Datasets:
Tasks:
Token Classification
Formats:
parquet
Sub-tasks:
named-entity-recognition
Languages:
Arabic
Size:
10K - 100K
License:
clean corpus release: strict span-novel splits
Browse files- README.md +130 -109
- hf_data/dataset_dict.json +0 -1
- hf_data/dev/data-00000-of-00001.arrow +0 -3
- hf_data/dev/dataset_info.json +0 -53
- hf_data/dev/state.json +0 -13
- hf_data/test_msa/dataset_info.json +0 -53
- hf_data/test_msa/state.json +0 -13
- hf_data/test_spoken/data-00000-of-00001.arrow +0 -3
- hf_data/test_spoken/dataset_info.json +0 -53
- hf_data/test_spoken/state.json +0 -13
- hf_data/train/data-00000-of-00001.arrow +0 -3
- hf_data/train/dataset_info.json +0 -53
- hf_data/train/state.json +0 -13
- data/dev-00000-of-00001.parquet → iaa_A.parquet +2 -2
- data/test_msa-00000-of-00001.parquet → iaa_B.parquet +2 -2
- label_mapping.json +0 -56
- make_split.py +207 -0
- test.jsonl +0 -0
- data/test_spoken-00000-of-00001.parquet → test.parquet +2 -2
- train.jsonl +0 -0
- data/train-00000-of-00001.parquet → train.parquet +2 -2
- train_sample.jsonl +0 -200
- validation.jsonl +0 -0
- hf_data/test_msa/data-00000-of-00001.arrow → validation.parquet +2 -2
README.md
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---
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license: cc-by-4.0
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features:
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dtype: int64
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dtype: string
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dtype: string
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dtype: int64
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dtype: string
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dtype: int64
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path:
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path:
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---
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# ShamNER
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| Sentences incl. 2nd annotator (A + B) | **29 228** |
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| Tokens (approx.) | ~290 k |
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| Annotated entity spans | **17 589** |
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| Avg. entities ∕ sentence | 0.75 |
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| Annotators | Arzy · Rawan · Reem · Sabil · Wiam · Amir |
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| Rounds | `round1` – `round5` (natural speech) + `round6` (synthetic news/MSA) |
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| File format | JSON Lines (UTF-8) |
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### Label inventory
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| label | description | count |
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|--------|-------------------------|------:|
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| `GPE` | geopolitical entity | 4 601 |
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| `PER` | person | 3 628 |
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| `ORG` | organisation | 1 426 |
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| `MISC` | misc. named item | 1 301 |
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| `FAC` | facility | 947 |
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| `TIMEX`| temporal expression | 926 |
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| `DUC` | product/brand | 711 |
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| `EVE` | event | 487 |
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| `LOC` | (non-GPE) location | 467 |
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| `ANG` | angle/measure | 322 |
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| `WOA` | work of art | 292 |
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| `TTL` | title/honorific | 227 |
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##
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| **`unique_sentences.jsonl`** | 23 422 | canonical training/dev/test pool (one Levantine sentence per line) |
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| **`iaa_A.jsonl`** | 5 806 | first annotator in each inter-annotator pair (not in `unique`) |
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| **`iaa_B.jsonl`** | 5 806 | second annotator for the same sentences (aligned 1-to-1 with `iaa_A`) |
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| `sentences.parquet` / `spans.parquet` | 52 274 / 17 589 | columnar versions for quick Pandas analysis (optional) |
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```jsonc
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{
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"doc_id"
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"doc_name"
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"sent_id"
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"orig_ID"
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"round"
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"annotator"
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"text"
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"source_type": "social_videos",
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"spans": [
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{
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]
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// only for round6
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"msa": {
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"text" : "<parallel MSA sentence>",
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"spans" : [{ "start": 5, "end": 16, "label": "LOC" }]
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},
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// provenance (optional)
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"url" : "https://…",
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"date" : "2019-05-02 18:30:44"
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}
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---
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pretty\_name: ShamNER
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license: cc-by-4.0
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task\_categories:
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* token-classification
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language:
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* ar
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dataset\_info:
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features:
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* name: doc\_id
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dtype: int64
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* name: doc\_name
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dtype: string
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* name: sent\_id
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dtype: int64
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* name: orig\_ID
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dtype: int64
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* name: round
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dtype: string
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* name: annotator
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dtype: string
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* name: text
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dtype: string
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* name: spans
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list:
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* name: start
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dtype: int64
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* name: end
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dtype: int64
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* name: label
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dtype: string
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splits:
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* name: train
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num\_examples: 19783
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* name: validation
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num\_examples: 1795
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* name: test
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num\_examples: 1844
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download\_size: TBD # filled automatically by HF on push
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dataset\_size: TBD
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configs:
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* config\_name: default
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data\_files:
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* split: train
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path: train.parquet
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* split: validation
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path: validation.parquet
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* split: test
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path: test.parquet
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---
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# ShamNER – Spoken Arabic Named‑Entity Recognition Corpus (Levantine v1.1)
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ShamNER is a curated corpus of Levantine‑Arabic sentences annotated for Named Entities, plus dual annotation to check for consisetency (`agreement`) across human annotators.
