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.gitignore

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-.history/
-
-# Byte-compiled / optimized / DLL files
-__pycache__/
-*.py[cod]
-*$py.class
-
-# C extensions
-*.so
-
-# Distribution / packaging
-.Python
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-wheels/
-share/python-wheels/
-*.egg-info/
-.installed.cfg
-*.egg
-MANIFEST
-
-# PyInstaller
-#  Usually these files are written by a python script from a template
-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
-# Installer logs
-pip-log.txt
-pip-delete-this-directory.txt
-
-# Unit test / coverage reports
-htmlcov/
-.tox/
-.nox/
-.coverage
-.coverage.*
-.cache
-nosetests.xml
-coverage.xml
-*.cover
-*.py,cover
-.hypothesis/
-.pytest_cache/
-cover/
-
-# Translations
-*.mo
-*.pot
-
-# Django stuff:
-*.log
-local_settings.py
-db.sqlite3
-db.sqlite3-journal
-
-# Flask stuff:
-instance/
-.webassets-cache
-
-# Scrapy stuff:
-.scrapy
-
-# Sphinx documentation
-docs/_build/
-
-# PyBuilder
-.pybuilder/
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-# Jupyter Notebook
-.ipynb_checkpoints
-
-# IPython
-profile_default/
-ipython_config.py
-
-# pyenv
-#   For a library or package, you might want to ignore these files since the code is
-#   intended to run in multiple environments; otherwise, check them in:
-# .python-version
-
-# pipenv
-#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
-#   However, in case of collaboration, if having platform-specific dependencies or dependencies
-#   having no cross-platform support, pipenv may install dependencies that don't work, or not
-#   install all needed dependencies.
-#Pipfile.lock
-
-# poetry
-#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
-#   This is especially recommended for binary packages to ensure reproducibility, and is more
-#   commonly ignored for libraries.
-#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
-#poetry.lock
-
-# pdm
-#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
-#pdm.lock
-#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
-#   in version control.
-#   https://pdm.fming.dev/#use-with-ide
-.pdm.toml
-
-# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
-__pypackages__/
-
-# Celery stuff
-celerybeat-schedule
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-
-# SageMath parsed files
-*.sage.py
-
-# Environments
-.env
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-.spyderproject
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-.mypy_cache/
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-.pyre/
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-.pytype/
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-# Cython debug symbols
-cython_debug/
-
-# PyCharm
-#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
-#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
-#  and can be added to the global gitignore or merged into this file.  For a more nuclear
-#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-#.idea/
-Foo

README.md

@@ -1,220 +0,0 @@
----
-annotations_creators:
-- machine-generated
-language_creators:
-- machine-generated
-language:
-- as
-- bn
-- gu
-- hi
-- kn
-- ml
-- mr
-- or
-- pa
-- ta
-- te
-license:
-- cc0-1.0
-multilinguality:
-- multilingual
-pretty_name: IndicXNLI
-size_categories:
-- 1M<n<10M
-source_datasets:
-- original
-task_categories:
-- text-classification
-task_ids:
-- natural-language-inference
----
-
-# Dataset Card for "IndicXNLI"
-
-## Table of Contents
-
-- [Dataset Card for "IndicXNLI"](#dataset-card-for-indicxnli)
-  - [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 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
-    })
-})
-
-``` -->
-
-Language      | ISO 639-1 Code |Train | Test | Dev |
---------------|----------------|-------|-----|------|
-Assamese | as | 392,702 | 5,010 | 2,490 |
-Bengali | bn | 392,702 | 5,010 | 2,490 |
-Gujarati | gu |  392,702 | 5,010 | 2,490 |
-Hindi | hi | 392,702 | 5,010 | 2,490 |
-Kannada | kn |  392,702 | 5,010 | 2,490 |
-Malayalam | ml |392,702  | 5,010 | 2,490 |
-Marathi | mr |392,702 | 5,010 | 2,490 |
-Oriya | or | 392,702 | 5,010 | 2,490 |
-Punjabi | pa | 392,702 | 5,010 | 2,490 |
-Tamil | ta | 392,702 | 5,010 | 2,490 |
-Telugu | te | 392,702 | 5,010 | 2,490 |
-
-<!-- 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
-
-[Detailed in the paper](https://arxiv.org/abs/2204.08776)
-
-### Discussion of Biases
-
-[Detailed in the paper](https://arxiv.org/abs/2204.08776)
-
-### Other Known Limitations
-
-[Detailed in the paper](https://arxiv.org/abs/2204.08776)
-
-### 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 -->

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