Create README.md

README.md

@@ -1,3 +1,177 @@
----
-license: mit
----
+---
+license: mit
+task_categories:
+- text-classification
+language:
+- en
+tags:
+- classification,
+- sentiment-analysis,
+- binary-classification,
+- complex-text
+- jsonl
+size_categories:
+- 100K<n<1M
+---
+Excellent — congrats on getting the repo ready 🚀
+Here’s a **professional Hugging Face Dataset Card (README.md)** you can paste directly into your repository.
+
+This is written to match HF best practices and serious research usage.
+
+---
+
+# 📘 README.md
+
+👉 Copy everything below into your `README.md`
+
+---
+
+# Sentiment-Analysis-Complex
+
+## 🧠 Overview
+
+**Sentiment-Analysis-Complex** is a large-scale synthetic sentiment analysis dataset designed for benchmarking modern NLP models under long-context, noisy, and semi-structured text conditions.
+
+The dataset contains **10 million labeled samples** with:
+
+* Long text sequences (**20–40 tokens per sample**)
+* Grammar-based sentence construction
+* Internet slang and hashtags
+* Rich vocabulary diversity
+* Balanced binary sentiment labels
+
+It is optimized for:
+
+* Transformer benchmarking
+* Tokenizer stress testing
+* Long-context modeling
+* Robustness evaluation
+* Large-scale NLP pipelines
+
+---
+
+## 📦 Dataset Structure
+
+```
+Sentiment-Analysis-Complex/
+ ├── train.jsonl   (8,000,000 samples)
+ ├── test.jsonl    (2,000,000 samples)
+ └── README.md
+```
+
+Split ratio:
+
+* **Train:** 80%
+* **Test:** 20%
+
+---
+
+## 🧾 Data Format
+
+Each line is a JSON object:
+
+```json
+{
+  "id": 123456,
+  "text": "I really love how this system consistently delivers smooth reliable performance and scalable architecture with intuitive workflow and strong documentation lol #innovation",
+  "label": "positive"
+}
+```
+
+### Fields
+
+| Field   | Type    | Description                              |
+| ------- | ------- | ---------------------------------------- |
+| `id`    | Integer | Unique sample identifier                 |
+| `text`  | String  | Input sentence (20–40 tokens)            |
+| `label` | String  | Sentiment class (`positive`, `negative`) |
+
+Encoding: UTF-8 (emoji and special characters supported)
+
+---
+
+## 📊 Dataset Characteristics
+
+* ✔️ Total samples: **10,000,000**
+* ✔️ Classes: **positive / negative (balanced)**
+* ✔️ Sequence length: **20–40 tokens**
+* ✔️ Vocabulary size: ~300+ words
+* ✔️ Includes slang and hashtags
+* ✔️ Grammar-driven generation
+* ✔️ Streaming-friendly JSONL format
+
+---
+
+## 🔬 Intended Use
+
+This dataset is suitable for:
+
+* Sentiment classification benchmarking
+* Large-scale training pipelines
+* Tokenization analysis
+* Long-context modeling experiments
+* Data loading stress tests
+* Distributed training validation
+* Synthetic NLP research
+
+---
+
+## ⚠️ Limitations
+
+* Synthetic text — not reflective of natural human distribution.
+* Limited semantic depth and discourse structure.
+* No real-world bias modeling.
+* No multilingual coverage (English only).
+* No sarcasm or pragmatic reasoning.
+
+Not recommended for production sentiment systems.
+
+---
+
+## 🤗 How to Load
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("NNEngine/Sentiment-Analysis-Complex")
+print(dataset)
+```
+
+Streaming mode:
+
+```python
+dataset = load_dataset(
+    "NNEngine/Sentiment-Analysis-Complex",
+    streaming=True
+)
+```
+
+---
+
+## 🏷️ Tags
+
+```
+sentiment-analysis
+nlp
+synthetic-data
+large-scale
+text-classification
+benchmark
+huggingface-dataset
+long-context
+```
+
+---
+
+## 📜 License
+
+MIT License
+Free for research, education, and experimentation.
+
+---
+
+## ✨ Author
+
+Created by **NNEngine** for large-scale NLP benchmarking and experimentation.
+
+---