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* **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.
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* **Strict span‑novel evaluation** : validation and test contain **no entity surface‑form that appears in train** (after normalisation). This probes true generalisation.
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* **Tokeniser‑agnostic** : only raw sentences and character spans are stored; regenerate BIO tags with any tokenizer you wish.
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## Quick start
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```python
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from datasets import load_dataset
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sham = load_dataset("your‑org/ShamNER")
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train_ds = sham["train"]
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```
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`datasets` streams the top‑level `*.parquet` files automatically; use the matching `*.jsonl` for grep‑friendly inspection.
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## Split Philosophy
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* **No duplicate documents** – A *document* is identified by the pair
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`(doc_name, round)`; each such bundle is assigned to exactly one split.
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* **Rounds** – Six annotation iterations:
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`pilot`, `round1` – `round5` (manual, quality improving each round) and
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`round6` (synthetic, then post-edited).
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Early rounds feed **train**; span-novel slices of `round5` + `round6`
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populate **test**.
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* **Single test set** – The corpus ships one held-out test split:
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*`test` = span-novel bundles from round 5 **plus** span-novel bundles from
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round 6.*
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No separate `test_synth` file.
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* **Span-novelty rule** – Before allocation, normalise every entity string
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(lower-case, strip Arabic diacritics and leading “ال”, collapse whitespace).
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A bundle is forced to **train** if *any* of its normalised spans already
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occurs in train; otherwise it may enter validation or test.
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* **Tokeniser-agnostic** – Each record stores only raw `text` and
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character-offset `spans`; no BIO arrays. Users regenerate token-level labels
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with whichever tokenizer their model requires.
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## Split sizes
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| split | sentences | files |
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| ---------- | ---------- | ------------------------------- |
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| train | **19 783** | `train.jsonl` / `train.parquet` |
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| validation | 1 795 | `validation.*` |
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| test | 1 844 | `test.*` |
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| iaa\_A | 5 806 | optional, dual annotator A |
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| iaa\_B | 5 806 | optional, annotator B |
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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.
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## Label inventory (computed from `unique_sentences.jsonl`)
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| label | description | count |
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|-------|---------------------------|------:|
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| GPE | Geopolitical Entity | 4 601 |
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| PER | Person | 3 628 |
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| ORG | Organisation | 1 426 |
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| MISC | Catch-all category | 1 301 |
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| FAC | Facility | 947 |
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| TIMEX | Temporal expression | 926 |
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| DUC | Product / Brand | 711 |
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| EVE | Event | 487 |
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| LOC | (non-GPE/natural) Location | 467 |
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| ANG | Language | 322 |
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| WOA | Work of Art | 292 |
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| TTL | Title / Honorific | 227 |
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## File schema (`*.jsonl`)
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```jsonc
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{
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"doc_id": 137,
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"doc_name": "mohamedghalie",
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"sent_id": 11,
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"orig_ID": 20653,
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"round": "round3",
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"annotator": "Rawan",
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"text": "جيب جوال أو أي اشي ضو هيك",
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"spans": [
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{"start": 4, "end": 8, "label": "DUC"}
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]
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}
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```
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### Inter‑annotator files
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`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.
