describe a real event. Do not use this data to judge real-world claims or to make statements about real people, places or organisations. Licence: **CC BY-NC 4.0** (non-commercial).

## 1. Short description

CodeMixCheck is a dataset of English–Tamil code-mixed ("Tanglish") social-media-style text. Its main task is **4-way claim veracity classification** (`True`, `False`, `Partially True`, `Unverifiable`). Two small auxiliary tasks, **sentiment** and **abuse detection**, are included as separate configurations. Most texts are written in Roman script, and some mix in Tamil script.

## 2. Dataset overview

| | v2.2.1 project corpus | this release | not released |
|---|---|---|---|
| records | 7,602 | **7,254** | 348 |
| `fact_verification` | 7,004 (6,660 synthetic + 344 curated) | 7,004 | 0 |
| `sentiment` | 473 (132 synthetic + 341 human-collected) | 128 | 341 human-collected + 4 synthetic* |
| `abuse_detection` | 125 (all synthetic) | 122 | 3 synthetic* |

\* Synthetic rows that closely resemble one of the unreleased social-media comments (normalised `fuzz.ratio` ≥ 85), held back as a precaution (audit issue G06).

Every record keeps its original `id` (`CMC-NNNNN`), text and label from v2.2.1. Nothing has been relabelled, translated, or spell-corrected.

## 3. Research motivation

Most fact-checking resources are monolingual. Indian social media often mixes Tamil and English in one message, with Romanised Tamil, Tamil script, hashtags, emoji, forwarded-message conventions and deliberate typos. CodeMixCheck was built for a final-year project (BCSE497J) at Vellore Institute of Technology. The project studies retrieval-augmented, evidence-grounded veracity classification of such text. The dataset provides controlled training data and a separate hand-curated evaluation set.

## 4. Intended applications

- Benchmarking and pre-training classifiers for veracity framing in code-mixed text: hearsay versus attributed claims, and partially true exaggerations.
- Studying robustness to Romanisation, script mixing, typos and social-media noise.
- Comparing in-distribution (`test_synthetic`) with out-of-distribution (`test_curated`) performance.
- Auxiliary sentiment and abuse modelling for multi-task experiments.

## 5. Supported NLP tasks

| config | task | labels |
|---|---|---|
| `fact_verification` | text classification (claim veracity) | `True`, `False`, `Partially True`, `Unverifiable`; optional 3-way `label_3way` = `SUPPORTED` / `REFUTED` / `NOT_ENOUGH_INFO` (`Partially True` has no 3-way equivalent and is `null`) |
| `sentiment` | text classification | `positive`, `negative`, `neutral` |
| `abuse_detection` | text classification | `abusive`, `not_abusive` |

## 6. Languages and linguistic characteristics

- Tamil–English code-mixed text. Matrix language and code-switching density vary by batch.
- Scripts: Roman (Latin) only, mixed Roman + Tamil, and a few Tamil-script-only texts (`script` column).
- `tamil_script_token_ratio` is the share of tokens (maximal runs of `[A-Za-z஀-௿]`) that contain Tamil characters. The stored values were reproduced exactly by recomputation.
- Many synthetic texts have deliberate noise (`noise_level`: none / light / medium / heavy): dropped vowels, casing changes, and repeated or replacement punctuation. **Do not "clean" this noise**, because it is part of the signal.
- Platform conventions: hashtags, emoji, `@` placeholder handles (invented, not real accounts) and multi-line posts.

