--- license: apache-2.0 task_categories: - translation - text-classification - automatic-speech-recognition - text-generation - question-answering language: - ar - arz - en tags: - egyptian-arabic - egyptian-dialect - arz - masri - arabic - arabic-dialect - low-resource-language - machine-translation - speech-recognition - asr - wikipedia - conversational-ai - chatbot - instruction-tuning - fake-review-detection - spam-detection - sentiment - nlp - code-switching - parallel-corpus size_categories: 10M **Dataset evaluation:** See [`EVALUATION.md`](EVALUATION.md) for schema/config checks, row-count status, and the data-governance plan. > **Viewer note:** `default` is a lightweight preview; select `egyptian_arabic` or another source config to load the full data. ## Current Hub Validation Status - Dataset Server rows: **25,399,945** - Dataset Server original/Parquet size: **2,758,228,707 bytes** (~2.76 GB) - Default Hub configuration currently exposes one column: `text` Source-specific configurations are explicitly declared in the dataset metadata; use the config that matches the schema you need. Use the source-specific configs for typed tasks; do not concatenate heterogeneous schemas without normalization. > [!WARNING] > **Data governance review required before redistribution.** This repository aggregates mixed web, social, speech, translation, and review sources. The current card does not establish a complete source-by-source license or privacy audit. Review or remove sensitive content, redact PII, and verify upstream permissions before production or commercial redistribution. # Egyptian Arabic Mega Corpus (EAMC) — 25M Unified Egyptian Dialect Dataset
**The Largest Unified Open Corpus for Egyptian Arabic (Masri / `arz`)** `25.4M` Samples | `2.76 GB` (Parquet) | `11 Hub configurations` | `Apache 2.0` | `Ready-to-train` [![License: Apache 2.0](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) [![HF Datasets](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Datasets-yellow)](https://huggingface.co/datasets) [![Language: arz](https://img.shields.io/badge/Language-Egyptian%20Arabic%20(arz)-green)](https://en.wikipedia.org/wiki/Egyptian_Arabic) [![Size: 25M](https://img.shields.io/badge/Samples-25.4M-orange)](https://huggingface.co/datasets) *Comprehensive coverage: Raw Text · Wikipedia · Conversations · Speech (Whisper) · Parallel Translation (EN↔EGY) · Trilingual QA · Wikipedia Quality Classification · Fake Review / Spam Detection*
--- ## Table of Contents 1. [Dataset Summary](#dataset-summary) 2. [Supported Tasks](#supported-tasks) 3. [Languages](#languages) 4. [Dataset Structure & Configs](#dataset-structure--configs) 5. [Data Instances & Fields (All 1 Hub configuration)](#data-instances--fields) 6. [Data Splits & Statistics](#data-splits--statistics) 7. [Appendix A: Detailed Data Types & Schemas](#appendix-a-detailed-data-types--physical-schemas--mapping-files--configs--hf-features) 8. [How to Use (Loading + Training Recipes)](#how-to-use) 9. [Data Collection & Annotation](#data-collection--annotation) 10. [Considerations, Bias & Limitations](#considerations-bias--limitations) 11. [Intended Uses & Out-of-Scope Uses](#intended-uses) 12. [Preprocessing & Cleaning](#preprocessing--cleaning) 13. [Citation](#citation) 14. [License - Apache 2.0](#license---apache-20) 15. [Contributing Guide](#contributing-guide) 16. [Maintainers & Acknowledgments](#maintainers--acknowledgments) 17. [Changelog](#changelog) 18. [Search Keywords / Tags](#search-keywords--tags) --- ## Dataset Summary **Egyptian Arabic Mega Corpus (EAMC)** is a unified, production-ready collection for **Egyptian Arabic (`arz` / Masri)** — the most widely understood Arabic dialect (100M+ speakers). It merges **8 logically distinct sources** (exposed as 10 source-specific Hugging Face configs plus a lightweight preview) into one canonical repository to eliminate fragmentation in Egyptian dialect research. > **Goal:** Provide a single `load_dataset()` entry point for every major Egyptian Arabic task — from language modeling and MT to ASR and credibility/spam detection — under the repository's current Apache 2.0 metadata, subject to upstream source terms. This release aggregates raw parquet shards discovered in 9 folders (`balanced`, `unbalanced`, `uncategorized`, `Egyptian-Arabic`, `Egyptian_Arabic_Wikipedia_20230101`, `egyptian_arabic_convs`, `egyptian-arabic-speech-dataset`, `english-to-arabic`, `part 1 data`) into a clean Hugging Face Datasets structure. ### Why This Dataset? * **Fragmentation solved:** Before EAMC, Egyptian resources were scattered (OSIAN, CALLHOME, ArzEn, NADI, Wikipedia dumps). Now one import. * **Scale:** **~25.5 Million rows** — 23.8M raw text + 728K Wikipedia articles + 569K credibility labels + 60K spam reviews + 33K parallel sentences + 37K QA quadruples + 2.5K multi-turn dialogues + 2.5K Whisper-ready speech samples. * **License note:** The repository metadata declares Apache 2.0, but this aggregated corpus includes mixed upstream sources. Verify compatibility and permissions source-by-source before redistribution or commercial release. * **Multi-modal:** Text, conversation, speech (80-dim log-Mel), and tabular-rich review data. * **Trilingual:** Egyptian Arabic (`ar_eg` / `arz`), Modern Standard Arabic (`ar`), and English (`en`) aligned. --- ## Supported Tasks | Task | Config(s) | Label Type | | :--- | :--- | :--- | | **Language Modeling (Autoregressive / MLM)** | `egyptian_arabic`, `wikipedia`, `convs` | `text` | | **Instruction Tuning / Conversational AI** | `convs` | `role: user/model` multi-turn | | **Machine Translation (EN ↔ EGY)** | `english_to_arabic` | `Egy` ↔ `English` | | **Question Answering / Knowledge QA** | `qa_trilingual` (train.csv) | `question` → `answer` | | **Text Classification (Human vs. Template-translated Wikipedia)** | `balanced`, `unbalanced`, `uncategorized` | `label: Human-generated / Template-translated` | | **Fake Review / Spam Detection** | `reviews_spam` (`part 1 data`) | `label: authentic/fake`, `spam_hit_score` | | **Sentiment Analysis (3-way)** | `reviews_spam` | `sentiment_label: positive/neutral/negative` | | **Automatic Speech Recognition (ASR)** | `speech_whisper` | `input_features` (log-Mel) → `labels` (token IDs) | | **Dialect Identification / Credibility / Bot Detection** | `balanced/unbalanced/uncategorized` + metadata | `bots_editors_percentage`, `top_editors` | --- ## Languages | Code | Name | Script | Coverage | | :--- | :--- | :--- | :--- | | `arz` / `ar_eg` | Egyptian Arabic (Masri) | Arabic (and some Arabizi in raw) | **Primary** - all configs | | `ar` | Modern Standard Arabic (MSA) | Arabic | QA corpus, Wikipedia, parallel | | `en` | English | Latin | Parallel MT + QA + reviews (`original_review`) | **Dialect notes:** Egyptian Arabic differs sharply from MSA in morphology, lexicon (e.g., `مش` for negation, `دلوقتي` for "now"), and code-switching with English/French. The `convs` and `reviews_spam` configs are the purest dialectal, while `wikipedia` is more MSA-leaning but within `arz.wikipedia.org`. --- ## Dataset Structure & Configs The dataset uses the Hugging Face **Configs** pattern. One repository, ten source-specific configs plus a lightweight preview. ```text dataset/ ├── Egyptian-Arabic/ → config: egyptian_arabic ├── Egyptian_Arabic_Wikipedia_20230101/ → config: wikipedia ├── egyptian_arabic_convs/ → config: convs ├── egyptian-arabic-speech-dataset/ → config: speech_whisper ├── english-to-arabic/ → config: english_to_arabic (+ qa_trilingual via train.csv) ├── balanced/ → config: balanced ├── unbalanced/ → config: unbalanced ├── uncategorized/ → config: uncategorized └── part 1 data/ → config: reviews_spam ``` ### How configs map to files | Config Name | HF `load_dataset(..., "config_name")` | Underlying Files | Rows | | :--- | :--- | :--- | :--- | | `egyptian_arabic` | `egyptian_arabic` | `Egyptian-Arabic/*.parquet` (10 shards) | **23,850,855** | | `wikipedia` | `wikipedia` | `Egyptian_Arabic_Wikipedia_20230101/*.parquet` (2 shards) | **728,337** | | `convs` | `convs` | `egyptian_arabic_convs/train-*.parquet` | **2,517** | | `speech_whisper` | `speech_whisper` | `egyptian-arabic-speech-dataset/*.parquet` (6 shards) | **2,571** | | `english_to_arabic` | `english_to_arabic` | `english-to-arabic/*.parquet` (3 unique sources) | **33,299 unique** (39,853 with duplicate) | | `qa_trilingual` | `qa_trilingual` | `english-to-arabic/train.csv` | **37,149** (CSV) / **44,821** if counting raw CSV | | `balanced` | `balanced` | `balanced/*.parquet` (2 shards) | **20,000** | | `unbalanced` | `unbalanced` | `unbalanced/*.parquet` (2 shards) | **166,401** | | `uncategorized` | `uncategorized` | `uncategorized/*.parquet` (3 shards) | **569,264** | | `reviews_spam` | `reviews_spam` | `part 1 data/*.parquet` (2 shards) | **60,000** | | **TOTAL (deduplicated)** | — | **33 Parquet + 1 CSV** | **~25,533,244 unique** | --- ## Data Instances & Fields ### 1. `egyptian_arabic` — Massive Raw Egyptian Text > Unfiltered Egyptian social / web text for pre-training. Ideal for Egyptian LM. | Field | Type | Description | | :--- | :--- | :--- | | `text` | `string` | Free-form Egyptian Arabic text (tweets, quotes, dialectal sentences). Sample avg ~167 chars. Mix of dialect, MSA, and English code-switch. | **Example:** ```json { "text": "عندى كام فكره كده على فكره الأسرة جميلة فكره العيلة و ان يكون عندك أهل بيحبوك و بتحبهم .. و يخافوا عليك و كده نعمة من نعم الله علينا" } { "text": "You have no idea how hard I've looked for a gift to bring You. Nothing...– Rumi" } ``` --- ### 2. `wikipedia` — Egyptian Arabic Wikipedia Dump (2023-01-01) > Clean encyclopedic `arz` articles. Long-form (avg 2,292 chars) for knowledge-intensive LM. | Field | Type | Description | | :--- | :--- | :--- | | `text` | `string` | Full Wikipedia article text (title + body merged). Example starts with `"علم جنوب السودان ..."`. Max > 18k chars. | **Use:** Continue pre-training, retrieval-augmented QA, summarization. Note: First row is header `"text"` literal — filter with `df = df[df.text != "text"]`. --- ### 3. `convs` — Egyptian Multi-Turn Conversations (2,517 dialogues) > High-quality instruction / chat data in Egyptian dialect. Each `text` is a **JSON-serialized list** of turns. | Field | Type | Description | | :--- | :--- | :--- | | `text` | `string` (JSON) | `[{"role":"user","content":"مين سقراط وامتى مات؟"}, {"role":"model","content":"سقراط كان فيلسوف يوناني ..."}, ...]` — avg 5,901 chars, 5-15 turns, topics: philosophy, physics (FEP/Free Energy Principle), VQ-VAE, history. Fully Egyptian Arabic. | **Example (decoded, first turn):** ``` user: هل مبدأ الطاقة الحرة متعلق ب VQ-VAE؟ model: مبدأ الطاقة الحرة بيشير لمفهوم إن الكائنات الحية بتحاول تقلل من الطاقة الحرة بتاعتها بشكل طبيعي... ``` **Tip to parse:** ```python import json turns = json.loads(example["text"]) # or ast.literal_eval if single quotes messages = [{"role": t["role"], "content": t["content"]} for t in turns] ``` --- ### 4. `speech_whisper` — Egyptian Arabic Speech (Whisper-preprocessed) > Ready-to-train for `openai/whisper` without audio decoding. Log-Mel + token IDs. | Field | Type | Description | | :--- | :--- | :--- | | `input_features` | `List[np.ndarray[float32, shape=(3000,)]]` length 80 | 80-channel log-Mel spectrogram, 30s padded (3000 frames @100 Hz). Each element is shape `(3000,)` → stack to `(80, 3000)`. | | `labels` | `List[int]` | Whisper tokenizer token IDs, including special tokens: `50258` (`<|startoftranscript|>`), `50272` (`<|ar|>`), `50359` (`<|transcribe|>`), `50363` (`<|notimestamps|>`), `50257` (`<|endoftext|>`). Vocab ~51K, max label len ~50. | **Example shape:** ```python features = np.stack(example["input_features"]) # (80, 3000) labels = example["labels"] # [50258, 50272, 50359, 50363, 28814, 1829, ... 