add readme

README.md

@@ -28,4 +28,196 @@ task_categories:
 - text-classification
 task_ids:
 - natural-language-inference
----
+---
+
+# Dataset Card for "IndicParaphrase"
+
+## Table of Contents
+
+- [Dataset Card for "IndicParaphrase"](#dataset-card-for-indicparaphrase)
+  - [Table of Contents](#table-of-contents)
+  - [Dataset Description](#dataset-description)
+    - [Dataset Summary](#dataset-summary)
+    - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+    - [Languages](#languages)
+  - [Dataset Structure](#dataset-structure)
+    - [Data Instances](#data-instances)
+    - [Data Fields](#data-fields)
+    - [Data Splits](#data-splits)
+  - [Dataset usage](#dataset-usage)
+  - [Dataset Creation](#dataset-creation)
+    - [Curation Rationale](#curation-rationale)
+    - [Source Data](#source-data)
+      - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
+      - [Who are the source language producers?](#who-are-the-source-language-producers)
+      - [Human Verification Process](#human-verification-process)
+  - [Considerations for Using the Data](#considerations-for-using-the-data)
+    - [Social Impact of Dataset](#social-impact-of-dataset)
+    - [Discussion of Biases](#discussion-of-biases)
+    - [Other Known Limitations](#other-known-limitations)
+  - [Additional Information](#additional-information)
+    - [Dataset Curators](#dataset-curators)
+    - [Licensing Information](#licensing-information)
+    - [Citation Information](#citation-information)
+    - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** <https://github.com/divyanshuaggarwal/IndicXNLI>
+- **Paper:** [IndicXNLI: Evaluating Multilingual Inference for Indian Languages](https://arxiv.org/abs/2204.08776)
+- **Point of Contact:** [Divyanshu Aggarwal](mailto:divyanshuggrwl@gmail.com)
+
+### Dataset Summary
+. INDICXNLI is similar to existing
+XNLI dataset in shape/form, but focusses on Indic language family. INDICXNLI include NLI
+data for eleven major Indic languages that includes
+Assamese (‘as’), Gujarat (‘gu’), Kannada (‘kn’),
+Malayalam (‘ml’), Marathi (‘mr’), Odia (‘or’),
+Punjabi (‘pa’), Tamil (‘ta’), Telugu (‘te’), Hindi
+(‘hi’), and Bengali (‘bn’).
+
+### Supported Tasks and Leaderboards
+
+**Tasks:** Natural Language Inference
+
+**Leaderboards:** Currently there is no Leaderboard for this dataset.
+
+### Languages
+
+- `Assamese (as)`
+- `Bengali (bn)`
+- `Gujarati (gu)`
+- `Kannada (kn)`
+- `Hindi (hi)`
+- `Malayalam (ml)`
+- `Marathi (mr)`
+- `Oriya (or)`
+- `Punjabi (pa)`
+- `Tamil (ta)`
+- `Telugu (te)`
+
+## Dataset Structure
+
+### Data Instances
+
+One example from the `hi` dataset is given below in JSON format.
+
+  ```python
+ {'premise': 'अवधारणात्मक रूप से क्रीम स्किमिंग के दो बुनियादी आयाम हैं-उत्पाद और भूगोल।',
+ 'hypothesis': 'उत्पाद और भूगोल क्रीम स्किमिंग का काम करते हैं।',
+ 'label': 1 (neutral) }
+  ```
+
+### Data Fields
+
+- `premise (string)`: Premise Sentence
+- `hypothesis (string)`: Hypothesis Sentence
+- `label (integer)`: Integer label `0` if hypothesis `entails` the premise, `2` if hypothesis `negates` the premise and `1` otherwise.
+
+### Data Splits
+
+Below is the dataset split given for `hi` dataset.
+
+```python
+DatasetDict({
+    train: Dataset({
+        features: ['premise', 'hypothesis', 'label'],
+        num_rows: 392702
+    })
+    test: Dataset({
+        features: ['premise', 'hypothesis', 'label'],
+        num_rows: 5010
+    })
+    validation: Dataset({
+        features: ['premise', 'hypothesis', 'label'],
+        num_rows: 2490
+    })
+})
+
+```
+
+The dataset split remains same across all languages.
+
+## Dataset usage
+
+Code snippet for using the dataset using datasets library.
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("Divyanshu/indicxnli")
+```
+
+## Dataset Creation
+Machine translation of XNLI english dataset to 11 listed Indic Languages.
+
+### Curation Rationale
+
+[More information needed]
+
+### Source Data
+
+[XNLI dataset](https://cims.nyu.edu/~sbowman/xnli/)
+
+#### Initial Data Collection and Normalization
+
+[Detailed in the paper](https://arxiv.org/abs/2204.08776)
+
+#### Who are the source language producers?
+
+[Detailed in the paper](https://arxiv.org/abs/2204.08776)
+
+#### Human Verification Process
+
+[Detailed in the paper](https://arxiv.org/abs/2204.08776)
+
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More information needed]
+
+### Discussion of Biases
+
+[More information needed]
+
+### Other Known Limitations
+
+[More information needed]
+
+## Additional Information
+
+### Dataset Curators
+
+Divyanshu Aggarwal, Vivek Gupta, Anoop Kunchukuttan
+
+### Licensing Information
+
+Contents of this repository are restricted to only non-commercial research purposes under the [Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/). Copyright of the dataset contents belongs to the original copyright holders.
+
+### Citation Information
+
+If you use any of the datasets, models or code modules, please cite the following paper:
+
+```
+@misc{https://doi.org/10.48550/arxiv.2204.08776,
+  doi = {10.48550/ARXIV.2204.08776},
+  
+  url = {https://arxiv.org/abs/2204.08776},
+  
+  author = {Aggarwal, Divyanshu and Gupta, Vivek and Kunchukuttan, Anoop},
+  
+  keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
+  
+  title = {IndicXNLI: Evaluating Multilingual Inference for Indian Languages}, 
+  
+  publisher = {arXiv},
+  
+  year = {2022},
+  
+  copyright = {Creative Commons Attribution 4.0 International}
+}
+```
+
+### Contributions