## 7. Dataset composition

| column | description |
|---|---|
| `id` | stable v2.2.1 row id (`CMC-NNNNN`; not contiguous, because some IDs were retired in earlier versions) |
| `text` | the original text, unmodified |
| `label` | task label (unchanged from v2.2.1) |
| `label_3way` | (fact_verification) SUPPORTED / REFUTED / NOT_ENOUGH_INFO, `null` for Partially True |
| `evidence_explanation` | (fact_verification) short explanation of the label, in Tanglish or English |
| `proposition`, `frame_family` | (fact_verification, synthetic rows) parsed claim core, plus how the claim is framed (`forwarded_message`, `official_attribution`, `personal_hearsay`, `general_report`, `unparsed`) |
| `abuse_label` / `sentiment_label` | auxiliary cross-task label (on sentiment / abuse rows respectively) |
| `sentiment_label_suggested` | (sentiment) model suggestion, where one existed |
| `hf_split`, `split_v2_2_1` | Hub split name, and the split value used in the v2.2.1 project CSV |
| `eval_subset`, `cv_fold` | sub-benchmark and 5-fold assignment (curated test rows only) |
| `label_status` | `gold` or `silver` exactly as recorded in v2.2.1 |
| `label_origin` | `template_construction` (synthetic) or `dataset_author_annotation` (curated) |
| `human_validated`, `label_source`, `review_flag`, `annotation_note`, `content_flag` | original annotation metadata |
| `domain`, `register`, `tone`, `post_format`, `noise_level` | generation / style metadata |
| `script`, `tamil_script_token_ratio` | script metadata |
| `provenance`, `data_origin`, `source_detail` | original provenance fields |
| `provenance_class`, `source_reference`, `generator` | normalised provenance; `not_recorded` wherever the information was not stored |
| `generation_batch` | the v2.2.1 `batch` value |
| `release_tier` | `public` for every record in this repository |
| `template_group`, `content_group`, `is_content_duplicate` | template family / content-group ids for grouped splitting |
| `change_v2_1`, `change_v2_2`, `change_v2_2_1` | per-row version history (verbatim) |
| `audit_flags` | IDs of row-level issues found in the v2.2.1 release audit (see below) |
| `task` | config name |

`audit_flags` codes that occur in this release:

| code | meaning |
|---|---|
| C02 / C03 | normalised duplicate text (with / without hashtags) |
| C04 | near-duplicate (fuzz.ratio ≥ 90 after normalisation) of another row in the same task |
| C05 / C07 | content group / template group contains differing labels |
| D01 | the same claim core (`proposition`) occurs in another split, worded differently |
| D02 | near-duplicate of a row in another split |
| I01 | contains an invented `@` placeholder handle |
| A02 | curated row whose `evidence_explanation` was cleared in v2.2.1 after relabelling |
| G02 | curated row whose specific source articles were not recorded |

## 8. What is not released

| set | rows | reason |
|---|---|---|
| human-collected sentiment comments (`test_human` 119 + `silver` 222) | 341 | Comments written by third-party Instagram users. The dataset author does not have the rights to redistribute them, and some name individuals. Results on them are reported only in the authors’ ongoing research. |
| synthetic sentiment/abuse rows resembling those comments | 7 | precaution against releasing text derived from third-party comments |

The public `sentiment` configuration therefore has no test split. Use it for auxiliary or multi-task training, not as a standalone sentiment benchmark.

## 9. Dataset statistics

All numbers below are generated by `scripts/dataset_statistics.py` from the Parquet files in this repository. `scripts/validate_dataset.py` fails if they differ.

<!-- STATS:BEGIN -->
Total records in these files: **7254**.

#### `fact_verification` (7004 records)

Label distribution by split:

| label | train | validation | test_synthetic | test_curated | total |
|---|---|---|---|---|---|
| False | 1409 | 153 | 168 | 30 | 1760 |
| Partially True | 1311 | 158 | 140 | 15 | 1624 |
| True | 1330 | 207 | 218 | 149 | 1904 |
| Unverifiable | 1287 | 144 | 135 | 150 | 1716 |
| total | 5337 | 662 | 661 | 344 | 7004 |

Provenance class by split:

| provenance_class | train | validation | test_synthetic | test_curated | total |
|---|---|---|---|---|---|
| llm_assisted_human_curated | 0 | 0 | 0 | 344 | 344 |
| template_generated | 5337 | 662 | 661 | 0 | 6660 |
| total | 5337 | 662 | 661 | 344 | 7004 |

Label origin / human-validation status by split:

| label_origin | train | validation | test_synthetic | test_curated | total |
|---|---|---|---|---|---|
| dataset_author_annotation | 0 | 0 | 0 | 344 | 344 |
| template_construction | 5337 | 662 | 661 | 0 | 6660 |
| total | 5337 | 662 | 661 | 344 | 7004 |

| human_validated | train | validation | test_synthetic | test_curated | total |
|---|---|---|---|---|---|
| no | 5337 | 662 | 661 | 0 | 6660 |
| user_accepted_suggestion | 0 | 0 | 0 | 130 | 130 |
| user_labelled | 0 | 0 | 0 | 214 | 214 |
| total | 5337 | 662 | 661 | 344 | 7004 |