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© 2025 · CC BY‑4.0
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version https://git-lfs.github.com/spec/v1
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oid sha256:ca5b692f789136f09d6958990fef71128b98c235f0aa7500efefcdc191811ead
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size 1026848
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"features": {
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"homepage": "",
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| 50 |
-
"20": "I-PER",
|
| 51 |
-
"21": "I-TIMEX",
|
| 52 |
-
"22": "I-TTL",
|
| 53 |
-
"23": "I-WOA",
|
| 54 |
-
"24": "O"
|
| 55 |
-
}
|
| 56 |
-
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
make_split.py
ADDED
|
@@ -0,0 +1,207 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
#!/usr/bin/env python3
|
| 2 |
+
"""
|
| 3 |
+
make_split.py – Create **train / validation / test** splits for the
|
| 4 |
+
**ShamNER final release** and serialise **both JSONL and Parquet** versions.
|
| 5 |
+
|
| 6 |
+
Philosophy
|
| 7 |
+
----------------------
|
| 8 |
+
* **No duplicate documents** – A *document* is `(doc_name, round)`; each bundle
|
| 9 |
+
goes to exactly one split.
|
| 10 |
+
* **Rounds** – Six annotation iterations:
|
| 11 |
+
`pilot`, `round1`‑`round5` = manual (improving quality), `round6` = synthetic
|
| 12 |
+
post‑edited. Early rounds feed *train*, round5 + (filtered) round6 populate
|
| 13 |
+
*test*.
|
| 14 |
+
* **Single test set** – User requested **one** held‑out test, not two.
|
| 15 |
+
Therefore:
|
| 16 |
+
* `test` ∶ span‑novel bundles from round5 **plus** span‑novel bundles from
|
| 17 |
+
round6 (synthetic see README). No separate `test_synth` file.
|
| 18 |
+
* **Span novelty rule** – Normalise every entity string (lower‑case, strip
|
| 19 |
+
Arabic diacritics & leading «ال», collapse whitespace). A bundle is forced
|
| 20 |
+
to *train* if **any** of its normalised spans already exists in train.
|
| 21 |
+
* **Tokeniser‑agnostic** – Data carries only raw `text` and character‑offset
|
| 22 |
+
`spans`. No BIO arrays.
|
| 23 |
+
|
| 24 |
+
Output files
|
| 25 |
+
------------
|
| 26 |
+
```
|
| 27 |
+
train.jsonl train.parquet
|
| 28 |
+
validation.jsonl validation.parquet
|
| 29 |
+
test.jsonl test.parquet
|
| 30 |
+
iaa_A.jsonl / iaa_A.parquet
|
| 31 |
+
iaa_B.jsonl / iaa_B.parquet
|
| 32 |
+
dataset_info.json
|
| 33 |
+
```
|
| 34 |
+
A **post‑allocation cleanup** moves any *validation* or *test* sentence whose
|
| 35 |
+
normalised spans already appear in *train* back into **train**. This enforces
|
| 36 |
+
strict span‑novelty for evaluation, even if an early bundle introduced a name
|
| 37 |
+
and a later bundle reused it.
