Update README.md

README.md

@@ -2,4 +2,119 @@
 license: other
 license_name: open-data-attribution-training-disclosure-license-odatl-1.0
 license_link: LICENSE
+language:
+- en
+tags:
+- tiny
+- english
 ---
+# LLM-English-100MB — Compact & Dense English Teaching Corpus
+
+A 100MB, extremely clean CSV designed to teach an LLM English from scratch via instruction-tuning. No noise, no HTML, no duplicates — just pure grammar, vocabulary, and syntax transformations.
+
+Generated with a single paste-and-run Python script in Google Colab.
+
+### Why this teaches English
+
+Instead of raw text, the dataset is **instruction -> input -> output** pairs that force the model to learn rules:
+
+- **Grammar Mechanics:** tense conversion, negation, question formation, active/passive, subject-verb agreement, article & preposition usage, contraction
+- **Vocabulary:** pluralization, synonyms, antonyms, definitions, comparatives
+- **Error Correction:** common ESL mistakes with corrected form + rule
+
+Each row includes an explicit `rule` column so the model learns the *pattern*, not just memorizes.
+
+### Dataset Schema
+
+Extremely clean: UTF-8, LF (`\n`), `QUOTE_MINIMAL`, no empty fields, no newlines inside fields.
+
+| Column | Type | Description |
+| :--- | :--- | :--- |
+| `id` | int | Unique row ID |
+| `level` | string | CEFR level: A1, A2, B1, B2 |
+| `category` | string | `grammar`, `vocabulary`, `syntax` |
+| `task_type` | string | e.g. `tense_past`, `pluralization`, `negation`, `question_formation`, `comparative`, `active_passive`, `article_usage`, `synonym`, `antonym` |
+| `instruction` | string | What the model must do |
+| `input_text` | string | Input sentence / prompt |
+| `output_text` | string | Correct target |
+| `rule` | string | Short linguistic rule |
+
+Example:
+```
+instruction: Convert to simple past tense.
+input_text: We build the house.
+output_text: We built the house.
+rule: build -> built
+```
+
+### Stats
+
+- **Size:** 100 MB (configurable)
+- **Rows:** ~650k - 750k (avg ~150 bytes/row)
+- **Format:** CSV
+- **Encoding:** UTF-8
+- **Generation Time:** ~90-120 seconds on Colab
+
+### How to Use
+
+**Pandas:**
+```python
+import pandas as pd
+df = pd.read_csv("/content/llm_english_100MB.csv")
+df.head()
+```
+
+**Hugging Face Datasets:**
+```python
+from datasets import load_dataset
+ds = load_dataset("csv", data_files="/content/llm_english_100MB.csv")["train"]
+# Format for instruction tuning: instruction + input -> output
+def format_prompt(ex):
+    return {"text": f"### Instruction: {ex['instruction']}\n### Input: {ex['input_text']}\n### Output: {ex['output_text']}"}
+ds = ds.map(format_prompt)
+```
+
+**Training Prompt Template:**
+```
+Below is an instruction that teaches English.
+
+### Instruction:
+Convert to simple past tense.
+
+### Input:
+She eats the apple.
+
+### Output:
+She ate the apple.
+```
+
+### Quality Guarantees — EXTREMELY CLEAN
+
+- No HTML, no URLs, no emojis
+- No nulls / NaNs
+- Stripped whitespace, no `\r`, no `\n` inside fields
+- Deterministic seed (42) for reproducibility
+- Validated with `csv.DictReader`
+- All rows are synthetic and license-free
+
+### Customize
+
+Edit vocab lists at top of script:
+```python
+NOUNS = [...]
+VERBS = [...]
+```
+
+Add new generators to `GENERATORS` list to add new task types.
+
+### License
+
+Open Data Attribution Training Disclosure License (ODATL‑1.0)
+
+### File Structure
+
+```
+/content/
+  llm_english_100MB.csv  # 100MB corpus
+  README.md              # this file
+```