Script by split:

| script | train | validation | test_synthetic | test_curated | total |
|---|---|---|---|---|---|
| mixed_script | 817 | 109 | 100 | 243 | 1269 |
| roman | 4520 | 553 | 561 | 100 | 5734 |
| tamil_script | 0 | 0 | 0 | 1 | 1 |
| total | 5337 | 662 | 661 | 344 | 7004 |

Code-mixing / script statistics:

| statistic | value |
|---|---|
| mean_tokens_per_text | 12.52 |
| median_tokens_per_text | 12.0 |
| tamil_script_token_ratio_mean | 0.0295 |
| tamil_script_token_ratio_mean_over_texts_with_tamil | 0.1627 |
| pct_texts_with_any_tamil_script | 18.13 |
| pct_texts_roman_only | 81.87 |
| pct_texts_tamil_script_only | 0.01 |
| pct_texts_with_emoji | 70.9 |
| pct_texts_with_hashtag | 38.56 |
| heuristic_english_lexicon_share_of_roman_tokens_mean | 0.3726 |

Duplicate statistics:

| check | count |
|---|---|
| duplicate_ids | 0 |
| exact_duplicate_texts | 0 |
| normalised_duplicate_texts | 0 |
| normalised_text_shared_across_splits | 0 |
| rows_flagged_near_duplicate_C04 | 379 |
| rows_flagged_cross_split_near_duplicate_D02 | 115 |
| rows_flagged_cross_split_proposition_D01 | 228 |
| is_content_duplicate_true | 183 |

Columns with missing values (% null):

| column | pct_null |
|---|---|
| annotation_note | 100.0 |
| change_v2_1 | 98.91 |
| change_v2_2 | 73.63 |
| change_v2_2_1 | 98.14 |
| content_flag | 100.0 |
| cv_fold | 95.09 |
| eval_subset | 95.09 |
| evidence_explanation | 1.84 |
| frame_family | 4.91 |
| label_3way | 23.19 |
| label_source | 92.23 |
| noise_level | 22.27 |
| post_format | 4.94 |
| proposition | 4.91 |
| review_flag | 100.0 |
| tone | 93.76 |

#### `sentiment` (128 records)

Label distribution by split:

| label | train | validation | total |
|---|---|---|---|
| negative | 37 | 14 | 51 |
| neutral | 25 | 5 | 30 |
| positive | 36 | 11 | 47 |
| total | 98 | 30 | 128 |

Provenance class by split:

| provenance_class | train | validation | total |
|---|---|---|---|
| template_generated | 98 | 30 | 128 |
| total | 98 | 30 | 128 |

Label origin / human-validation status by split:

| label_origin | train | validation | total |
|---|---|---|---|
| template_construction | 98 | 30 | 128 |
| total | 98 | 30 | 128 |

| human_validated | train | validation | total |
|---|---|---|---|
| no | 98 | 30 | 128 |
| total | 98 | 30 | 128 |

Script by split:

| script | train | validation | total |
|---|---|---|---|
| roman | 98 | 30 | 128 |
| total | 98 | 30 | 128 |

Code-mixing / script statistics:

| statistic | value |
|---|---|
| mean_tokens_per_text | 4.57 |
| median_tokens_per_text | 4.0 |
| tamil_script_token_ratio_mean | 0.0 |
| tamil_script_token_ratio_mean_over_texts_with_tamil | n/a (no Tamil-script tokens) |
| pct_texts_with_any_tamil_script | 0.0 |
| pct_texts_roman_only | 100.0 |
| pct_texts_tamil_script_only | 0.0 |
| pct_texts_with_emoji | 100.0 |
| pct_texts_with_hashtag | 0.0 |
| heuristic_english_lexicon_share_of_roman_tokens_mean | 0.1837 |

Duplicate statistics:

| check | count |
|---|---|
| duplicate_ids | 0 |
| exact_duplicate_texts | 0 |
| normalised_duplicate_texts | 0 |
| normalised_text_shared_across_splits | 0 |
| rows_flagged_near_duplicate_C04 | 95 |
| rows_flagged_cross_split_near_duplicate_D02 | 58 |
| rows_flagged_cross_split_proposition_D01 | 0 |
| is_content_duplicate_true | 0 |

