Upload README.md

README.md

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+---
+language:
+- as
+- bn
+- brx
+- doi
+- en
+- gom
+- gu
+- hi
+- kn
+- ks
+- mai
+- ml
+- mr
+- mni
+- ne
+- or
+- pa
+- sa
+- sat
+- sd
+- ta
+- te
+- ur
+language_details: >-
+  asm_Beng, ben_Beng, brx_Deva, doi_Deva, eng_Latn, gom_Deva, guj_Gujr,
+  hin_Deva, kan_Knda, kas_Arab, mai_Deva, mal_Mlym, mar_Deva, mni_Mtei, 
+  npi_Deva, ory_Orya, pan_Guru, san_Deva, sat_Olck, snd_Deva, tam_Taml, 
+  tel_Telu, urd_Arab
+license: cc-by-4.0
+language_creators:
+- expert-generated
+multilinguality:
+- multilingual
+- translation
+pretty_name: in22-gen
+size_categories:
+- 1K<n<10K
+task_categories:
+- translation
+---
+
+# IN22-Gen
+
+IN22 is a newly created comprehensive benchmark for evaluating machine translation performance in multi-domain, n-way parallel contexts across 22 Indic languages. IN22-Gen is a general-purpose multi-domain evaluation subset of IN22. It has been created from two sources: Wikipedia and Web Sources offering diverse content spanning news, entertainment, culture, legal, and India-centric topics. The evaluation subset consists of 1024 sentences translated across 22 Indic languages enabling evaluation of MT systems across 506 directions.
+
+Here is the domain and source distribution of our IN22-Gen evaluation subset.
+
+<table style="width: 40%">
+    <tr>
+        <td>domain</td>
+        <td>web sources</td>
+        <td>wikipedia</td>
+    </tr>
+    <tr>
+        <td>culture</td>
+        <td>40</td>
+        <td>40</td>
+    </tr>
+    <tr>
+        <td>economy</td>
+        <td>40</td>
+        <td>40</td>
+    </tr>
+    <tr>
+        <td>education</td>
+        <td>40</td>
+        <td>40</td>
+    </tr>
+    <tr>
+        <td>entertainment</td>
+        <td>40</td>
+        <td>40</td>
+    </tr>
+    <tr>
+        <td>geography</td>
+        <td>40</td>
+        <td>40</td>
+    </tr>
+    <tr>
+        <td>governments</td>
+        <td>40</td>
+        <td>40</td>
+    </tr>
+    <tr>
+        <td>health</td>
+        <td>40</td>
+        <td>40</td>
+    </tr>
+    <tr>
+        <td>industry</td>
+        <td>40</td>
+        <td>40</td>
+    </tr>
+    <tr>
+        <td>legal</td>
+        <td>40</td>
+        <td>40</td>
+    </tr>
+    <tr>
+        <td>news</td>
+        <td>32</td>
+        <td>32</td>
+    </tr>
+    <tr>
+        <td>religion</td>
+        <td>40</td>
+        <td>40</td>
+    </tr>
+    <tr>
+        <td>sports</td>
+        <td>40</td>
+        <td>40</td>
+    </tr>
+    <tr>
+        <td>tourism</td>
+        <td>40</td>
+        <td>40</td>
+    </tr>
+    <tr>
+        <td>total</td>
+        <td>512</td>
+        <td>512</td>
+    </tr>
+</table>
+
+Please refer to the `Appendix E: Dataset Card` of the [preprint](https://arxiv.org/abs/2305.16307) on detailed description of dataset curation, annotation and quality control process.
+
+
+### Dataset Structure
+
+#### Dataset Fields
+
+- `id`: Row number for the data entry, starting at 1.
+- `context`: Context window of 3 sentences, typically includes one sentence before and after the candidate sentence.
+- `source`: The source from which the candidate sentence is considered.
+- `url`: The URL for the English article from which the sentence was extracted. Only available for candidate sentences sourced from Wikipedia
+- `domain`: The domain of the sentence.
+- `num_words`: The number of words in the candidate sentence.
+- `bucket`: Classification of the candidate sentence as per predefined bucket categories.
+- `sentence`: The full sentence in the specific language (may have _lang for pairings)
+
+#### Data Instances
+
+A sample from the `gen` split for the English language (`eng_Latn` config) is provided below. All configurations have the same structure, and all sentences are aligned across configurations and splits.
