Add dataset card

README.md

@@ -1,31 +1,110 @@
 ---
+language:
+  - en
+  - lv
+license: cc-by-sa-4.0
+task_categories:
+  - translation
+tags:
+  - parallel-corpus
+  - wikipedia
+  - wikimatrix
+  - en-lv
+  - latvian
+  - machine-translation
+  - labse
+  - faiss
+size_categories:
+  - 100K<n<1M
+source_datasets:
+  - wikipedia
+pretty_name: WikiMatrix EN-LV
 dataset_info:
   features:
-  - name: en
-    dtype: string
-  - name: lv
-    dtype: string
-  - name: score
-    dtype: float64
+    - name: en
+      dtype: string
+    - name: lv
+      dtype: string
+    - name: score
+      dtype: float64
   splits:
-  - name: train
-    num_bytes: 163686105
-    num_examples: 537732
-  - name: validation
-    num_bytes: 9093672
-    num_examples: 29874
-  - name: test
-    num_bytes: 9093672
-    num_examples: 29874
-  download_size: 124377242
-  dataset_size: 181873449
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: validation
-    path: data/validation-*
-  - split: test
-    path: data/test-*
+    - name: train
+      num_examples: 537732
+    - name: validation
+      num_examples: 29874
+    - name: test
+      num_examples: 29874
 ---
+
+# WikiMatrix EN-LV
+
+## Dataset Description
+
+**597,480** English-Latvian parallel sentence pairs mined from Wikipedia using the WikiMatrix methodology.
+
+### Method
+
+1. **Source**: English and Latvian Wikipedia article dumps (April 2026)
+2. **Embeddings**: [LaBSE](https://huggingface.co/sentence-transformers/LaBSE) (Language-agnostic BERT Sentence Embeddings)
+3. **Retrieval**: FAISS approximate nearest-neighbor search (IndexFlatIP)
+4. **Scoring**: Margin-based scoring — `margin(x, y) = cos(x, y) / [(Σ cos(x, nn_y) + Σ cos(y, nn_x)) / (2k)]`
+5. **Filtering**: Pairs with margin score ≥ 1.04 retained
+
+### Columns
+
+| Column | Type | Description |
+|--------|------|-------------|
+| `en` | string | English sentence |
+| `lv` | string | Latvian sentence |
+| `score` | float | Margin similarity score (higher = more confident alignment) |
+
+### Splits
+
+| Split | Examples |
+|-------|----------|
+| train | 537,732 |
+| validation | 29,874 |
+| test | 29,874 |
+
+### Quality Thresholds
+
+The `score` column can be used to filter for higher-quality pairs:
+
+| Threshold | Approx. Pairs | Quality |
+|-----------|---------------|---------|
+| ≥ 1.04 | 597,480 | All pairs (full dataset) |
+| ≥ 1.06 | ~500,000 | **Recommended for training** |
+| ≥ 1.10 | ~350,000 | High confidence |
+| ≥ 1.20 | ~150,000 | Very high confidence |
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("aimamba/WikiMatrix-en-lv")
+
+# Filter for high-quality pairs
+high_quality = ds["train"].filter(lambda x: x["score"] >= 1.06)
+print(f"High-quality pairs: {len(high_quality)}")
+```
+
+### License
+
+CC-BY-SA 4.0 (inherited from Wikipedia)
+
+### Citation
+
+```bibtex
+@misc{wikimatrix-en-lv-2026,
+  title={WikiMatrix EN-LV: English-Latvian Parallel Corpus from Wikipedia},
+  author={aimamba},
+  year={2026},
+  howpublished={\url{https://huggingface.co/datasets/aimamba/WikiMatrix-en-lv}},
+  note={597,480 sentence pairs mined using LaBSE + FAISS}
+}
+```
+
+### Acknowledgments
+
+- [LaBSE](https://arxiv.org/abs/2007.01852) — Feng et al., 2022
+- [WikiMatrix](https://arxiv.org/abs/1907.05791) — Schwenk et al., 2019 (methodology inspiration)
+- [FAISS](https://github.com/facebookresearch/faiss) — Facebook AI Research