Add tokenlearn docs data for CornStack (CodeRankEmbed embeddings)

README.md

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+---
+dataset_info:
+- config_name: go
+  splits:
+  - name: train
+    num_bytes: 0
+    num_examples: 50000
+- config_name: java
+  splits:
+  - name: train
+    num_bytes: 0
+    num_examples: 50000
+- config_name: javascript
+  splits:
+  - name: train
+    num_bytes: 0
+    num_examples: 50000
+- config_name: php
+  splits:
+  - name: train
+    num_bytes: 0
+    num_examples: 50000
+- config_name: python
+  splits:
+  - name: train
+    num_bytes: 0
+    num_examples: 50000
+- config_name: ruby
+  splits:
+  - name: train
+    num_bytes: 0
+    num_examples: 50000
+configs:
+- config_name: go
+  data_files:
+  - split: train
+    path: go/train-*
+- config_name: java
+  data_files:
+  - split: train
+    path: java/train-*
+- config_name: javascript
+  data_files:
+  - split: train
+    path: javascript/train-*
+- config_name: php
+  data_files:
+  - split: train
+    path: php/train-*
+- config_name: python
+  data_files:
+  - split: train
+    path: python/train-*
+  default: true
+- config_name: ruby
+  data_files:
+  - split: train
+    path: ruby/train-*
+---
+
+# Pringled/cornstack-docs-tokenlearn Dataset Card
+
+This dataset was created with [Tokenlearn](https://github.com/MinishLab/tokenlearn) for training [Model2Vec](https://github.com/MinishLab/model2vec) models on code retrieval. It contains mean token embeddings produced by [nomic-ai/CodeRankEmbed](https://huggingface.co/nomic-ai/CodeRankEmbed), used as training targets for static embedding distillation.
+
+The dataset contains code documents from [CornStack](https://huggingface.co/datasets/nomic-ai/cornstack-python-v1) across 6 programming languages (50,000 rows per language, 300,000 total).
+
+## Dataset Details
+
+| Field | Value |
+|---|---|
+| **Source** | CornStack (nomic-ai) |
+| **Embedding model** | [nomic-ai/CodeRankEmbed](https://huggingface.co/nomic-ai/CodeRankEmbed) |
+| **Embedding dimension** | 768 |
+| **Languages** | Python, Java, PHP, Go, JavaScript, Ruby |
+| **Rows per language** | 50,000 |
+| **Total rows** | 300,000 |
+| **Field** | `document` |
+
+## Source Datasets
+
+| Language | Source |
+|---|---|
+| `python` | [nomic-ai/cornstack-python-v1](https://huggingface.co/datasets/nomic-ai/cornstack-python-v1) |
+| `java` | [nomic-ai/cornstack-java-v1](https://huggingface.co/datasets/nomic-ai/cornstack-java-v1) |
+| `php` | [nomic-ai/cornstack-php-v1](https://huggingface.co/datasets/nomic-ai/cornstack-php-v1) |
+| `go` | [nomic-ai/cornstack-go-v1](https://huggingface.co/datasets/nomic-ai/cornstack-go-v1) |
+| `javascript` | [nomic-ai/cornstack-javascript-v1](https://huggingface.co/datasets/nomic-ai/cornstack-javascript-v1) |
+| `ruby` | [nomic-ai/cornstack-ruby-v1](https://huggingface.co/datasets/nomic-ai/cornstack-ruby-v1) |
+
+## Dataset Structure
+
+| Column | Type | Description |
+|---|---|---|
+| `text` | `string` | Truncated input text (tokenizer max length 512) |
+| `embedding` | `list[float32]` | Mean token embedding from `nomic-ai/CodeRankEmbed`, excluding BOS/EOS tokens |
+
+## Usage
+
+Load a single language config:
+
+```python
+from datasets import load_dataset
+
+# Load Python code documents
+dataset = load_dataset("Pringled/cornstack-docs-tokenlearn", name="python")
+
+# Load all languages and concatenate
+from datasets import concatenate_datasets
+all_langs = concatenate_datasets([
+    load_dataset("Pringled/cornstack-docs-tokenlearn", name=lang)["train"]
+    for lang in ["python", "java", "php", "go", "javascript", "ruby"]
+])
+```
+
+## Creation
+
+Featurized from CornStack using [nomic-ai/CodeRankEmbed](https://huggingface.co/nomic-ai/CodeRankEmbed) with mean token pooling (BOS/EOS excluded). Two sampling seeds (42 and 100) were used with a 10k streaming shuffle buffer to maximise diversity. Texts are truncated to 512 tokens.
+
+## Library Authors
+
+Tokenlearn was developed by the [Minish](https://github.com/MinishLab) team consisting of [Stephan Tulkens](https://github.com/stephantul) and [Thomas van Dongen](https://github.com/Pringled).
+
+## Citation
+
+```
+@software{minishlab2024model2vec,
+  author       = {Stephan Tulkens and {van Dongen}, Thomas},
+  title        = {Model2Vec: Fast State-of-the-Art Static Embeddings},
+  year         = {2024},
+  publisher    = {Zenodo},
+  doi          = {10.5281/zenodo.17270888},
+  url          = {https://github.com/MinishLab/model2vec},
+  license      = {MIT}
+}
+```

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