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README.md

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+
+# Persian Poetry Dataset
+
+## Dataset Description
+
+### Overview
+
+This dataset contains a collection of Persian poems structured in a question-answering format. The dataset is derived from various Persian poets and their poems, providing a rich source for exploring Persian poetry in a structured manner suitable for machine learning applications, especially in natural language processing tasks like question answering.
+
+### Data Collection
+
+- **Data Collection Source:** The data is sourced from the [Ganjoor project](https://github.com/ganjoor/). The specific database file can be found in the [releases section](https://github.com/ganjoor/desktop/releases/tag/v2.81) of their GitHub repository.
+- **Time Period:** Oct-12-2023
+- **Collection Methods:** The data was collected by downloading the raw database file from the Ganjoor project's GitHub repository.
+
+### Data Structure
+
+The dataset is structured into a CSV file with the following columns:
+- `context`: A static string which is "Persian Poetry or شعر فارسی".
+- `question`: A string that asks for a sample poem from a specific poet in the format "یک نمونه از شعر [POET_NAME]".
+- `answer`: Text of a hemistich or verse.
+- `answer_start`: The starting character index of `answer` within `context` (Note: this is always -1 in the current dataset as `answer` is not a substring of `context`).
+
+### Data Example
+
+```plaintext
+context,question,answer,answer_start
+Persian Poetry or شعر فارسی,یک نمونه از شعر Hafez,"The sun's eye has tears, and it's from separation’s pain", -1
+```
+
+## Dataset Usage
+
+### Use Cases
+
+This dataset can be utilized for various Natural Language Processing and analysis tasks related to Persian poetry, such as:
+- Question Answering
+- Text Generation
+- Language Modeling
+- Style Analysis
+
+### Challenges & Limitations
+
+- The `answer_start` field is always -1 as the `answer` is not a substring of `context`. Depending on your use-case, you might need to adjust how `context` and `answer_start` are determined.
+- The dataset might contain long verses that are over 100 characters.
+
+### License
+
+GPL (GNU General Public License)
+
+## Additional Information
+
+### Citation
+
+```
+Persian Poetry Dataset. Collected by Kakooch from the Ganjoor Project. Available at: https://huggingface.co/datasets/persian_poetry
+```
+
+### Dataset Link 
+
+[Download the dataset from Hugging Face](https://huggingface.co/datasets/persian_poetry)
+
+### Contact 
+
+Email: [[email protected]](mailto:[email protected]) | GitHub: [kakooch](https://github.com/kakooch)
+
 ---
-license: gpl-2.0
----
+
+*This README was generated by Kakooch.*

persian_poetry-qa.py

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+import pandas as pd
+from datasets import DatasetBuilder, SplitGenerator, Split, Features, Value
+
+class PersianPoetry(DatasetBuilder):
+    VERSION = "1.0.0"
+    
+    def _info(self):
+        return datasets.DatasetInfo(
+            description="This dataset contains a rich collection of Persian poems along with metadata about the poets and the verses.",
+            features=Features({
+                'context': Value('string'),
+                'question': Value('string'),
+                'answer': Value('string'),
+                'answer_start': Value('int32'),
+            }),
+            homepage="https://github.com/ganjoor/desktop/releases/tag/v2.81",
+            citation="""Persian Poetry Dataset. Collected by Kakooch from the Ganjoor Project. Available at: https://huggingface.co/datasets/persian_poetry""",
+        )
+
+    def _split_generators(self, dl_manager):
+        # Path to your CSV file
+        csv_path = poems-qa.csv"
+        return [
+            SplitGenerator(
+                name=Split.TRAIN,
+                gen_kwargs={
+                    "filepath": csv_path
+                }
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        df = pd.read_csv(filepath)
+        for idx, row in df.iterrows():
+            yield idx, {
+                'context': row['context'],
+                'question': row['question'],
+                'answer': row['answer'],
+                'answer_start': row['answer_start'],
+            }