README.md

metadata

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.

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

10. Data collection methodology

See 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.

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.

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. 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. 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. 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

@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

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.