Update README.md

README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+task_categories:
+- feature-extraction
+- text-classification
+- token-classification
+- translation
+- text-generation
+- summarization
+- text2text-generation
+language:
+- sa
+tags:
+- code
+pretty_name: Śihva Mahāpurāṇa
+size_categories:
+- 10K<n<100K
+---
+# Dataset Card for Shiv_Mahapuran
+
+## Dataset Details
+
+### Dataset Description
+
+This dataset contains a complete, structured representation of the Śiva Mahāpurāṇa (often called Śivapurāṇa) in CSV format. It is broken down into Saṃhitās (seven surviving Saṃhitās), Khaṇḍas, Adhyāyas, and individual ślokas, enabling fine-grained NLP work on classical Sanskrit scripture.
+- **Curated by:** [Aluminium](https://huggingface.co/13Aluminium)  
+- **Organization:** [Snskrt](https://huggingface.co/snskrt)  
+- **Shared by:** [Snskrt](https://huggingface.co/snskrt)  
+- **Language(s):** Sanskrit (ISO code: sa)  
+- **License:** Apache-2.0  
+- **Size:** ~24,489 ślokas 
+
+### Dataset Sources
+
+- **Repository:** [huggingface.co/datasets/snskrt/Shiv_Mahapuran](https://huggingface.co/datasets/snskrt/Shiv_Mahapuran)  
+
+## Uses
+
+### Direct Use
+
+- Training and evaluating Sanskrit language models on classical hymn/text generation  
+- Building Sanskrit question-answering systems over Purāṇic content  
+- Machine translation between Sanskrit and modern languages  
+- Summarization and feature extraction of mythological scripture  
+
+### Out-of-Scope Use
+
+- Modern colloquial or conversational Sanskrit tasks  
+
+## Dataset Structure
+
+Each record in the CSV/JSON has these fields:
+
+- `samhita` (string): Name of the Saṃhitā, e.g. `"Rudrasaṃhitā"`  
+- `khanda` (string): Khanda name, e.g. `"Parvati kand"`  
+- `khanda_number` (string): Khanda name, e.g. `"1"`  
+- `adhyay` (string): Adhyāya title or number, e.g. `"1.1"`    
+- `shloka_number` (int): Position of the śloka within the Adhyāya  
+- `shloka_text` (string): Full Sanskrit text of the śloka  
+
+## Dataset Creation
+
+### Curation Rationale
+
+To supply researchers and developers with a fully parsed, program-friendly version of the Śiva Mahāpurāṇa, facilitating a range of NLP tasks on one of Hinduism’s major Purāṇas.
+### Source Data
+
+#### Data Collection and Processing
+
+- Raw Sanskrit text sourced from critical editions of the Śiva Mahāpurāṇa  
+- Divided into JSON Saṃhitā → Khanda → Adhyāya → śloka hierarchy  
+- Converted to CSV via Python scripts, preserving khanda-level structure and normalizing field names  
+
+#### Who are the source data producers?
+
+Original verses are attributed to Vyāsa; digital encoding and structuring by Snskrt.
+
+## Bias, Risks, and Limitations
+
+- Classical text only—no modern translations or commentary included.  
+- Possible editorial or typographical errors from digitization.
+
+---