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--- |
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license: mit |
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task_categories: |
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- text-generation |
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language: |
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- en |
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pretty_name: English-translated Akkadian Corpus |
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size_categories: |
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- 1K<n<10K |
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--- |
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# Akkadian English Corpus |
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This dataset is a cleaned English-translated Akkadian language dataset. This dataset can and has been used for text generation tasks, for example to fine-tune LLMs. |
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## How it was generated |
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Please visit my [repo](https://github.com/veezbo/akkadian_english_corpus) on Github which explains the steps that were taken to prepare this dataset for a text generation task. |
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At a high level, these are steps that were taken: |
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- Sourced a high-quality dataset of English-translated Akkadian by experts |
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- Enforced a minimum line length |
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- Removed duplicate lines |
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- Removed textual notes and other generic notes within parantheses |
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- Inserted translation notes and literal notes in place (preserving grammar and adding clarity to the corpus) |
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## Credit |
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Credit for the aggregation of the raw data belongs to the [Akkademia](https://github.com/gaigutherz/Akkademia/tree/master) project. Specifically, the exact data file used as the starting dataset is linked [here](https://github.com/gaigutherz/Akkademia/blob/master/NMT_input/train.en) and was also used to train their SOTA neural machine translation Akkadian->English model as described in their recent [paper](https://academic.oup.com/pnasnexus/article/2/5/pgad096/7147349) Gutherz et al. 2023 [1]. |
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Credit for the original source of the raw data belongs to the incredible Open Richly Annotated Cuneiform Corpus ([ORACC](http://oracc.org)) project [2]. Specifically, as noted by the Akkademia project above, the RINAP 1, 3, 4, and 5 datasets are the source of the original raw data. |
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## Citations |
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[1] Gai Gutherz, Shai Gordin, Luis Sáenz, Omer Levy, Jonathan Berant, Translating Akkadian to English with neural machine translation, PNAS Nexus, Volume 2, Issue 5, May 2023, pgad096, https://doi.org/10.1093/pnasnexus/pgad096 |
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[2] Jamie Novotny, Eleanor Robson, Steve Tinney, Niek Veldhuis, et al. Open Richly Annotated Cuneiform Corpus, http://oracc.org |
