Update README.md

README.md

@@ -43,34 +43,39 @@ The release also includes the labels predicted for the same sentences by GPT-5.5
 BAREC-X was introduced in *Benchmarking Fine-Grained Readability Explanations for Arabic* (Rabih, Elmadani, Taha-Thomure and Habash). Project page: https://barec.camel-lab.com/
 
 
-## Contents
+## Content
 
-| Config | Rows | What it holds |
+|| Config | Rows | What it holds |
 |---|---|---|
 | `annotations` (default) | 1,793 | Human annotations: sentence, readability level, labels. |
-| `gpt` | 1,793 | Labels predicted by GPT. TODO: exact model version and prompting setup. |
-| `gemini` | 1,793 | Labels predicted by Gemini. TODO: exact model version and prompting setup. |
-| `rule_based` | 1,793 | Labels predicted by the rule-based system. TODO: one line on the system. |
+| `gpt` | 1,793 | Labels predicted by GPT-5.5 with four-shot prompting. |
+| `gemini` | 1,793 | Labels predicted by Gemini-3-Flash with four-shot prompting. |
+| `rule_based` | 1,793 | Labels predicted by the training-free rule-based system. |
 | `labels` | 148 | The label inventory: code, dimension, level, Arabic description. |
 
 The four sentence tables have the same 1,793 rows in the same order and share the `id` column, so they can be joined on `id` or compared row by row.
 
+All three systems receive the sentence and its gold readability level and return the reasons that justify that level.
+The LLM prompts contain a task description, the BAREC guideline excerpt for the sentence's level, and four example sentences with their gold reasons.
+The rule-based system extracts its features with CAMeL Tools 1.5.6 and CamelParser 2.0 and returns the activated reasons whose guideline level matches the given level.
+
+
 ## Loading
 
 ```python
 from datasets import load_dataset
 
 repo = "TODO-user/TODO-name"
-gold = load_dataset(repo, split="train")                 # human annotations
-gpt = load_dataset(repo, "gpt", split="train")           # also "gemini", "rule_based"
-labels = load_dataset(repo, "labels", split="train")     # code -> Arabic description
+gold = load_dataset(repo)                 # human annotations
+gpt = load_dataset(repo, "gpt")           # also "gemini", "rule_based"
+labels = load_dataset(repo, "labels")     # code -> Arabic description
 ```
 
 ## `annotations`
 
 | Column | Type | Description |
 |---|---|---|
-| `id` | string | Unique 11-digit sentence identifier. Stored as a string; do not cast to a number. |
+| `id` | string | Unique 11-digit sentence identifier. |
 | `sentence` | string | The Arabic sentence. |
 | `readability_level` | int | Readability level, 1–19. |
 | `spelling` | list of string | Spelling label codes (prefix `O`). |
@@ -85,7 +90,7 @@ A dimension with no label is an empty list. A sentence can carry more than one l
 Example row:
 
 ```json
-{"id": "10100060071", "sentence": "لا تقولي \"نعم\" أو \"لا\".. أو \"من فضلك\".. أو \"شكرًا\"..", "readability_level": 8, "spelling": [], "word_count": [], "morphology": ["M8-4"], "syntax": ["S8-1"], "vocabulary": ["V8-2"], "content": []}
+{"id": "10100060071", "sentence": "عالم غريب المعالم", "readability_level": 11, "spelling": [], "word_count": [], "morphology": [], "syntax": ["S11-2"], "vocabulary": [], "content": []}
 ```
 
 ## `gpt`, `gemini`, `rule_based`