Columns with missing values (% null):

| column | pct_null |
|---|---|
| abuse_label | 100.0 |
| annotation_note | 100.0 |
| change_v2_1 | 100.0 |
| change_v2_2 | 100.0 |
| change_v2_2_1 | 100.0 |
| content_flag | 100.0 |
| cv_fold | 100.0 |
| domain | 100.0 |
| eval_subset | 100.0 |
| label_source | 100.0 |
| noise_level | 100.0 |
| review_flag | 100.0 |
| sentiment_label_suggested | 100.0 |

#### `abuse_detection` (122 records)

Label distribution by split:

| label | train | validation | test_synthetic | total |
|---|---|---|---|---|
| abusive | 34 | 9 | 9 | 52 |
| not_abusive | 33 | 18 | 19 | 70 |
| total | 67 | 27 | 28 | 122 |

Provenance class by split:

| provenance_class | train | validation | test_synthetic | total |
|---|---|---|---|---|
| template_generated | 67 | 27 | 28 | 122 |
| total | 67 | 27 | 28 | 122 |

Label origin / human-validation status by split:

| label_origin | train | validation | test_synthetic | total |
|---|---|---|---|---|
| template_construction | 67 | 27 | 28 | 122 |
| total | 67 | 27 | 28 | 122 |

| human_validated | train | validation | test_synthetic | total |
|---|---|---|---|---|
| no | 67 | 27 | 28 | 122 |
| total | 67 | 27 | 28 | 122 |

Script by split:

| script | train | validation | test_synthetic | total |
|---|---|---|---|---|
| mixed_script | 13 | 3 | 5 | 21 |
| roman | 54 | 24 | 23 | 101 |
| total | 67 | 27 | 28 | 122 |

Code-mixing / script statistics:

| statistic | value |
|---|---|
| mean_tokens_per_text | 5.87 |
| median_tokens_per_text | 5.5 |
| tamil_script_token_ratio_mean | 0.0304 |
| tamil_script_token_ratio_mean_over_texts_with_tamil | 0.1766 |
| pct_texts_with_any_tamil_script | 17.21 |
| pct_texts_roman_only | 82.79 |
| pct_texts_tamil_script_only | 0.0 |
| pct_texts_with_emoji | 100.0 |
| pct_texts_with_hashtag | 0.0 |
| heuristic_english_lexicon_share_of_roman_tokens_mean | 0.2057 |

Duplicate statistics:

| check | count |
|---|---|
| duplicate_ids | 0 |
| exact_duplicate_texts | 0 |
| normalised_duplicate_texts | 0 |
| normalised_text_shared_across_splits | 0 |
| rows_flagged_near_duplicate_C04 | 79 |
| rows_flagged_cross_split_near_duplicate_D02 | 70 |
| rows_flagged_cross_split_proposition_D01 | 0 |
| is_content_duplicate_true | 4 |

Columns with missing values (% null):

| column | pct_null |
|---|---|
| annotation_note | 100.0 |
| change_v2_1 | 77.05 |
| change_v2_2 | 100.0 |
| change_v2_2_1 | 100.0 |
| content_flag | 100.0 |
| cv_fold | 100.0 |
| domain | 100.0 |
| eval_subset | 100.0 |
| label_source | 100.0 |
| noise_level | 100.0 |
| review_flag | 100.0 |
<!-- STATS:END -->

## 10. Data collection methodology

See [`docs/data_collection.md`](docs/data_collection.md). In summary:

- **Template-generated records** were produced in batches (`set1`–`set4`, `set7_conversational`, `set9_expansion`, `set10_expansion`, `set11_general_knowledge`, `set8_abuse`). Claim propositions about fictional or generic entities were combined with framing templates (forwarded message, official attribution, personal hearsay, general report), post formats, tones, hashtags, emoji and controlled typing noise.
- **Curated records** (`test_curated`, 344) were selected by the dataset author from news and reference material and rephrased in his own Tanglish wording where needed. LLM assistants helped with drafting. Individual source articles were not recorded, so no sources are listed.

## 11. AI-assisted generation methodology

The dataset author created the templates, entity pools and curated claims, using the LLM chat assistants Claude and ChatGPT as writing helpers. Which assistant contributed to which batch or row was not recorded, so `generator` is set to `not_recorded` rather than guessed. See [`docs/methodology.md`](docs/methodology.md).