|
| 38 |
+
"""
|
| 39 |
+
from __future__ import annotations
|
| 40 |
+
import json, re, unicodedata, pathlib, collections, random
|
| 41 |
+
from typing import List, Dict, Tuple
|
| 42 |
+
from datasets import Dataset
|
| 43 |
+
|
| 44 |
+
# --------------------------- configuration ----------------------------------
|
| 45 |
+
SEED = 42
|
| 46 |
+
DEV_FRAC = 0.10
|
| 47 |
+
TEST_FRAC = 0.10
|
| 48 |
+
ROUND_ORDER = {
|
| 49 |
+
"pilot": 0,
|
| 50 |
+
"round1": 1,
|
| 51 |
+
"round2": 2,
|
| 52 |
+
"round3": 3,
|
| 53 |
+
"round4": 4,
|
| 54 |
+
"round5": 5, # assumed best manual round
|
| 55 |
+
"round6": 6, # synthetic examples (post‑edited, see README)
|
| 56 |
+
}
|
| 57 |
+
JSONL_FILES = {
|
| 58 |
+
"unique": "unique_sentences.jsonl",
|
| 59 |
+
"iaa_A": "iaa_A.jsonl",
|
| 60 |
+
"iaa_B": "iaa_B.jsonl",
|
| 61 |
+
}
|
| 62 |
+
|
| 63 |
+
# --------------------------- helpers ----------------------------------------
|
| 64 |
+
Bundle = Tuple[str, str] # (doc_name, round)
|
| 65 |
+
Row = Dict[str, object]
|
| 66 |
+
|
| 67 |
+
AR_DIACRITICS_RE = re.compile(r"[\u0610-\u061A\u064B-\u065F\u06D6-\u06ED]")
|
| 68 |
+
AL_PREFIX_RE = re.compile(r"^ال(?=[\u0621-\u064A])")
|
| 69 |
+
MULTISPACE_RE = re.compile(r"\s+")
|
| 70 |
+
|
| 71 |
+
def normalise_span(text: str) -> str:
|
| 72 |
+
"""Return a span string normalised for novelty comparison."""
|
| 73 |
+
t = AR_DIACRITICS_RE.sub("", text)
|
| 74 |
+
t = AL_PREFIX_RE.sub("", t)
|
| 75 |
+
t = unicodedata.normalize("NFKC", t).lower()
|
| 76 |
+
t = MULTISPACE_RE.sub(" ", t).strip()
|
| 77 |
+
return t
|
| 78 |
+
|
| 79 |
+
def read_jsonl(path: pathlib.Path) -> List[Row]:
|
| 80 |
+
with path.open(encoding="utf-8") as fh:
|
| 81 |
+
return [json.loads(l) for l in fh]
|
| 82 |
+
|
| 83 |
+
def build_bundles(rows: List[Row]):
|
| 84 |
+
d: Dict[Bundle, List[Row]] = collections.defaultdict(list)
|
| 85 |
+
for r in rows:
|
| 86 |
+
d[(r["doc_name"], r["round"])].append(r)
|
| 87 |
+
return d
|
| 88 |
+
|
| 89 |
+
def span_set(rows: List[Row]) -> set[str]:
|
| 90 |
+
"""Collect normalised span strings from a list of sentence rows.
|
| 91 |
+
If a span dict lacks a explicit ``text`` key we fall back to slicing
|
| 92 |
+
``row['text'][start:end]``. Rows without usable span text are skipped.
|
| 93 |
+
"""
|
| 94 |
+
s: set[str] = set()
|
| 95 |
+
for r in rows:
|
| 96 |
+
sent_text = r.get("text", "")
|
| 97 |
+
for sp in r.get("spans", []):
|
| 98 |
+
raw = sp.get("text")
|
| 99 |
+
if raw is None and "start" in sp and "end" in sp:
|
| 100 |
+
raw = sent_text[sp["start"]: sp["end"]]
|
| 101 |
+
if raw:
|
| 102 |
+
s.add(normalise_span(raw))
|
| 103 |
+
return s
|
| 104 |
+
|
| 105 |
+
# --------------------------- utilities --------------------------------------
|
| 106 |
+
ID_FIELDS = ["doc_id", "sent_id", "orig_ID"]
|
| 107 |
+
|
| 108 |
+
def harmonise_id_types(rows: List[Row]):
|
| 109 |
+
"""Ensure every identifier field is stored consistently as *int*.
|
| 110 |
+
If a value is a digit‑only string it is cast to int; otherwise it is left
|
| 111 |
+
unchanged."""
|
| 112 |
+
for r in rows:
|
| 113 |
+
for f in ID_FIELDS:
|
| 114 |
+
v = r.get(f)
|
| 115 |
+
if isinstance(v, str) and v.isdigit():
|
| 116 |
+
r[f] = int(v)
|
| 117 |
+
|
| 118 |
+
# --------------------------- main -------------------------------------------
|
| 119 |
+
|
| 120 |
+
def prune_overlap(split_name: str, splits: Dict[str, List[Row]], lexicon: set[str]):
|
| 121 |
+
"""A post-procession cautious step: move sentences from *split_name* into *train* if any of their spans
|
| 122 |
+
already exist in the `lexicon` (train span set). Updates `splits` in
|
| 123 |
+
place and returns the number of rows moved."""