50257] ``` **Source vocab:** Equivalent to `openai/whisper-small` / `large` tokenizer (multilingual). Language token `50272` corresponds to Arabic. --- ### 5. `english_to_arabic` — Parallel EN↔EGY (33K unique) > Three sub-sources merged; deduplicated. | Field | Type | Description | | :--- | :--- | :--- | | `Egy` | `string` | Egyptian Arabic sentence. | | `English` | `string` | English translation. | | `Egy_Text_Source` | `string` | Provenance citation. | **Sub-sources:** | Source | Rows | Source Citation | | :--- | :--- | :--- | | **ArzEn-MultiGenre** | 13,946 | `Al-Sabbagh, Rania (2023). ArzEn-MultiGenre: An aligned parallel dataset of Egyptian Arabic song lyrics, novels, and subtitles, with English translations. Mendeley Data, V3. DOI:10.17632/6k97jty9xg.3` | | **Milion_Token_EGY_Songs** | 6,554 | Million Token Egyptian Songs lyrics (song parallel) | | **NADI_2024_SubTask_EgyText_Translated** | 12,799 | `NADI 2024: The Fifth Nuanced Arabic Dialect Identification Shared Task (Abdul-Mageed et al., ArabicNLP-WS 2024)` https://aclanthology.org/2024.arabicnlp-1.79 | **Example NADI:** ```json { "Egy": "أنا بقترح إنك تيجي في مايو أو أكتوبر اللي هما أحسن مواسم في السنة.", "English": "I suggest you come in May or October, which are the best seasons of the year." } ``` **Deduplication Note:** `Milion_Token_EGY_Songs-*.parquet` and `milion_token_EGY_songs-*.parquet (1)` are byte-identical duplicates (6,554 rows). Use deduplicated 33,299 count for training. --- ### 6. `qa_trilingual` — Trilingual QA with Categories (37K, CSV) > `train.csv` (often overlooked). Structured QA quadruples in 3 language variants. Perfect for instruction tuning & category-conditional generation. | Field | Type | Description | | :--- | :--- | :--- | | `question` | `string` | Question in `ar`, `ar_eg`, or `en` | | `answer` | `string` | Answer in same language | | `category` | `string` | Top-level domain: `Geography`, `Art`, `History`, `Science`, `Education`, etc. (48 categories) | | `sub_category` | `string` | Fine-grained: `Countries`, `History`, `Genres`, `Theories`, `Inventions`, etc. | | `language` | `string` | `ar` (MSA), `ar_eg` (Egyptian), `en` (English) | | `question_char_length` | `int32` | Precomputed length | | `answer_char_length` | `int32` | - | | `question_word_count` | `int32` | Avg Q 8.7 words | | `answer_word_count` | `int32` | Avg A 34.9 words | **Language distribution:** `ar_eg: 12,451 | en: 12,432 | ar: 12,266` — perfectly balanced. **Top categories:** `Geography 3,617`, `Art 873`, `History 850`, `Science 793` ... **Example (history):** ```json { "question": "مين اللي بيقولوا عليه اخترع شبكة الويب العالمية؟", "answer": "بيقولوا على العالم البريطاني تيم بيرنرز لي هو اللي اخترع شبكة الويب العالمية. هو اللي طور أساسيات زي لغة HTML...", "category": "History", "sub_category": "Discoveries", "language": "ar_eg" } ``` **Source `dataset_info.yaml` features:** ```yaml features: [question, answer, category, sub_category, language, question_char_length, ...] ``` --- ### 7. `balanced` / `unbalanced` / `uncategorized` — Wikipedia Human vs. Template-Translated Detection > Detect bot/template-translated vs. human-written Egyptian Wikipedia pages. Rich editor metadata. | Field | Type | Description | | :--- | :--- | :--- | | `page_title` | `string` | Wikipedia page title (Egyptian Arabic) | | `creation_date` | `date` | Page creation date (2020+ mostly) | | `creator_name` | `string` | Username (often `HitomiAkane`) | | `total_edits` | `int` | Number of edits | | `total_editors` | `int` | Distinct editors | | `top_editors` | `list[string]` | e.g., `['GhalyBot', 'HitomiAkane']` | | `bots_editors_percentage` | `float` | % bot editors | | `humans_editors_percentage` | `float` | % human editors | | `total_bytes` | `int` | Page size bytes | | `total_chars` | `int` | Character count | | `total_words` | `int` | Word count | | `page_text` | `string` | Full article text (avg 460-1,002 chars depending on config; uncategorized has max 184K chars) | | `label` | `string` | `Human-generated` or `Template-translated` | **Key stats:** | Config | Train | Test | Human | Template | Ratio (Template %) | | :--- | ---: | ---: | ---: | ---: | :--- | | `balanced` | 16,000 | 4,000 | 8,015 (train) + ~? | 7,985 | **50%** (deliberately balanced) | | `unbalanced` | 133,120 | 33,281 | 8,872 | 124,248 | **93.3% template** (real-world) | | `uncategorized` | 455,411 | 113,853 | 129,812 (train shard) | 97,894 | **~43% template** (noisy) | **Use:** Train credibility classifiers, bot detection, data cleaning filters for LLM pre-training (filter template-translated low-quality pages). --- ### 8. `reviews_spam` — Arabic Fake Review & Spam Detection (60K) > Richly annotated Yelp-style reviews: Arabic translations + normalized forms + user/product graph features + sentiment + spam signals. | Field | Type | Description | | :--- | :--- | :--- | | `user_id` | `int64` | Anonymized user | | `product_id` | `int64` | Product / restaurant ID | | `original_review` | `string` | English original review (e.g., "Love this place! ...") | | `translated_review` | `string` | Egyptian Arabic translation (dialectal) | | `normalized_translated_review` | `string` | Normalized Arabic (alef/ya/ta marbuta normalized: `ة→ه`, `ى→ي`, `أ→ا`) — ready for tokenization | | `date` | `string` | Review date | | `rating` | `int` | 1-5 stars (`5:19,707 | 4:15,318 | 3:5,790 | 1:5,131 | 2:4,054`) | | `sentiment_label` | `string` | `positive` (30,611) / `negative` (11,596) / `neutral` (7,793) | | `positive_normalized_score` | `float` | Sentiment model score | | `neutral_normalized_score` | `float` | - | | `negative_normalized_score` | `float` | - | | `spam_hit_score` | `int` | Spam heuristic hits (0: 46,955, 1: 2,892, 2: 144, >2 rare) | | `arabic_num_words` | `int` | Arabic word count | | `entropy1` / `entropy2` | `float` | Text entropy features | | `first_review_date` / `last_review_date` | `string` | User history window | | `review_gap_days` | `int` | Days between reviews | | `review_count` | `int` | User total reviews | | `product_avg_rating` | `float` | Product mean rating | | `rating_deviation` | `float` | `rating - product_avg` | | `product_first_review_date` | `string` | Product history | | `days_since_first_review` | `int` | Recency | | `user_tenure_days` | `int` | User tenure | | `label` | `int` | `1: fake / spam`, `0: authentic` (or inverted; check `label_str`) | | `label_str` | `string` | `fake` (25,000 train) / `authentic` (25,000 train) — perfectly balanced 50/50 | **Example (authentic, positive, 5 stars):** ``` original_review: "Love this place! At one point, we knew the GM of Maialino's who gave us a proper introduction..." translated_review: "بحب المكان ده! في وقت من الأوقات كنا نعرف المدير العام لمطعم Maialino's اللي عرفنا على المكان ده..." normalized: "بحب المكان ده! في وقت من الاوقات كنا نعرف المدير العام لمطعم..." label_str: authentic | rating: 5 | sentiment: positive ``` **Strength:** Only Egyptian Arabic fake-review dataset with user/product graph features — enables GNN, temporal, and behavioral spam detection beyond text. --- ## Data Splits & Statistics | Config | Split | Examples | Size (approx) | Avg Chars | Columns | | :--- | :--- | ---: | :--- | ---: | :--- | | `egyptian_arabic` | `train` | 23,700,035 | ~2.1 GB | 167 | 1 | | | `test` | 150,820 | 13 MB | 167 | 1 | | `wikipedia` | `train` | 728,337 | 94 MB | 2,292 | 1 | | `convs` | `train` | 2,517 | 10 MB | 5,901 | 1 (JSON) | | `speech_whisper` | `train` | 2,235 | 285 MB | — (80×3000) | 2 | | | `test` | 336 | 42 MB | — | 2 | | `english_to_arabic` | `train`* | 33,299 | 8 MB | — | 3 | | `qa_trilingual` | `train` | 37,149 | 16 MB | Q 41 / A 302 (char) | 9 | | `balanced` | `train` | 16,000 | 14 MB | 1,003 | 13 | | | `test` | 4,000 | 3 MB | — | 13 | | `unbalanced` | `train` | 133,120 | 47 MB | 461 | 13 | | | `test` | 33,281 | 11 MB | — | 13 | | `uncategorized` | `train` | 455,411 | 113 MB | 622 | 13 | | | `test` | 113,853 | 28 MB | — | 13 | | `reviews_spam` | `train` | 50,000 | 62 MB | ~450 words | 26 | | | `test` | 10,000 | 12 MB | — | 26 | \* `english_to_arabic` is distributed without train/test split in source; recommended 90/5/5. **Total compressed parquet on disk:** `2.76 GB` (deduplicated `2.65 GB`). **Total uncompressed rows:** `~25.44M parquet + 37K CSV`. --- ## Appendix A: Detailed Data Types & Physical Schemas — Mapping Files ↔ Configs ↔ HF Features > This appendix bridges **physical files → Parquet/Arrow types → HF `Features` → pandas dtypes → usage**. Every row is linked to its main documentation section in `README.md:171` (“Data Instances & Fields”). Use this as a contract when validating new shards. ### A.0 Legend | Layer | What you see | Tool | | :--- | :--- | :--- | | **Parquet** | `optional binary field_id=-1 text (String)` | `pyarrow.parquet.ParquetFile(path).schema` `README.md:153` | | **Arrow** | `text: string` | `pf.schema_arrow` | | **HF Features** | `{"dtype":"string","_type":"Value"}` | `pf.metadata.metadata[b'huggingface']` | | **Pandas** | `text: object / string` | `pd.read_parquet(path).dtypes` | All string fields are **UTF-8**, all numeric are **little-endian**. `optional` = nullable (`None` allowed). `required group` = top-level schema wrapper. ### A.1 `egyptian_arabic` ↔ `Egyptian-Arabic/*.parquet` — See `README.md:173` | Column | Parquet Physical | Arrow | HF Feature | Pandas dtype | Nullable | Example (physical storage) | | :--- | :--- | :--- | :--- | :--- | :--- | :--- | | `text` | `optional binary (String)` | `string` | `Value(dtype=string)` | `string` (`object` in py <2) | ✅ | `عندى كام فكره...` / `November 2011` / `أسرة` | *Files: 10 shards (train 00000-00008 + test 00000). Total `23,850,855` rows. Single column → minimal overhead, streaming-friendly. Avg `167` chars ≈ `~0.9 KB` per row uncompressed. Links to **Data Splits `README.md:369`** and **Supported Tasks `README.md:30` (Language Modeling).*** **PyArrow verification:** ```python import pyarrow.parquet as pq pf = pq.ParquetFile("Egyptian-Arabic/train-00000-of-00009.parquet") print(pf.schema) # optional binary text (String) print(pf.schema_arrow) # text: string ``` ### A.2 `wikipedia` ↔ `Egyptian_Arabic_Wikipedia_20230101/*.parquet` — See `README.md:188` Identical physical type to `egyptian_arabic` (intentionally compatible for concatenation): | Column | Parquet | Arrow | HF | Pandas | Example | | :--- | :--- | :--- | :--- | :--- | :--- | | `text` | `optional binary (String)` | `string` | `Value(string)` | `string` | `علم جنوب السودان ...` (avg 2,292 chars, header `text` in row 0 must be filtered) | *2 shards, 728,337 rows. No `huggingface` metadata in header (raw dump) → `datasets` infers `Value(string)`. See **Considerations `README.md:525`** for header artifact.* ### A.3 `convs` ↔ `egyptian_arabic_convs/train-*.parquet` — See `README.md:199` | Column | Parquet Physical | Arrow | HF Feature | Pandas | Nullable | Storage Detail | | :--- | :--- | :--- | :--- | :--- | :--- | :--- | | `text` | `optional group (List) { repeated group list { optional group element { optional binary content (String), optional binary role (String) } } }` | `list>` | `Sequence(Sequence(struct))` → `text: [ {content: Value(string), role: Value(string)} ]` | `object` (list of dicts) | ✅ | JSON-list serialized; actually **Arrow List** not plain string. 2,517 rows, avg 5,901 chars when joined. | **HF Features (from parquet metadata):** ```json "text": [{"content": {"dtype":"string","_type":"Value"}, "role": {"dtype":"string","_type":"Value"}}] ``` **Usage mapping:** Decode → chat template (`README.md:211`): ```python import json, pyarrow.parquet as pq pf = pq.ParquetFile("egyptian_arabic_convs/train-00000-of-00001.parquet") print(pf.schema_arrow) # text: list> # datasets loads as List[Dict] turns = dataset["train"][0]["text"] # already list[dict] via HF, or json.loads(raw_text) ``` ### A.4 `speech_whisper` ↔ `egyptian-arabic-speech-dataset/*.parquet` — See `README.md:220` | Column | Parquet Physical | Arrow | HF Feature | Pandas | Example Shape | | :--- | :--- | :--- | :--- | :--- | :--- | | `input_features` | `optional group (List) { repeated group list { optional group element (List) { repeated group list { optional float element } } } }` | `list>` | `Sequence(Sequence(Value(float32)))` → 2-level nesting: outer 80 (mel bins), inner 3000 (frames) | `object` (list of np arrays) | `(80, 3000)` float32 ≈ 0.96 MB/row | | `labels` | `optional group (List) { repeated group list { optional int64 element } }` | `list` | `Sequence(Value(int64))` | `object` (list of int) | `[50258,50272,50359,50363,28814,...,50257]` len ~15-50 | **Physical verification:** ```python pf = pq.ParquetFile("egyptian-arabic-speech-dataset/train-00001-of-00006.parquet") print(pf.schema_arrow) # input_features: list>, labels: list meta = json.loads(pf.metadata.metadata[b'huggingface']) # {"input_features": {"feature":{"feature":{"dtype":"float32"}}, "labels":{"feature":{"dtype":"int64"}}} feats = np.stack(example["input_features"]) # (80,3000) float32 log-Mel, padding value ~ -0.598 ``` *6 shards (5 train 447×5=2,235 + test 336 =2,571). Links to **How to Use C `README.md:465`** (Whisper fine-tune). No raw audio — only precomputed Mel, hence `float` not `binary`.* ### A.5 `english_to_arabic` ↔ `english-to-arabic/*.parquet` — See `README.md:237` All 3 sources share **identical schema** (intentional for union): | Column | Parquet | Arrow | HF Feature | Pandas | Example | | :--- | :--- | :--- | :--- | :--- | :--- | | `Egy` | `optional binary (String)` | `string` | `Value(string)` | `string` | `رضا فين؟` / `أنا بقترح إنك تيجي...` | | `English` | `optional binary (String)` | `string` | `Value(string)` | `string` | `Where is Rida?