## 12. Human annotation methodology

- **Synthetic rows:** labels are assigned **by construction** from the generator's design. No human checked them row by row (`human_validated = no`, `label_origin = template_construction`). `label_status = gold` in these rows means "assigned by the generator", **not** "human-verified".
- **Curated test:** labelled by the dataset author (`user_labelled`, 214 rows). 130 rows went through a v2.2.1 review in which the author accepted a model-suggested correction (`user_accepted_suggestion`); 129 of these changed from `Partially True` to `True` under guideline rule 1 ("judge the claim as written"). The 129 changed rows have no `evidence_explanation`, because the old explanation no longer applied.
- There is a single annotator, and no inter-annotator agreement study has been done.

Full guidance: [`docs/annotation_guidelines.md`](docs/annotation_guidelines.md).

## 13. Label definitions

| label | definition used in the data |
|---|---|
| `True` | The claim, **as written**, matches the (simulated) official source or established reference knowledge. |
| `False` | The (simulated) official statement or records, or established knowledge, directly contradicts the claim. |
| `Partially True` | The core fact holds, but a sweeping, absolute or extreme part of the claim is exaggerated or incomplete. |
| `Unverifiable` | There is no named or checkable source (hearsay / anecdote only), so the claim can be neither confirmed nor refuted. |

For synthetic rows, "official source" refers to a **simulated evidence state** encoded in `evidence_explanation`. It does not refer to real documents. No evidence corpus is included.

## 14. Train / validation / test methodology

- Splits were assigned at **content-group / template-family level**, so no `content_group` or `template_group` spans two splits.
- v2.1 moved 22 rows between splits to remove template-family leakage (see `change_v2_1`).
- The release audit found **no exact or normalised-identical text across splits**, but it did find **semantic overlap**:
  - fact_verification: 84 claim cores (`proposition`), covering 228 rows, appear in more than one split with different framing (flag D01). 73 cross-split near-duplicate pairs (fuzz.ratio ≥ 90) are mostly the same template with a different entity, or typo variants (flag D02).
  - sentiment / abuse synthetic sets: many cross-split typo/emoji variants. These sets were generated by perturbation, so their validation and test scores are **optimistic**.
- Records were not removed. A **strict** `test_synthetic` without D01/D02 rows has 587 of 661 rows (see the loading examples).

## 15. Synthetic versus curated evaluation

`test_synthetic` (661) comes from the same generator as `train`, so it measures in-distribution performance. `test_curated` (344) was written separately and measures transfer to more natural claims. Its sub-benchmarks are in `eval_subset`:

| eval_subset | rows | labels |
|---|---|---|
| `human_verifiable_true_false` | 50 | 20 True / 30 False (science and health facts) |
| `human_health_lifestyle` | 144 | 129 True / 15 Partially True |
| `human_hearsay` | 150 | 150 Unverifiable |

**Never merge or average the two test sets.** Report them separately, report `test_curated` per `eval_subset` as well, and note that its label distribution is very skewed.

## 16. Data preprocessing

1. Read the v2.2.1 project CSV, which was left unmodified (sha256 `37d5bdc3…ad4e1c7f`).
2. Text columns preserved byte-for-byte. Leading/trailing whitespace stripped in categorical columns only (0 changes needed).
3. Empty strings converted to `null`. Booleans and numbers stored with proper types (`bool`, `int8`, `float32`).
4. Redundant columns dropped after verifying that they were redundant: `veracity_label` (identical to `label`), the per-task duplicate of `label`, columns that are empty for a task, and `is_human_authored` (determined by `provenance`).
5. Added `label_origin`, `provenance_class`, `source_reference`, `generator`, `release_tier`, `hf_split` and `audit_flags`. Original split names are kept in `split_v2_2_1`.
6. One Parquet file written per config and split. Records not released (section 8) were excluded and are documented here.

No label, text, split or ID was changed.

## 17. Known limitations

See [`docs/limitations.md`](docs/limitations.md). Key points:

- 6,910 of the 7,254 records are **synthetic**. Their labels follow a simulated world, and models can learn generator artefacts.
- There is semantic cross-split overlap (see section 14), and the synthetic sentiment/abuse sets are small and repetitive.
- `test_curated` is small and label-skewed (only 30 `False` and 15 `Partially True`).
- There is no public sentiment test set, and no evidence corpus for retrieval.
- Coverage is Tamil Nadu-centric, with one annotator.

