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--- |
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license: apache-2.0 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: |
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- "ott-qa/train.json" |
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- "phrase-bank/train.json" |
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- "tat-qa/train.json" |
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- "template/train.json" |
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- "wiki-sql/train.json" |
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- split: test |
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path: |
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- "ott-qa/test.json" |
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- "phrase-bank/test.json" |
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- "tat-qa/test.json" |
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- "template/test.json" |
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- "wiki-sql/test.json" |
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- split: val |
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path: |
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- "ott-qa/dev.json" |
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- "phrase-bank/dev.json" |
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- "tat-qa/dev.json" |
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- "template/dev.json" |
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- "wiki-sql/dev.json" |
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--- |
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## Raven Dataset |
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The dataset that we use to fine-tune Raven is composed from four distinct question-answering datasets. Two are specifically from the financial domain with the remaining being generic and incorporating questions over both tables and text. |
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### TAT-QA. |
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[Table-and-Text Question](https://paperswithcode.com/dataset/tat-qa) Answering consists of 16,552 questions generated by financial experts associated with 2,757 hybrid contexts drawn from real-world financial reports. |
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### Financial PhraseBank |
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[Financial PhraseBank](https://paperswithcode.com/sota/sentiment-analysis-on-financial-phrasebank) consists of 4,846 phrases derived from English financial news on listed companies in OMX Helsinki. The dataset contains phrase-level annotation by financial markets experts, that categorises each sample sentence exclusively from an investor's standpoint as either positive, negative, or neutral. |
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### Wiki-SQL |
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[Wiki-SQL](https://paperswithcode.com/dataset/wikisql) consists of 80,654 manually annotated crowd sourced examples of natural language questions and corresponding SQL queries over 24,241 tables found on Wikipedia |
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### OTT-QA |
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Similar to \textsc{TAT-QA}, [Open Table-and-Text Question Answering](https://paperswithcode.com/dataset/ott-qa) consists of 43,683 questions over tabular data and unstructured text across diverse domains. The majority of questions necessitate multi-hop inference involving both forms of data. |
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### Data preparation |
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The datasets described above have diverse formats and are not suited for fine-tuning Raven as-is. We employ a data conversion pipeline to convert these four datasets into a homogeneous dataset suitable to fine-tune our financial model. In general, we extract up to four key attributes from the original datasets. These are (1) instruction that describes the task to perform, for example, *Determine the sentiment of the following phrase*, or the question *What is the percentage change in revenue after the adoption of ASC 606?* (2) input that provides more context such as the phrase to classify or a passage, (3) data that accompanies the context, in tabular format, (4) derivation that produces the answer or expected response. Refer to [templates](https://github.com/adriantheuma/llama2-raven/blob/main/templates/README.md) for examples of the full prompt. |
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To obtain a balanced dataset we randomly sub-sample larger datasets such that we obtain a uniformly distributed dataset among the different sources. The size of the final training dataset is 47.6K samples, validation 5.26K and test 5.81K. |
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