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--- |
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language: |
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- as |
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- bn |
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- brx |
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- doi |
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- en |
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- gom |
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- gu |
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- hi |
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- kn |
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- ks |
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- mai |
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- ml |
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- mr |
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- mni |
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- ne |
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- or |
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- pa |
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- sa |
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- sat |
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- sd |
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- ta |
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- te |
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- ur |
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language_details: >- |
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asm_Beng, ben_Beng, brx_Deva, doi_Deva, eng_Latn, gom_Deva, guj_Gujr, |
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hin_Deva, kan_Knda, kas_Arab, mai_Deva, mal_Mlym, mar_Deva, mni_Mtei, |
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npi_Deva, ory_Orya, pan_Guru, san_Deva, sat_Olck, snd_Deva, tam_Taml, |
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tel_Telu, urd_Arab |
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license: cc-by-4.0 |
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language_creators: |
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- expert-generated |
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multilinguality: |
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- multilingual |
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- translation |
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pretty_name: in22-conv |
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size_categories: |
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- 1K<n<10K |
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task_categories: |
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- translation |
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--- |
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# IN22-Conv |
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IN-22 is a newly created comprehensive benchmark for evaluating machine translation performance in multi-domain, n-way parallel contexts across 22 Indic languages. IN22-Conv is the conversation domain subset of IN22. It is designed to assess translation quality in typical day-to-day conversational-style applications. The evaluation subset consists of 1503 sentences translated across 22 Indic languages enabling evaluation of MT systems across 506 directions. |
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Currently, we use it for sentence-level evaluation of MT systems but it can be repurposed for document translation evaluation as well. |
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Here is the domain distribution of our IN22-Conv evaluation subset. |
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<table style="width:25%"> |
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<tr> |
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<td>domain</td> |
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<td>count</td> |
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</tr> |
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<tr> |
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<td>hobbies</td> |
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<td>120</td> |
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</tr> |
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<tr> |
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<td>daily_dialogue</td> |
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<td>117</td> |
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</tr> |
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<tr> |
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<td>government</td> |
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<td>116</td> |
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</tr> |
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<tr> |
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<td>geography</td> |
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<td>114</td> |
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</tr> |
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<tr> |
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<td>sports</td> |
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<td>100</td> |
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</tr> |
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<tr> |
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<td>entertainment</td> |
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<td>97</td> |
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</tr> |
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<tr> |
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<td>history</td> |
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<td>97</td> |
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</tr> |
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<tr> |
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<td>legal</td> |
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<td>96</td> |
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</tr> |
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<tr> |
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<td>arts</td> |
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<td>95</td> |
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</tr> |
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<tr> |
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<td>college_life</td> |
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<td>94</td> |
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</tr> |
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<tr> |
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<td>tourism</td> |
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<td>91</td> |
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</tr> |
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<tr> |
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<td>school_life</td> |
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<td>87</td> |
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</tr> |
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<tr> |
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<td>insurance</td> |
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<td>82</td> |
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</tr> |
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<tr> |
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<td>culture</td> |
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<td>73</td> |
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</tr> |
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<tr> |
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<td>healthcare</td> |
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<td>67</td> |
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</tr> |
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<tr> |
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<td>banking</td> |
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<td>57</td> |
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</tr> |
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<tr> |
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<td>total</td> |
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<td>1503</td> |
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</tr> |
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</table> |
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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. |
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### Dataset Structure |
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#### Dataset Fields |
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- `id`: Row number for the data entry, starting at 1. |
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- `doc_id`: Unique identifier of the conversation. |
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- `sent_id`: Unique identifier of the sentence order in each conversation. |
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- `topic`: The specific topic of the conversation within the domain. |
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- `domain`: The domain of the conversation. |
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- `prompt`: The prompt provided to annotators to simulate the conversation. |
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- `scenario`: The scenario or context in which the conversation takes place. |
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- `speaker`: The speaker identifier in the conversation. |
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- `turn`: The turn within the conversation. |
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#### Data Instances |
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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. |
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```python |
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{ |
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"id": 1, |
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"doc_id": 0, |
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"sent_id": 1, |