` | | `Egy_Text_Source` | `optional binary (String)` | `string` | `Value(string)` | `string` | `Al-Sabbagh... Mendeley V3` / `NADI 2024...` | *Files: `ArzEn_MultiGenre` 13,946 + `Milion_Token_EGY_Songs` 6,554 ×2 duplicate + `NADI_2024` 12,799 = 39,853 raw, 33,299 dedup. See **Data Splits `README.md:369`** and **Citation `README.md:590`**. Duplicate noted in **Considerations `README.md:525`**.* ### A.6 `qa_trilingual` ↔ `english-to-arabic/train.csv` (+ `dataset_info.yaml`) — See `README.md:262` *Only CSV in corpus; not Parquet. Inferred schema via `pyarrow.csv` / `pandas`:* | Column | CSV Raw | Arrow (from pandas) | HF Feature (`dataset_info.yaml:3`) | Pandas dtype | Example | | :--- | :--- | :--- | :--- | :--- | :--- | | `question` | `string` (UTF-8, quoted) | `large_string` | `Value(string)` | `string` | `مين اللي بيقولوا عليه اخترع شبكة الويب العالمية؟` | | `answer` | `string` | `large_string` | `Value(string)` | `string` | `بيقولوا على العالم البريطاني تيم بيرنرز لي...` | | `category` | `string` | `large_string` | `Value(string)` | `string` | `History` / `Geography` (48 distinct) | | `sub_category` | `string` | `large_string` | `Value(string)` | `string` | `Discoveries` / `Countries` | | `language` | `string` (enum) | `large_string` | `Value(string)` | `string` | `ar` / `ar_eg` / `en` (3 values) | | `question_char_length` | `int` (decimal) | `int64` | `Value(int32)` | `int64` | `48` | | `answer_char_length` | `int` | `int64` | `Value(int32)` | `int64` | `213` | | `question_word_count` | `int` | `int64` | `Value(int32)` | `int64` | `8` | | `answer_word_count` | `int` | `int64` | `Value(int32)` | `int64` | `37` | *37,149 rows (CSV) / 44,821 counting raw duplicates. `dataset_info.yaml:5` declares `splits: train num_examples:0` placeholder — actual count is CSV row count. Avg Q 8.7 words / A 34.9 words. Links to **Translation & QA tasks `README.md:30`**.* ### A.7 `balanced` / `unbalanced` / `uncategorized` ↔ `balanced/*.parquet`, `unbalanced/*.parquet`, `uncategorized/*.parquet` — See `README.md:296` *All 3 configs share **identical 13-column schema** (provenance via same WikiExtractor pipeline; differs only in class balance). Physical types:* | Column | Parquet Physical | Arrow | HF Feature | Pandas dtype | Nullable | Role | | :--- | :--- | :--- | :--- | :--- | :--- | :--- | | `page_title` | `optional binary (String)` | `string` | `Value(string)` | `string` | ✅ | Text | | `creation_date` | `optional binary (String)` | `string` | `Value(string)` | `string` | ✅ | ISO date `2020-05-08` (stored as string, not date32) | | `creator_name` | `optional binary (String)` | `string` | `Value(string)` | `string` | ✅ | `HitomiAkane` | | `total_edits` | `optional int64` | `int64` | `Value(int64)` | `int64` | ✅ | Count | | `total_editors` | `optional int64` | `int64` | `Value(int64)` | `int64` | ✅ | Count | | `top_editors` | `optional binary (String)` | `string` | `Value(string)` | `string` | ✅ | **Serialized JSON string** e.g. `"['GhalyBot', 'HitomiAkane']"` — not native list; parse with `ast.literal_eval` | | `bots_editors_percentage` | `optional double` | `double` | `Value(float64)` | `float64` | ✅ | `50.0`, `66.67` | | `humans_editors_percentage` | `optional double` | `double` | `Value(float64)` | `float64` | ✅ | `50.0`, `33.33` | | `total_bytes` | `optional int64` | `int64` | `Value(int64)` | `int64` | ✅ | Bytes | | `total_chars` | `optional int64` | `int64` | `Value(int64)` | `int64` | ✅ | Chars | | `total_words` | `optional int64` | `int64` | `Value(int64)` | `int64` | ✅ | Words | | `page_text` | `optional binary (String)` | `string` | `Value(string)` | `string` | ✅ | Body (avg 461–1,002) | | `label` | `optional binary (String)` | `string` | `Value(string)` | `string` | ✅ | `Human-generated` / `Template-translated` | *Row counts: `balanced` 20,000, `unbalanced` 166,401, `uncategorized` 569,264. Links to **Data Splits `README.md:369`** and **Considerations `README.md:516`**.* **Warning mapping:** `top_editors` looks like `list` but is **stored as string** → must parse. `creation_date` is `string` not `date32` → `pd.to_datetime(df['creation_date'])`. ### A.8 `reviews_spam` ↔ `part 1 data/*.parquet` — See `README.md:327` *Widest schema: 26 columns, mixed types ( richest for tabular + text). All `optional` nullable:* | # | Column | Parquet | Arrow | HF Feature | Pandas | Example / Range | | :--- | :--- | :--- | :--- | :--- | :--- | :--- | | 1 | `user_id` | `int64` | `int64` | `Value(int64)` | `int64` | `48592` | | 2 | `product_id` | `int64` | `int64` | `Value(int64)` | `int64` | `3791` | | 3 | `original_review` | `binary (String)` | `string` | `Value(string)` | `string` | `Love this place! ...` (English) | | 4 | `translated_review` | `binary (String)` | `string` | `Value(string)` | `string` | `بحب المكان ده! ...` (dialectal) | | 5 | `normalized_translated_review` | `binary (String)` | `string` | `Value(string)` | `string` | `بحب المكان ده! في وقت من الاوقات...` (normalized) | | 6 | `date` | `binary (String)` | `string` | `Value(string)` | `string` | `2014-03-21` | | 7 | `rating` | `int64` | `int64` | `Value(int64)` | `int64` | `1-5` (5 most common) | | 8 | `sentiment_label` | `binary (String)` | `string` | `Value(string)` | `string` | `positive` / `negative` / `neutral` | | 9 | `positive_normalized_score` | `double` | `double` | `Value(float64)` | `float64` | `0.0-1.0` | | 10 | `neutral_normalized_score` | `double` | `double` | `Value(float64)` | `float64` | `0.0-1.0` | | 11 | `negative_normalized_score` | `double` | `double` | `Value(float64)` | `float64` | `0.0-1.0` | | 12 | `spam_hit_score` | `int64` | `int64` | `Value(int64)` | `int64` | `0` (46,955) / `1` (2,892) / `2` (144) | | 13 | `arabic_num_words` | `int64` | `int64` | `Value(int64)` | `int64` | e.g. `112` | | 14 | `entropy1` | `double` | `double` | `Value(float64)` | `float64` | `3.5-8.0` | | 15 | `entropy2` | `double` | `double` | `Value(float64)` | `float64` | `3.5-8.0` | | 16 | `first_review_date` | `binary (String)` | `string` | `Value(string)` | `string` | `2012-01-10` | | 17 | `last_review_date` | `binary (String)` | `string` | `Value(string)` | `string` | `2014-05-01` | | 18 | `review_gap_days` | `int64` | `int64` | `Value(int64)` | `int64` | `0-2000` | | 19 | `review_count` | `int64` | `int64` | `Value(int64)` | `int64` | `1-500` | | 20 | `product_avg_rating` | `double` | `double` | `Value(float64)` | `float64` | `2.5-5.0` | | 21 | `rating_deviation` | `double` | `double` | `Value(float64)` | `float64` | `-3.0 .. +3.0` | | 22 | `product_first_review_date` | `binary (String)` | `string` | `Value(string)` | `string` | `2011-06-15` | | 23 | `days_since_first_review` | `int64` | `int64` | `Value(int64)` | `int64` | `0-4000` | | 24 | `user_tenure_days` | `int64` | `int64` | `Value(int64)` | `int64` | `0-4000` | | 25 | `label` | `int64` | `int64` | `Value(int64)` | `int64` | `0` / `1` | | 26 | `label_str` | `binary (String)` | `string` | `Value(string)` | `string` | `authentic` / `fake` (50/50) | *60,000 rows (50K train, 10K test). `label` (int) + `label_str` (string) are dual encodings of same binary target → use `label_str` for readability, `label` for loss. Links to **How to Use D `README.md:477`** and **Data Collection `README.md:499`**.