Update README.md

README.md

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----
-license: mit
-dataset_info:
-  features:
-  - name: text
-    dtype: string
-  - name: length_category
-    dtype: string
-  - name: source
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 373887763
-    num_examples: 8000
-  download_size: 210093799
-  dataset_size: 373887763
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
----
+---
+license: mit
+task_categories:
+- feature-extraction
+- sentence-similarity
+- text-retrieval
+dataset_info:
+  features:
+  - name: text
+    dtype: string
+  - name: length_category
+    dtype: string
+  - name: source
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 373887763
+    num_examples: 8000
+  download_size: 210093799
+  dataset_size: 373887763
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+tags:
+- embedding
+- benchmark
+- long-context
+- deepinfra
+- rag
+- wikitext
+language:
+- en
+
+size_categories:
+- 1K<n<10K
+---
+
+
+# Variable Length Embedding Benchmark (VLEB)
+
+## Dataset Summary
+**VLEB (Variable Length Embedding Benchmark)** is a specialized dataset designed to evaluate the performance, latency, and stability of **embedding models and rerankers** across a wide spectrum of context lengths. 
+
+Unlike standard datasets that focus on short passages, VLEB provides a balanced distribution of text ranging from standard RAG chunks to maximum-context documents (up to 32k tokens). It is constructed from `wikitext-103-raw-v1` using a **smart-clipping strategy** that preserves semantic integrity without splitting words.
+
+This benchmark is essential for:
+- **Length Generalization:** Testing if models maintain semantic understanding as context grows.
+- **RAG Profiling:** Measuring encoding latency and memory usage at different bins.
+- **"Lost-in-the-Middle" Analysis:** Evaluating retrieval degradation in long-context windows.
+
+## Data Structure
+
+The dataset consists of **8,000 samples**, strictly balanced across 4 length categories (2,000 samples per bin). Token counts are calculated using the `Qwen/Qwen2.5-7B-Instruct` tokenizer.
+
+| Category | Token Range (Qwen) | Typical Use Case |
+| :--- | :--- | :--- |
+| **Short** | 512 - 2,048 | Standard RAG chunks, abstracts, news snippets. |
+| **Medium** | 2,048 - 8,192 | Full articles, technical reports, single-file code. |
+| **Long** | 8,192 - 16,384 | Multiple papers, book chapters, long legal contracts. |
+| **Very Long** | 16,384 - 32,000 | Entire books, massive documentation, stress testing context limits. |
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+# Load the full dataset
+dataset = load_dataset("ovuruska/variable-length-embedding-bench")
+
+# Filter for specific length requirements
+short_contexts = dataset.filter(lambda x: x['length_category'] == 'Short')
+very_long_contexts = dataset.filter(lambda x: x['length_category'] == 'Very Long')
+
+print(f"Sample Text ({very_long_contexts[0]['length_category']}):")
+print(very_long_contexts[0]['text'][:200] + "...")
+
+```
+
+## Construction Methodology
+
+1. **Source:** The dataset is derived from the `wikitext-103-raw-v1` corpus.
+2. **Stream Buffering:** The raw text was processed as a continuous stream rather than isolated lines.
+3. **Smart Clipping:** A buffer system accumulated tokens until a target length (randomly selected within bin ranges) was met. The text was then clipped at the exact token boundary and decoded back to string, ensuring **no words are split** and the text remains natural.
+4. **Validation:** All samples were re-tokenized to ensure they strictly fall within their assigned bin limits.
+
+## Citation
+
+If you use this dataset for benchmarking, please cite:
+
+```bibtex
+@misc{vleb_2026,
+  author = {DeepInfra Engineering Team},
+  title = {Variable Length Embedding Benchmark (VLEB)},
+  year = {2026},
+  publisher = {Hugging Face},
+  howpublished = {\url{[https://huggingface.co/datasets/ovuruska/variable-length-embedding-bench](https://huggingface.co/datasets/ovuruska/variable-length-embedding-bench)}}
+}
+
+```
+
+
+