README.md

---
pretty_name: ShamNER
license: cc-by-4.0
language:
- ar
annotations_creators:
- expert-annotated
task_categories:
- token-classification
task_ids:
- named-entity-recognition
multilinguality: monolingual
data_files:
  train: train.parquet
  validation: validation.parquet
  test: test.parquet
  iaa_A: iaa_A.parquet
  iaa_B: iaa_B.parquet
splits:
- name: train
  num_examples: 19532
- name: validation
  num_examples: 1931
- name: test
  num_examples: 1959
- name: iaa_A
  num_examples: 5806
- name: iaa_B
  num_examples: 5806
---
# ShamNER – Spoken Arabic Named‑Entity Recognition Corpus (Levantine v1.1)

ShamNER is a curated corpus of Levantine‑Arabic sentences annotated for Named Entities, plus dual annotation to check for consisetency (`agreement`) across human annotators. 

* **Rounds** : `pilot`, `round1`–`round5` (manual, as a rule quality improved across rounds) and `round6` (synthetic, post‑edited). The `sythentic` data is done by sampling label-rich annotated spans from an MSA project and writing it with an LLM while force-injecting the annotated spans. Native speakers of Arabic then edited the these chunks to see to it that they sound as fluent and dilactical as possible. They were instructed not to touch the annotated spans. A script validated that no spans were modified. 
* **Strict span‑novel evaluation** : validation and test contain **no entity surface‑form that appears in train** (after normalisation).  This probes true generalisation.
* **Tokeniser‑agnostic** : only raw sentences and character spans are stored; regenerate BIO tags with any tokenizer you wish.

## Quick start



```python
# Uncomment next line if you hit a LocalFileSystem / fsspec error on Colab
# !pip install -U "datasets>=2.16.0" "fsspec>=2023.10.0"
from datasets import load_dataset
sham = load_dataset("HebArabNlpProject/ShamNER")
train_ds = sham["train"]
```

`datasets` streams the top‑level `*.parquet` files automatically; use the matching `*.jsonl` for grep‑friendly inspection.

## Split Philosophy

* **No duplicate documents** – A *document* is identified by the pair  
  `(doc_name, round)`; each such bundle is assigned to exactly one split. This rule holds true for bundles, though individual sentences within bundles might have overlapping spans after post-allocation pruning for specific thresholds.

* **Rounds** – Six annotation iterations:  
  `pilot`, `round1` – `round5` (manual, quality improving each round) and  
  `round6` (synthetic, then post-edited).  
  Early rounds feed **train**; span-novel slices of `round5` + `round6`
  populate **test**.

* **Single test set** – The corpus ships one held-out test split:  
  *`test` = span-novel bundles from round 5 **plus** span-novel bundles from
  round 6.*  
  No separate `test_synth` file.

* **Span-novelty rule (Relaxed)**

Before allocation, normalise every entity string:
- Convert to lowercase (Latin aphbaet exists in social media)
- Strip Arabic diacritics
- Remove leading “ال”
- Collapse internal whitespace

A bundle is forced to **train** if **any** of its normalised spans already occurs in train.

A **post-allocation pruning** step then moves sentences from validation or test back to train  
**only if more than 50%** of their normalized spans already exist in the training set.

This threshold (**0.50**) was chosen to provide more learning examples to the model  
in the evaluation sets, leading to improved performance.

* **Tokeniser-agnostic** – Each record stores only raw `text` and
  character-offset `spans`; no BIO arrays.  Users regenerate token-level labels
  with whichever tokenizer their model requires.


## Split sizes

| split      | sentences  | files                           |
| ---------- | ---------- | ------------------------------- |
| train      | **19 532** | `train.jsonl` / `train.parquet` |
| validation | 1 931      | `validation.*`                  |
| test       | 1 931      | `test.*`                        |
| iaa\_A     | 5 806      | optional, dual annotator A      |
| iaa\_B     | 5 806      | optional, annotator B           |

Every sentence that appears in iaa_A.jsonl is also in the train split (with the same labels), while iaa_B.jsonl provides the alternative annotation for agreement/noise studies.

## Label inventory (computed from `unique_sentences.jsonl`)

| label | description  | count |
|-------|---------------------------|------:|
| GPE   | Geopolitical Entity       | 4 601 |
| PER   | Person                    | 3 628 |
| ORG   | Organisation              | 1 426 |
| MISC  | Catch-all category        | 1 301 |
| FAC   | Facility                  |   947 |
| TIMEX | Temporal expression       |   926 |
| DUC   | Product / Brand           |   711 |
| EVE   | Event                     |   487 |
| LOC   | (non-GPE/natural) Location        |   467 |
| ANG   | Language       |   322 |
| WOA   | Work of Art               |   292 |
| TTL   | Title / Honorific         |   227 |

## File schema (`*.jsonl`)

```jsonc
{
  "doc_id": 137,
  "doc_name": "mohamedghalie",
  "sent_id": 11,
  "orig_ID": 20653,
  "round": "round3",
  "annotator": "Rawan",
  "text": "جيب جوال أو أي اشي ضو هيك",
  "spans": [
    {"start": 4, "end": 8, "label": "DUC"}
  ]
}
```

### Inter‑annotator files

`iaa_A.jsonl` and `iaa_B.jsonl` contain parallel annotations for the same 5 806 sentences.  Use them to measure agreement or experiment with noise‑robust training.  These sentences **do not** overlap with the primary train/val/test splits. As stated above, only `iaa_A.jsonl` were injected into the train, dev and test set. 

© 2025 · CC BY‑4.0