+
+```python
+{
+   "id": 1,
+   "context": "A uniform is often viewed as projecting a positive image of an organisation. Maintaining personal hygiene is also an important aspect of personal appearance and dressing. An appearance is a bunch of attributes related with the service person, like their shoes, clothes, tie, jewellery, hairstyle, make-up, watch, cosmetics, perfume, etc.",
+   "source": "web",
+   "url": "",
+   "domain": "culture",
+   "num_words": 24,
+   "bucket": "18 - 25",
+   "sentence": "An appearance is a bunch of attributes related to the service person, like their shoes, clothes, tie, jewellery, hairstyle, make-up, watch, cosmetics, perfume, etc."
+}
+```
+
+When using a hyphenated pairing or using the `all` function, data will be presented as follows:
+
+```python
+{
+   "id": 1,
+   "context": "A uniform is often viewed as projecting a positive image of an organisation. Maintaining personal hygiene is also an important aspect of personal appearance and dressing. An appearance is a bunch of attributes related with the service person, like their shoes, clothes, tie, jewellery, hairstyle, make-up, watch, cosmetics, perfume, etc.",
+   "source": "web",
+   "url": "",
+   "domain": "culture",
+   "num_words": 24,
+   "bucket": "18 - 25",
+   "sentence_eng_Latn": "An appearance is a bunch of attributes related to the service person, like their shoes, clothes, tie, jewellery, hairstyle, make-up, watch, cosmetics, perfume, etc.",
+   "sentence_hin_Deva": "सेवा संबंधी लोगों के लिए भेष कई गुणों का संयोजन है, जैसे कि उनके जूते, कपड़े, टाई, आभूषण, केश शैली, मेक-अप, घड़ी, कॉस्मेटिक, इत्र, आदि।"
+}
+```
+
+
+### Usage Instructions
+
+```python
+from datasets import load_dataset
+
+# download and load all the pairs
+dataset = load_dataset("ai4bharat/IN22-Gen", "all")
+
+# download and load specific pairs
+dataset = load_dataset("ai4bharat/IN22-Gen", "eng_Latn-hin_Deva")
+```
+
+### Languages Covered
+
+<table style="width: 40%">
+    <tr>
+        <td>Assamese (asm_Beng)</td>
+        <td>Kashmiri (Arabic) (kas_Arab)</td>
+        <td>Punjabi (pan_Guru)</td>
+    </tr>
+    <tr>
+        <td>Bengali (ben_Beng)</td>
+        <td>Kashmiri (Devanagari) (kas_Deva)</td>
+        <td>Sanskrit (san_Deva)</td>
+    </tr>
+    <tr>
+        <td>Bodo (brx_Deva)</td>
+        <td>Maithili (mai_Deva)</td>
+        <td>Santali (sat_Olck)</td>
+    </tr>
+    <tr>
+        <td>Dogri (doi_Deva)</td>
+        <td>Malayalam (mal_Mlym)</td>
+        <td>Sindhi (Arabic) (snd_Arab)</td>
+    </tr>
+    <tr>
+        <td>English (eng_Latn)</td>
+        <td>Marathi (mar_Deva)</td>
+        <td>Sindhi (Devanagari) (snd_Deva)</td>
+    </tr>
+    <tr>
+        <td>Konkani (gom_Deva)</td>
+        <td>Manipuri (Bengali) (mni_Beng)</td>
+        <td>Tamil (tam_Taml)</td>
+    </tr>
+    <tr>
+        <td>Gujarati (guj_Gujr)</td>
+        <td>Manipuri (Meitei) (mni_Mtei)</td>
+        <td>Telugu (tel_Telu)</td>
+    </tr>
+    <tr>
+        <td>Hindi (hin_Deva)</td>
+        <td>Nepali (npi_Deva)</td>
+        <td>Urdu (urd_Arab)</td>
+    </tr>
+    <tr>
+        <td>Kannada (kan_Knda)</td>
+        <td>Odia (ory_Orya)</td>
+    </tr>
+</table>
+
+
+### Citation
+
+If you consider using our work then please cite using:
+
+```
+@article{gala2023indictrans,
+title={IndicTrans2: Towards High-Quality and Accessible Machine Translation Models for all 22 Scheduled Indian Languages},
+author={Jay Gala and Pranjal A Chitale and A K Raghavan and Varun Gumma and Sumanth Doddapaneni and Aswanth Kumar M and Janki Atul Nawale and Anupama Sujatha and Ratish Puduppully and Vivek Raghavan and Pratyush Kumar and Mitesh M Khapra and Raj Dabre and Anoop Kunchukuttan},
+journal={Transactions on Machine Learning Research},
+issn={2835-8856},
+year={2023},
+url={https://openreview.net/forum?id=vfT4YuzAYA},
+note={}
+}
+```
+