* ### A.9 Quick Reference Matrix (File → Config → README Link) | Physical File Pattern | Config Name `load_dataset` | README Section | Primary Type Pattern | | :--- | :--- | :--- | :--- | | `Egyptian-Arabic/*.parquet` | `egyptian_arabic` | `README.md:173` | Single `string` | | `Egyptian_Arabic_Wikipedia_20230101/*.parquet` | `wikipedia` | `README.md:188` | Single `string` | | `egyptian_arabic_convs/*.parquet` | `convs` | `README.md:199` | `List` | | `egyptian-arabic-speech-dataset/*.parquet` | `speech_whisper` | `README.md:220` | `List>` + `List` | | `english-to-arabic/*.parquet` | `english_to_arabic` | `README.md:237` | `string` ×3 | | `english-to-arabic/train.csv` | `qa_trilingual` | `README.md:262` | `string` ×5 + `int64` ×4 | | `balanced/*.parquet` | `balanced` | `README.md:296` | `string` ×5 + `int64` ×5 + `double` ×2 + `string` label | | `unbalanced/*.parquet` | `unbalanced` | `README.md:296` | Same as balanced | | `uncategorized/*.parquet` | `uncategorized` | `README.md:296` | Same as balanced | | `part 1 data/*.parquet` | `reviews_spam` | `README.md:327` | `int64` ×10 + `double` ×7 + `string` ×9 | ### A.10 Storage & Interop Notes * **All Parquet are Snappy-compressed, dictionary-encoded strings.** Direct `pandas` or `datasets` reading handles UTF-8 automatically; do NOT open with `encoding='charmap'` (will corrupt `README.md:525`). * **HF `datasets` Feature Inference:** Files without `b'huggingface'` metadata (e.g., `wikipedia`) are inferred at load time; files with metadata (e.g., `balanced`, `convs`, `speech`) preserve exact `Sequence`/`Value` nesting. * **Recommended Validation Snippet (links to Contributing Guide `README.md:704`):** ```python import pyarrow.parquet as pq, pandas as pd pf = pq.ParquetFile(path) assert pf.schema_arrow.equals(expected_arrow_schema), "Schema mismatch — update README.md:150" df = pd.read_parquet(path) assert df.dtypes.to_dict() == expected_pandas_dtypes ``` * **Size hint:** `reviews_spam` widest → slowest to scan; `egyptian_arabic` tallest → stream with `streaming=True` (`README.md:411`). --- ## How to Use ### 1. Install ```bash pip install datasets pyarrow pandas transformers librosa soundfile ``` ### 2. Load Any Config ```python from datasets import load_dataset # Raw Egyptian text (23M) — streaming for large scale ds = load_dataset("ISLAM-PO/documents-Egyptian-Arabic", "egyptian_arabic", streaming=True) print(next(iter(ds["train"])) ) # Wikipedia wiki = load_dataset("ISLAM-PO/documents-Egyptian-Arabic", "wikipedia") # Conversations — parse JSON import json convs = load_dataset("ISLAM-PO/documents-Egyptian-Arabic", "convs") turns = json.loads(convs["train"][0]["text"]) # list of {role, content} # Balanced credibility classification balanced = load_dataset("ISLAM-PO/documents-Egyptian-Arabic", "balanced") # unbalanced, uncategorized same # Parallel translation mt = load_dataset("ISLAM-PO/documents-Egyptian-Arabic", "english_to_arabic") # QA trilingual qa = load_dataset("ISLAM-PO/documents-Egyptian-Arabic", "qa_trilingual") # Speech (Whisper) speech = load_dataset("ISLAM-PO/documents-Egyptian-Arabic", "speech_whisper") import numpy as np feats = np.stack(speech["train"][0]["input_features"]) # (80, 3000) # Reviews / Spam reviews = load_dataset("ISLAM-PO/documents-Egyptian-Arabic", "reviews_spam") ``` ### 3. Training Recipes **A. Egyptian LM Fine-tuning (e.g., `CAMeL-Lab/bert-base-arabic-camelbert-mix` or `Qwen`):** ```python from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, Trainer tok = AutoTokenizer.from_pretrained("CAMeL-Lab/bert-base-arabic-camelbert-msa") tok.pad_token = tok.eos_token def tokenize(batch): return tok(batch["text"], truncation=True, max_length=512) tok_ds = ds.map(tokenize, batched=True, remove_columns=["text"]) ``` **B. Translation (EN→ARZ) with NLLB or M2M100:** ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tok = AutoTokenizer.from_pretrained("facebook/nllb-200-distilled-600M") tok.src_lang = "eng_Latn" tok.tgt_lang = "arz_Arab" # or "ar_Arab" fallback ``` **C. ASR Fine-tuning Whisper (Egyptian):** ```python from transformers import WhisperProcessor, WhisperForConditionalGeneration processor = WhisperProcessor.from_pretrained("openai/whisper-small") model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small") model.config.forced_decoder_ids = processor.get_decoder_prompt_ids(language="ar", task="transcribe") # input_features already log-Mel, labels are token ids — no audio decoding needed # If using raw audio, use processor(audio, sampling_rate=16000) ``` **D. Fake Review + Sentiment Multi-task:** ```python # Use normalized_translated_review for Arabic BERT # label: 0/1 (fake vs authentic), sentiment_label: 3-way from sklearn.model_selection import train_test_split from transformers import AutoModelForSequenceClassification ``` **E. Wikipedia Quality Filter (Human vs Template):** ```python from datasets import load_dataset from transformers import AutoModelForSequenceClassification bal = load_dataset("ISLAM-PO/documents-Egyptian-Arabic", "balanced") # Train classifier on page_text → label, then filter egyptian_arabic corpus # High precision on unbalanced (93% template) reflects real-world deployment ``` --- ## Data Collection & Annotation | Config | Collection Method | Annotation | | :--- | :--- | :--- | | `egyptian_arabic` | Web crawl / social media scrape (likely Twitter/Quotes/Forums) 2011-2023 | No manual annotation (raw) | | `wikipedia` | `arz.wikipedia.org` dump `20230101` via WikiExtractor | No annotation | | `convs` | Synthetic/LLM-generated conversations seeded from Egyptian prompts (Socratic philosophy, FEP, VQ-VAE) | `role` tags (user/model) auto-labeled; multi-turn coherence human-verified (?) | | `speech_whisper` | Egyptian Arabic speech (Common Voice / YouTube / in-house) preprocessed to Whisper log-Mel; transcribed and tokenized with Whisper tokenizer | `labels` are token IDs from transcripts (likely human transcripts) | | `english_to_arabic` | **ArzEn** (Mendeley Data V3) + **Milion Token EGY Songs** (lyrics aligned) + **NADI 2024** (dialect identification shared task) | Human translated & aligned sentence pairs | | `qa_trilingual` | Curated knowledge QA, then professionally translated/localized to `ar`, `ar_eg`, `en` (3x parallel) + category ontology | Category/sub-category human assigned; language tag verified | | `balanced/unbalanced/uncategorized` | Egyptian Wikipedia revision history + bot detection via `top_editors` & `bots_editors_percentage` | Heuristic + manual verification: pages with template-translated boilerplate vs human prose | | `reviews_spam` | Yelp / Amazon style reviews translated to Egyptian Arabic + normalized variant + enriched with user/product temporal & behavioral metadata, sentiment scores via classifier, entropy, spam_hit_score | `label` (fake/authentic) via injection / behavioral heuristics + `sentiment_label` via sentiment model | **Language identification:** No explicit LID field; `arz` dominance verified via lexical markers (`مش`, `عايز`, `دلوقتي`, `كده`, `اوي`). --- ## Considerations, Bias & Limitations ### Bias * **Geographic:** Heavily Egyptian (Cairo) — underrepresents Upper Egypt / Delta sub-dialects. * **Temporal:** 2011-2023; language drift, slang, and new entities post-2023 not covered. * **Gender/Topic:** Wikipedia balanced configs show editor skew (`HitomiAkane` dominant creator, `GhalyBot` top editor) — reflects Wikipedia bot-creation bias, not population. * **Sentiment:** Reviews `positive` 51% vs `negative` 19% — optimism bias; `authentic` vs `fake` exactly 50/50 is artificial balancing. * **Translation:** NADI & ArzEn sources may have translationese; Egyptian translations sometimes literal. ### Limitations * **No audio in `speech`**: Only precomputed Mel features; raw wav not provided → cannot re-extract with different frontends. * **Encoding:** Original parquet contains UTF-8 Arabic; opening with `charmap`/`latin1` corrupts (observed in diagnostics). Always use `utf-8`. * **Duplicate:** `Milion_Token_EGY_Songs` appears twice (identical MD5); deduplicate before reporting corpus size. * **Wiki duplicates:** `egyptian_arabic` contains short entries like dates (`"November 2011"`, `"2023-03-27"`) and single words (`"أسرة"`) — filter via `len(text.split()) > 3` for LM quality. * **Convs JSON:** `text` is JSON string, not native `list` — must parse. Some turns contain escaped Unicode (`\u0645...`) and mixed MSA/Egyptian code-switch. * **No PII redaction guarantee:** User/product IDs are anonymized but review text may contain restaurant names/locations; do not attempt de-anonymization. * **Template-translated label noise:** `uncategorized` (569K) is noisier than `balanced` (20K curated); evaluate accordingly. ### Social Impact & Ethical * Positive: Enables inclusive NLP for 100M Egyptian speakers ignored by MSA-centric models; preserves dialectal heritage. * Risk: Fake-review classifier could be abused to evade detection; mitigate by not releasing user-level de-anonymization and by requiring ethical use (see Apache 2.0 + this card). * Recommendation: When deploying translation/conversation models, add disclosure that outputs are Egyptian dialect and may not be understood by all Arabic speakers. --- ## Intended Uses **Recommended:** * Egyptian dialect LLM pre-training / fine-tuning (7B-70B scale with streaming). * EN↔EGY machine translation, transliteration, dialect normalization. * Egyptian chatbot / instruction tuning (convs + qa_trilingual). * Wikipedia quality filtering before pre-training (use balanced classifier to filter template-translated pages). * Fake/spam review detection in Arabic e-commerce. * Low-resource ASR fine-tuning for Egyptian accent. **Out-of-Scope / Not Recommended:** * Direct deployment for legal, medical, or financial advice without domain fine-tuning & human review. * Using `uncategorized` alone for production credibility without validation on `balanced`/`unbalanced`. * Language identification beyond Egyptian (Levantine/Gulf not covered). --- ## Preprocessing & Cleaning Recommended pipeline used by maintainers (and suggested for users): ```python import re, json # 1. Deduplicate (Milion Token + general) def dedup(df, subset=["Egy","English"] if "Egy" in df.columns else ["text"]): return df.drop_duplicates(subset=subset) # 2. Filter short / header rows df = df[df["text"].str.len() > 10] df = df[df["text"] != "text"] # wikipedia header artifact # 3. Normalize Arabic (for reviews_spam you already have normalized_translated_review) arabic_norm = str.maketrans({"أ":"ا","إ":"ا","آ":"ا","ة":"ه","ى":"ي","ؤ":"و","ئ":"ي"}) def normalize_ar(text): return text.translate(arabic_norm) # 4. Parse convs import ast turns = json.loads(text) if text.startswith("[") else ast.literal_eval(text) # 5. For speech, stack import numpy as np log_mel = np.stack(example["input_features"]) # (80,3000) ``` **Source cleaning already applied:** Parquet shards are already tokenization-ready; no HTML tags remain in Wikipedia (WikiExtractor). Speech is already log-Mel normalized (mean ≈ -0.6 padding value for silence). --- ## Citation If you use this dataset, please cite the original sub-sources **and** this unified corpus: **Unified Corpus:** ```bibtex @dataset{eamc2026, title = {Egyptian Arabic Mega Corpus (EAMC) - 25M Unified Dataset}, author = {EAMC Curators}, year = {2026}, publisher = {Hugging Face}, url = {https://huggingface.co/datasets/ISLAM-PO/documents-Egyptian-Arabic}, license = {Apache-2.0} } ``` **Sub-sources:** ```bibtex @dataset{arzen2023, title = {ArzEn-MultiGenre: An aligned parallel dataset of Egyptian Arabic song lyrics, novels, and subtitles, with English translations}, author = {Al-Sabbagh, Rania}, year = {2023}, publisher = {Mendeley Data}, version = {V3}, doi = {10.17632/6k97jty9xg.3} } @inproceedings{nadi2024, title = {NADI 2024: The Fifth Nuanced Arabic Dialect Identification Shared Task}, author = {Abdul-Mageed, Muhammad and others}, booktitle = {Proceedings of ArabicNLP 2024}, year = {2024}, url = {https://aclanthology.org/2024.arabicnlp-1.79} } @misc{arz_wiki2023, title = {Egyptian Arabic Wikipedia Dump 20230101}, howpublished = {\url{https://dumps.wikimedia.org/arz/}}, year = {2023} } ``` For the `balanced/unbalanced/uncategorized` Wikipedia quality labels, cite this card and link to `arz.wikipedia.org` bot activity literature. --- ## License and upstream terms The repository-level metadata currently declares the **Apache License 2.0**, but this does not override the terms of the upstream datasets. Complete a source-by-source license and privacy audit before redistribution or commercial release. **You are free to:** * ✅ Use commercially * ✅ Modify, distill, fine-tune * ✅ Distribute, sublicense * ✅ Patent grant **You must:** * Include copy of license & attribution (this card + original citations where applicable) * State significant changes if you redistribute **Full text:** https://www.apache.org/licenses/LICENSE-2.0 ``` Copyright 2026 EAMC Curators Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ``` **Why Apache 2.0 vs. CC-BY / MIT?** Explicit patent grant + compatibility with model weights (unlike CC-BY-SA's share-alike). Ideal for fine-tuning open LLMs. --- ## Contributing Guide We welcome contributions! EAMC is community-maintained. Here's how to contribute — even if you are new to Hugging Face. ### Ways to Contribute | Type | Examples | | :--- | :--- | | **Data** | Add new Egyptian dialect parallel data, dialectal QA, or dialectal speech; extend `convs` with new topics | | **Cleaning** | Deduplication scripts, LID filtering, better normalization, toxicity filtering | | **Labels** | Fix `Template-translated` vs `Human` mispredictions, add NER/POS tags | | **Docs** | Improve this card, add training notebooks, benchmarks | | **Benchmarks** | Evaluate `whisper-small` vs `large` on `speech_whisper`, publish WER; MT BLEU on `english_to_arabic` | ### Step-by-Step (Git + HF) 1. **Fork & Clone** ```bash # On HF: click Fork → your namespace git lfs install git clone https://huggingface.co/datasets/ISLAM-PO/documents-Egyptian-Arabic cd documents-Egyptian-Arabic ``` 2. **Create Branch** ```bash git checkout -b feat/add-new-dialect-data ``` 3. **Add / Modify Data** * Place new parquet shards under appropriate folder (e.g., `new_submission/`) * Ensure schema matches config (column names + dtypes). Validate: ```python import pyarrow.parquet as pq print(pq.read_schema("new_file.parquet")) # Must match existing config schema ``` * Run checks: ```bash python scripts/validate.py # (we provide: checks rows, duplicates, utf-8) ``` 4. **Document** * Update `README.md` statistics tables & Data Instances section. * If adding source, add citation to `Citation` section. 5. **Commit & Push** ```bash git add . git commit -m "feat: add 10K Egyptian conversation turns on health domain" git push origin feat/add-new-dialect-data ``` 6. **Open PR** * Go to HF → `New Pull Request` → describe changes, link issue, report `rows_added`, `source_license` (must be Apache-2.0 compatible). * Wait for maintainer review (CI validates parquet & runs `datasets` loader). ### Contribution Requirements * **License:** New data must be Apache-2.0 or CC0 / Public Domain. No NC/ND. * **Quality:** No machine-generated spam; if synthetic, label `synthetic: true` in metadata. * **Privacy:** No PII. Anonymize user IDs; scrub phone/email. * **Language tag:** Use `arz` (or `ar_eg`) for Egyptian; add `language` column if trilingual. * **Code:** Python ≥3.8, use `datasets` + `pyarrow`. ### Reporting Issues * **Dataset errors:** Open `Discussion` → `New Issue` with `bug` label + shard + row index + snippet. * **Bias / Harm:** Use `Community` tab → flag with `ethical-concern`. * **Questions:** Discussions → Q&A. ### For Non-Coders * Use HF's **Dataset Viewer → Edit** to propose small fixes (via web UI → creates PR automatically). * Or upload via **Files → Add file → Upload** (drag parquet/CSV, we handle validation). ### Code of Conduct * Be respectful of dialect diversity (Cairene vs Saidi). * No harassment, no political content. * Focus on preserving Egyptian linguistic heritage. --- ## Maintainers & Acknowledgments **Curators:** Community contributors + EAMC maintainers. **Fundamental Sources:** Rania Al-Sabbagh (ArzEn), NADI 2024 organizers, `arz.wikipedia.org` editors & bots (`GhalyBot`, `HitomiAkane`, `MenoBot`), Yelp/Amazon review translators, Common Voice Egyptian contributors, Egyptian conversation curators. **Thanks:** Hugging Face Datasets team, Whisper team, CAMeL Lab. Contact: via HF Discussions or `your.email@example.com` --- ## Changelog * **v1.0 (2026-09-01):** Initial unified release. 9 configs, 25.4M rows, Apache 2.0, HF-ready README, deduplication note, Whisper-ready speech. * *Planned v1.1:* Add `streaming` examples, add `arrow` optimized viewer, add `ner_egy` config, add audio wavs for speech. --- ## Search Keywords / Tags **For Hugging Face Search & SEO (copy-paste):** > `egyptian arabic, egyptian dialect, arz, masri, مصري, عامية مصرية, لهجة مصرية, arabic dialect, dialectal arabic, low resource, translation, english to arabic, arabic to english, machine translation, NLLB, NADI, ArzEn, Wikipedia, arz wiki, conversational ai, chatbot, instruction tuning, LLM, pretraining, language modeling, ASR, whisper, speech recognition, speech to text, log mel, fake review, spam detection, sentiment analysis, credibility, bot detection, template translated, human generated, balanced dataset, unbalanced, Egyptian Arabic Wikipedia, QA, question answering, trilingual, code switching` **Hugging Face `tags` field (already in frontmatter):** `egyptian-arabic`, `egyptian-dialect`, `arz`, `masri`, `arabic`, `arabic-dialect`, `dialectal-arabic`, `low-resource-language`, `machine-translation`, `speech-recognition`, `asr`, `whisper`, `wikipedia`, `conversational-ai`, `chatbot`, `instruction-tuning`, `fake-review-detection`, `spam-detection`, `sentiment`, `nlp`, `north-africa`, `msa`, `code-switching`, `parallel-corpus` **Categories for Papers With Code:** `Text Classification`, `Machine Translation`, `Speech Recognition`, `Conversational`, `Sentiment Analysis` ---
**If this dataset helps your research, please ⭐ the repository and cite!** `#EgyptianArabic #MASRI #arz #ArabicNLP #LowResource #Apache2`