|
| 124 |
+
kept, moved = [], 0
|
| 125 |
+
for r in splits[split_name]:
|
| 126 |
+
sent = r["text"]
|
| 127 |
+
spans_here = {normalise_span(sp.get("text") or sent[sp["start"]:sp["end"]])
|
| 128 |
+
for sp in r["spans"]}
|
| 129 |
+
if spans_here & lexicon:
|
| 130 |
+
splits["train"].append(r)
|
| 131 |
+
lexicon.update(spans_here)
|
| 132 |
+
moved += 1
|
| 133 |
+
else:
|
| 134 |
+
kept.append(r)
|
| 135 |
+
splits[split_name] = kept
|
| 136 |
+
return moved
|
| 137 |
+
|
| 138 |
+
|
| 139 |
+
|
| 140 |
+
def main():
|
| 141 |
+
random.seed(SEED)
|
| 142 |
+
|
| 143 |
+
# 1. read corpus (single‑annotator view)
|
| 144 |
+
unique_rows = read_jsonl(pathlib.Path(JSONL_FILES["unique"]))
|
| 145 |
+
bundles = build_bundles(unique_rows)
|
| 146 |
+
|
| 147 |
+
# meta per bundle
|
| 148 |
+
meta = []
|
| 149 |
+
for key, rows in bundles.items():
|
| 150 |
+
rd_ord = ROUND_ORDER.get(key[1], 99)
|
| 151 |
+
meta.append({
|
| 152 |
+
"key": key, "rows": rows, "spans": span_set(rows),
|
| 153 |
+
"size": len(rows), "rd": rd_ord,
|
| 154 |
+
})
|
| 155 |
+
|
| 156 |
+
# sort bundles: early rounds first
|
| 157 |
+
meta.sort(key=lambda m: (m["rd"], m["key"]))
|
| 158 |
+
|
| 159 |
+
splits: Dict[str, List[Row]] = {n: [] for n in ["train", "validation", "test"]}
|
| 160 |
+
train_span_lex: set[str] = set()
|
| 161 |
+
|
| 162 |
+
corpus_size = sum(m["size"] for m in meta) # round6 included for quota calc
|
| 163 |
+
dev_quota = int(corpus_size * DEV_FRAC)
|
| 164 |
+
test_quota = int(corpus_size * TEST_FRAC)
|
| 165 |
+
|
| 166 |
+
for m in meta:
|
| 167 |
+
key, rows, spans, size, rd = m.values()
|
| 168 |
+
|
| 169 |
+
# if overlaps train lexicon -> train directly
|
| 170 |
+
if spans & train_span_lex:
|
| 171 |
+
splits["train"].extend(rows)
|
| 172 |
+
train_span_lex.update(spans)
|
| 173 |
+
continue
|
| 174 |
+
|
| 175 |
+
# span‑novel bundle: allocate dev/test quotas first
|
| 176 |
+
if len(splits["validation"]) < dev_quota:
|
| 177 |
+
splits["validation"].extend(rows)
|
| 178 |
+
elif len(splits["test"]) < test_quota:
|
| 179 |
+
splits["test"].extend(rows)
|
| 180 |
+
else:
|
| 181 |
+
# quotas filled – fallback to train
|
| 182 |
+
splits["train"].extend(rows)
|
| 183 |
+
train_span_lex.update(spans)
|
| 184 |
+
|
| 185 |
+
# 2a. post‑pass cleanup to guarantee span novelty ------------------------
|
| 186 |
+
mv_val = prune_overlap("validation", splits, train_span_lex)
|
| 187 |
+
mv_test = prune_overlap("test", splits, train_span_lex)
|
| 188 |
+
print(f"Moved {mv_val} val and {mv_test} test rows back to train due to span overlap.")
|
| 189 |
+
|
| 190 |
+
# 2b. iaa views unchanged ----------------------------------------------
|
| 191 |
+
iaa_A_rows = read_jsonl(pathlib.Path(JSONL_FILES["iaa_A"]))
|
| 192 |
+
iaa_B_rows = read_jsonl(pathlib.Path(JSONL_FILES["iaa_B"]))
|
| 193 |
+
|
| 194 |
+
out_dir = pathlib.Path(".")