## 18. Ethical considerations

See [`docs/ethical_considerations.md`](docs/ethical_considerations.md). Synthetic claims name real districts, festivals and categories of institutions together with fictional events. They must not be read or redistributed as real news. `@` handles in the text are invented placeholders. Abuse examples contain insults and slurs in Tanglish.

## 19. Licence

[**CC BY-NC 4.0**](https://creativecommons.org/licenses/by-nc/4.0/). You may share and adapt the data for **non-commercial** purposes, with attribution (see section 22). The licence covers the records in this repository only. The unreleased social-media comments are not covered.

## 20. Intended and out-of-scope uses

**Intended:** non-commercial academic research on code-mixed veracity framing, robustness to noise, and multi-task learning.

**Out of scope:** commercial use; deciding whether real-world claims are true; content moderation or fact-checking deployed on real users; any statement about the real people, places, schemes or organisations mentioned; training generative models to produce realistic misinformation; treating template labels as human-verified.

## 21. Potential biases

- Topics, districts, festivals and entity names come from author-curated pools (11 synthetic domains, Tamil Nadu-centric). Coverage is not representative of Tamil-speaking populations elsewhere (Sri Lanka, Malaysia, Singapore, diaspora).
- The generator's choices determine label–frame associations. For example, rows with an `unparsed` frame are mostly `True` (589 of 845), so a model may learn that shortcut.
- Romanisation follows Tamil Nadu social-media conventions.
- Abuse examples are synthetic and few (122). They do not represent naturally occurring abuse patterns.

## 22. Citation

```bibtex
@misc{basu2026codemixcheck,
  author       = {Basu, Prajjit and Roy, Sanjiban Sekhar},
  title        = {{CodeMixCheck}: A {Tanglish} Fact-Checking and Auxiliary {NLP} Dataset},
  year         = {2026},
  version      = {2.2.1},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/Prajjit/CodeMixCheck}}
}
```

`CITATION.cff` holds the same metadata. A DOI will be added here if one is minted.

## 23. Version information

| version | change |
|---|---|
| v2.1 | split renames (`test` → `test_human` / `test_synthetic`); 22 rows moved to remove template-family leakage; 4 self-referential "X vs X" texts repaired |
| v2.2 | script / `tamil_script_token_ratio` recomputed (1,847 rows; 1,600 script reclassifications) |
| v2.2.1 | review pass: 129 curated rows `Partially True` → `True` (explanation cleared); 1 confirmed; 19 abuse labels confirmed |
| v2.2.1 (Hugging Face release) | packaging only: typed Parquet, task configs, provenance/audit metadata, exclusions in section 8. No data changes. |

782 IDs were retired before v2.1, and no per-row reasons survive.

## 24. Loading

```python
from datasets import load_dataset

repo = "Prajjit/CodeMixCheck"

fv = load_dataset(repo, "fact_verification")      # train / validation / test_synthetic / test_curated
curated = load_dataset(repo, "fact_verification", split="test_curated")
sent = load_dataset(repo, "sentiment")            # train / validation
abuse = load_dataset(repo, "abuse_detection")     # train / validation / test_synthetic

print(fv["train"][0]["text"], fv["train"][0]["label"])

# curated test, per sub-benchmark
hearsay = curated.filter(lambda r: r["eval_subset"] == "human_hearsay")

# stricter synthetic evaluation: drop rows overlapping another split
strict = fv["test_synthetic"].filter(lambda r: not ({"D01", "D02"} & set(r["audit_flags"])))
```

`scripts/load_dataset.py` runs all of these steps. `scripts/dataset_statistics.py` reproduces section 9, and `scripts/validate_dataset.py --hf` re-checks the whole release.

## 25. Contact and contributions

**Authors**

1. **Prajjit Basu** (first author), Vellore Institute of Technology, Vellore. prajjit.basu2023@vitstudent.ac.in
2. **Dr. Sanjiban Sekhar Roy** (second author), Vellore Institute of Technology, Vellore. s.roy@vit.ac.in

**Point of contact:** prajjit.basu2023@vitstudent.ac.in (Prajjit Basu) and s.roy@vit.ac.in (Dr. Sanjiban Sekhar Roy), or the *Community* tab of this dataset page.

Corrections, label disputes and takedown requests are welcome. Please quote the record `id`.