docs(README): rewrite dataset READMEs for clarity and alignment

README.md

@@ -47,9 +47,7 @@ size_categories:
 
 # mini-recurrence-converter-dsl-dataset
 
-This dataset contains natural language recurrence expressions paired with symbolic DSL function calls, formatted in a structured `system → in → out` format. It is designed for training models to convert everyday recurrence descriptions (e.g. "every Tuesday at 8am") into structured, executable DSL representations.
-
-It was uploaded using the [`fifo-tool-datasets`](https://github.com/gh9869827/fifo-tool-datasets) utility with the `dsl` adapter, which supports structured `system → in → out` triplets.
+This dataset contains natural language recurrence expressions paired with symbolic DSL function calls, formatted in a structured `system → in → out` format. It is used for prototyping models that convert English recurrence descriptions (e.g., "every Tuesday at 8am") into structured, executable DSL representations.
 
 ---
 
@@ -57,10 +55,12 @@ It was uploaded using the [`fifo-tool-datasets`](https://github.com/gh9869827/fi
 
 The dataset uses a **wide format** with the following three columns:
 
-- `system` — the system prompt defining the role of the model  
+- `system` — the system prompt that defines the model's task
 - `in` — the natural language recurrence string (e.g. `"every Tuesday at 8am"`)  
 - `out` — the corresponding DSL expression (e.g. `WEEKLY(1, [TU], TIME(8, 0))`)
 
+Each entry consists of a `system`, `in`, and `out` field, forming a structured input-output example.
+
 For all examples, the system prompt is set to:
 
 ```plaintext
@@ -81,28 +81,23 @@ $ You are a precise parser of recurring schedule expressions. Your only job is t
 
 The DSL used in this dataset is implemented in the [Mini Recurrence Converter DSL module](https://github.com/gh9869827/fifo-dev-dsl/tree/main/fifo_dev_dsl/domain_specific/mini_recurrence_converter_dsl).
 
-It provides:
-
-- 🧠 A symbolic DSL to represent natural language recurrence patterns  
-- 📅 A Python engine to evaluate the DSL into structured recurrence rules  
-- 🤖 Integration with LLMs to convert natural language into DSL code  
-- 🔒 A safe alternative to arbitrary code execution — only predefined DSL functions are supported, so untrusted model output **cannot execute arbitrary code**
-
 Example:
 
 ```python
-from fifo_dev_dsl.domain_specific.mini_recurrence_converter_dsl import MiniRecurrenceConverterDSL
+from fifo_dev_dsl.domain_specific.mini_recurrence_converter_dsl.core import MiniRecurrenceConverterDSL
+
 dsl = "WEEKLY(1, [MO, WE], TIME(10, 0))"
 parsed = MiniRecurrenceConverterDSL().parse(dsl)
+
 print(parsed)
-# Output: structured recurrence rule object
+# RecurrenceRule(unit=<RecurrenceUnit.WEEKLY>, frequency=1, days=[0, 2], day_of_month=None, month=None, occurrence=None, hour=10, minute=0)
 ```
 
 ---
 
 ## 🚀 Using the Dataset
 
-To load the dataset in your training or fine-tuning pipeline:
+To load the dataset:
 
 ```python
 from fifo_tool_datasets.sdk.hf_dataset_adapters.dsl import DSLAdapter
@@ -111,21 +106,21 @@ print(dataset_dict["train"])
 print(dataset_dict["validation"])
 ```
 
-The test split enables functional evaluation by comparing model-generated DSL outputs against expected outcomes.
-
 ---
 
-## ⚠️ Limitations
+## 🔧 Upload & Editing Tools
 
-This dataset does not cover all natural language recurrence phrasings or edge cases. It focuses on representative examples of common recurring schedule expressions. Users should evaluate and extend it as needed for their specific use case.
+Uploaded via [`fifo-tool-datasets`](https://github.com/gh9869827/fifo-tool-datasets) using the `dsl` adapter.
+
+You can edit or extend the dataset using its `.dat` format and CLI tools.
 
 ---
 
-## 🔧 Upload & Editing Tools
+## ⚠️ Limitations
 
-This dataset was uploaded using [`fifo-tool-datasets`](https://github.com/gh9869827/fifo-tool-datasets), which supports conversion between `.dat` files and Hugging Face-compatible `datasets.Dataset` objects.
+This dataset does not cover all natural language recurrence phrasings or edge cases. It focuses on illustrative examples of common recurring schedule expressions. Users should evaluate and extend it as needed for their specific use case.
 
-To re-import the dataset as a `.dat` file or update it, you can use the CLI or Python SDK included in that tool.
+This dataset is provided as-is, without any warranties, express or implied. The authors and contributors assume no responsibility for its accuracy or suitability for any purpose.
 
 ---