Update README.md

README.md

@@ -14,4 +14,47 @@ configs:
   data_files:
   - split: train
     path: data/train-*
+license: apache-2.0
+task_categories:
+- token-classification
+language:
+- uk
+size_categories:
+- 10M<n<100M
+tags:
+- silver-standard
+- ukrainian
+- NER
 ---
+
+# UberText-NER-Silver
+
+**UberText-NER-Silver** is a silver-standard named entity recognition (NER) dataset for the Ukrainian language. It was automatically annotated using a high-performance model trained on NER-UK 2.0 and covers over 2.5 million social media and web sentences. The dataset significantly expands the coverage of underrepresented entity types and informal domains.
+
+## Dataset Summary
+
+- **Total Sentences:** 2,573,205  
+- **Total Words:** 45,489,533  
+- **Total Entity Spans:** 4,393,316  
+- **Entity Types (13):** `PERS`, `ORG`, `LOC`, `DATE`, `TIME`, `JOB`, `MON`, `PCT`, `PERIOD`, `DOC`, `QUANT`, `ART`, `MISC`  
+- **Format:** IOB-style, token-level annotations
+
+## Source
+
+Texts were taken from the UberText 2.0 corpus social media part, filtered and preprocessed for noise reduction and duplication. The dataset includes both entity-rich and entity-free content to improve model generalization.
+
+
+## Example Usage
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("lang-uk/UberText-NER-Silver", split="train")
+print(dataset[0])
+```
+
+## Applications
+
+- Training large-scale NER models for Ukrainian
+- Improving performance in low-resource and informal text domains
+- Cross-lingual or transfer learning experiments