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"topic": "Festivities", |
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"domain": "culture", |
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"prompt": "14th April a holiday", |
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"scenario": "Historical importance", |
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"speaker": 1, |
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"turn": 1, |
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"sentence": "Mom, let's go for a movie tomorrow." |
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} |
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``` |
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When using a hyphenated pairing or using the `all` function, data will be presented as follows: |
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```python |
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{ |
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"id": 1, |
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"doc_id": 0, |
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"sent_id": 1, |
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"topic": "Festivities", |
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"domain": "culture", |
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"prompt": "14th April a holiday", |
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"scenario": "Historical importance", |
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"speaker": 1, |
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"turn": 1, |
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"sentence_eng_Latn": "Mom, let's go for a movie tomorrow.", |
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"sentence_hin_Deva": "माँ, चलो कल एक फिल्म देखने चलते हैं।" |
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} |
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``` |
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#### Sample Conversation |
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<table> |
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<tr> |
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<td>Speaker</td> |
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<td>Turn</td> |
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</tr> |
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<tr> |
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<td>Speaker 1</td> |
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<td>Mom, let's go for a movie tomorrow. I don't have to go to school. It is a holiday.</td> |
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</tr> |
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<tr> |
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<td>Speaker 2</td> |
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<td>Oh, tomorrow is the 14th of April right? Your dad will also have the day off from work. We can make a movie plan!</td> |
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</tr> |
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<tr> |
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<td>Speaker 1</td> |
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<td>That's a good news! Why is it a holiday though? Are all schools, colleges and offices closed tomorrow?</td> |
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</tr> |
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<tr> |
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<td>Speaker 2</td> |
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<td>It is Ambedkar Jayanti tomorrow! This day is celebrated annually to mark the birth of Dr. B. R Ambedkar. Have you heard of him?</td> |
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</tr> |
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<tr> |
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<td>Speaker 1</td> |
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<td>I think I have seen him in my History and Civics book. Is he related to our Constitution?</td> |
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</tr> |
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<tr> |
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<td>Speaker 2</td> |
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<td>Absolutely! He is known as the father of the Indian Constitution. He was a civil rights activist who played a major role in formulating the Constitution. He played a crucial part in shaping the vibrant democratic structure that India prides itself upon.</td> |
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</tr> |
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<tr> |
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<td></td> |
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<td>...</td> |
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</tr> |
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</table> |
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### Usage Instructions |
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```python |
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from datasets import load_dataset |
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# download and load all the pairs |
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dataset = load_dataset("ai4bharat/IN22-Conv", "all") |
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# download and load specific pairs |
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dataset = load_dataset("ai4bharat/IN22-Conv", "eng_Latn-hin_Deva") |
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``` |
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### Languages Covered |
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<table style="width: 40%"> |
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<tr> |
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<td>Assamese (asm_Beng)</td> |
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<td>Kashmiri (Arabic) (kas_Arab)</td> |
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<td>Punjabi (pan_Guru)</td> |
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</tr> |
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<tr> |
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<td>Bengali (ben_Beng)</td> |
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<td>Kashmiri (Devanagari) (kas_Deva)</td> |
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<td>Sanskrit (san_Deva)</td> |
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</tr> |
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<tr> |
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<td>Bodo (brx_Deva)</td> |
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<td>Maithili (mai_Deva)</td> |
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<td>Santali (sat_Olck)</td> |
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</tr> |
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<tr> |
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<td>Dogri (doi_Deva)</td> |
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<td>Malayalam (mal_Mlym)</td> |
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<td>Sindhi (Arabic) (snd_Arab)</td> |
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</tr> |
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<tr> |
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<td>English (eng_Latn)</td> |
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<td>Marathi (mar_Deva)</td> |
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<td>Sindhi (Devanagari) (snd_Deva)</td> |
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</tr> |
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<tr> |
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<td>Konkani (gom_Deva)</td> |
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<td>Manipuri (Bengali) (mni_Beng)</td> |
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<td>Tamil (tam_Taml)</td> |
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</tr> |
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<tr> |
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<td>Gujarati (guj_Gujr)</td> |
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<td>Manipuri (Meitei) (mni_Mtei)</td> |
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<td>Telugu (tel_Telu)</td> |
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</tr> |
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<tr> |
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<td>Hindi (hin_Deva)</td> |
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<td>Nepali (npi_Deva)</td> |
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<td>Urdu (urd_Arab)</td> |
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</tr> |
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<tr> |
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<td>Kannada (kan_Knda)</td> |
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<td>Odia (ory_Orya)</td> |
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</tr> |
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</table> |
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### Citation |
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If you consider using our work then please cite using: |
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``` |
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@article{gala2023indictrans, |
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title={IndicTrans2: Towards High-Quality and Accessible Machine Translation Models for all 22 Scheduled Indian Languages}, |
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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}, |
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journal={Transactions on Machine Learning Research}, |
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issn={2835-8856}, |
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year={2023}, |
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url={https://openreview.net/forum?id=vfT4YuzAYA}, |
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note={} |
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} |
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``` |
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