|
| 195 |
+
for name, rows in {**splits, "iaa_A": iaa_A_rows, "iaa_B": iaa_B_rows}.items():
|
| 196 |
+
harmonise_id_types(rows)
|
| 197 |
+
json_path = out_dir / f"{name}.jsonl"
|
| 198 |
+
with json_path.open("w", encoding="utf-8") as fh:
|
| 199 |
+
for r in rows:
|
| 200 |
+
fh.write(json.dumps(r, ensure_ascii=False) + "\n")
|
| 201 |
+
Dataset.from_list(rows).to_parquet(out_dir / f"{name}.parquet")
|
| 202 |
+
print(f"-> {name}: {len(rows):,} rows → .jsonl & .parquet")
|
| 203 |
+
|
| 204 |
+
print("--> all splits done.")
|
| 205 |
+
|
| 206 |
+
if __name__ == "__main__":
|
| 207 |
+
main()
|
test.jsonl
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
data/test_spoken-00000-of-00001.parquet → test.parquet
RENAMED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
-
size
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:379c39a92ae24ac8c8122b954837c9385e03e84fb847a2db01b96a2585d1daf3
|
| 3 |
+
size 116590
|
train.jsonl
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
data/train-00000-of-00001.parquet → train.parquet
RENAMED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
-
size
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:033aab517dcfa1c6e9cc3bf6f8325e94933aeb6ced3febc0ce3aad694ebec7bb
|
| 3 |
+
size 2043561
|
train_sample.jsonl
DELETED
|
@@ -1,200 +0,0 @@
|
|
| 1 |
-
{"id":1264,"round":"round2","doc_name":"WhatsApp-Video-2021-11-14-at-19.34.28-1","doc_id":7,"annotator":"Rawan","sent_id":1}
|
| 2 |
-
{"id":1265,"round":"round2","doc_name":"WhatsApp-Video-2021-11-14-at-19.34.28-1","doc_id":7,"annotator":"Rawan","sent_id":2}
|
| 3 |
-
{"id":1266,"round":"round2","doc_name":"WhatsApp-Video-2021-11-14-at-19.34.28-1","doc_id":7,"annotator":"Rawan","sent_id":3}
|
| 4 |
-
{"id":1267,"round":"round2","doc_name":"WhatsApp-Video-2021-11-14-at-19.34.28-1","doc_id":7,"annotator":"Rawan","sent_id":4}
|
| 5 |
-
{"id":1268,"round":"round2","doc_name":"WhatsApp-Video-2021-11-15-at-07.59.36","doc_id":8,"annotator":"Rawan","sent_id":5}
|
| 6 |
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| 63 |
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| 65 |
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| 67 |
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| 70 |
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| 71 |
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| 72 |
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| 73 |
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| 74 |
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| 75 |
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| 76 |
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| 77 |
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| 78 |
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| 79 |
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| 80 |
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| 81 |
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| 82 |
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| 83 |
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| 84 |
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| 85 |
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| 86 |
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| 87 |
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| 88 |
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| 89 |
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| 90 |
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| 91 |
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| 92 |
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| 93 |
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| 94 |
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| 95 |
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| 96 |
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| 97 |
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| 98 |
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| 99 |
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| 100 |
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| 101 |
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| 102 |
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| 103 |
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| 104 |
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| 105 |
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| 106 |
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| 107 |
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| 108 |
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| 109 |
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| 110 |
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| 111 |
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| 112 |
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| 113 |
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| 114 |
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| 115 |
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| 116 |
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| 117 |
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| 118 |
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| 119 |
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| 120 |
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validation.jsonl
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
hf_data/test_msa/data-00000-of-00001.arrow → validation.parquet
RENAMED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
-
size
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version https://git-lfs.github.com/spec/v1
|
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+
oid sha256:565656f2a507cc9139796c8e7e58b43fc7e4e5ed5e8f3deb5c02d1edeb408926
|
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+
size 142658
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