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Initial commit.
Browse files- README.md +71 -0
- extractive-qa_PQuAD.jsonl +0 -0
- keyword-extraction_SynKeywords.jsonl +0 -0
- ner_arman.jsonl +0 -0
- nli_farstail.jsonl +0 -0
- paraphrase-detection_FarsiParaphraseDetection.jsonl +0 -0
- paraphrase-detection_parsinlu.jsonl +0 -0
- sentiment-analysis_deepsentipers.jsonl +0 -0
- sts_FarSICK.jsonl +0 -0
- sts_SynPerSTS.jsonl +0 -0
- tone-classification_SynTone.jsonl +0 -0
- topic-classification_sid.jsonl +0 -0
README.md
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---
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license: cc-by-nc-nd-4.0
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---
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---
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license: cc-by-nc-nd-4.0
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language: [fa]
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tags:
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- nlu
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- text-classification
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- natural-language-inference
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- named-entity-recognition
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- paraphrase-identification
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- semantic-textual-similarity
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- extractive-question-answering
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- keyword-extraction
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- persian
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pretty_name: Persian NLU Benchmark
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task_categories:
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- text-classification
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- natural-language-inference
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- semantic-similarity-classification
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- named-entity-recognition
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- paraphrase-identification
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- question-answering
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- keyphrase-extraction
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---
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# Dataset Card for Persian NLU Benchmark
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## Dataset Summary
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The **Persian NLU Benchmark** is a curated collection of existing Persian datasets designed to evaluate **Natural Language Understanding (NLU)** capabilities across a diverse range of tasks. It provides a unified benchmark suite to assess different cognitive aspects of large language models (LLMs) in Persian.
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This benchmark includes the following tasks and datasets:
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- **Text Classification**:
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- *Synthetic Persian Tone*
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- *SID*
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- **Natural Language Inference (NLI)**:
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- *FarsTAIL*
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- **Semantic Textual Similarity (STS)**:
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- *Synthetic Persian STS*
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- *FarSICK*
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- **Named Entity Recognition (NER)**:
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- *Arman*
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- **Paraphrase Detection**:
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- *FarsiParaphraseDetection*
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- *ParsiNLU*
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- **Extractive Question Answering (EQA)**:
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- *PQuAD*
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- **Keyword Extraction**:
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- *Synthetic Persian Keywords*
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Each task highlights a distinct aspect of model performance, allowing researchers to evaluate and compare models across a broad range of NLU capabilities.
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---
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## Languages
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- `fa` — Persian (Farsi)
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---
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## License
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This dataset is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).
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You must give appropriate credit, may not use it for commercial purposes, and may not distribute modified versions of the dataset.
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For details, see the [license](LICENSE) file or https://creativecommons.org/licenses/by-nc-nd/4.0/
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paraphrase-detection_parsinlu.jsonl
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sentiment-analysis_deepsentipers.jsonl
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sts_FarSICK.jsonl
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tone-classification_SynTone.jsonl
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topic-classification_sid.jsonl
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