Datasets:
krkv
/
energy-prompt2query

Dataset card Viewer Files Community
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> what about if they had 1929.025 less past electricity [/INST]
previousfilter and change past_electricity decrease 1929.025 and previousoperation [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> show me people that have indoor temperature of 15.25 [/INST]
filter indoor_temperature equal to 15.25 and show [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> if people with past electricity colder than 1929.025 were to have past electricity increased by 640.4, what would the top 5 most important features be? [/INST]
filter past_electricity less than 1929.025 and change past_electricity increase 640.4 and important topk 5 [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> outdoor temperature is not greater than 21.5 sample mistakes [/INST]
filter outdoor_temperature less equal than 21.5 and mistake sample [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> what does the model predict for instances with outdoor temperature greater than 21.82399577? [/INST]
filter outdoor_temperature greater than 21.82399577 and predict [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> what's the average past electricity for people where indoor temperature more than 13.25? [/INST]
filter indoor_temperature greater than 13.25 and statistic past_electricity [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> what are some mistakes on outdoor temperature of 21.5 the model typically makes [/INST]
filter outdoor_temperature equal to 21.5 and mistake typical [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> please summarize any typical mistakes you make on this data [/INST]
previousfilter and mistake typical [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> for past electricity less than 1929.025, what is the distribution of outdoor temperature? [/INST]
filter past_electricity less than 1929.025 and statistic outdoor_temperature [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> what's the gold label for past electricity exceeds 1929.025 [/INST]
filter past_electricity greater than 1929.025 and label [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> what aspects of the data do you consider when reasoning about indoor temperature above 15.25 to make decisions? [/INST]
filter indoor_temperature greater than 15.25 and explain features [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> outdoor temperature less than 21.82399577 sample mistakes [/INST]
filter outdoor_temperature less than 21.82399577 and mistake sample [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> for past electricity is not less than 1929.025, what are the ground truth labels [/INST]
filter past_electricity greater equal than 1929.025 and label [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> i was wondering what are the most important features for instance 213? [/INST]
filter id 213 and important all [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> what is the models predictions on outdoor temperature greater than 12 and what would it be if you decreased indoor temperature by 21.82399577? [/INST]
filter outdoor_temperature greater than 12 and predict and change indoor_temperature decrease 21.82399577 and predict [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> can you provide me with reasoning for the model's predictions on past electricity not equal to 1929.025? [/INST]
filter past_electricity not equal to 1929.025 and explain features [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> if people with indoor temperature warmer than 15.25 and past electricity colder than 1929.025 were to have indoor temperature increased by 15.25, what would the top 5 most important features be? [/INST]
filter indoor_temperature greater than 15.25 and filter past_electricity less than 1929.025 and change indoor_temperature increase 15.25 and important topk 5 [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> what are the data labels for past electricity above 640.4 [/INST]
filter past_electricity greater than 640.4 and label [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> how many instances have past electricity is equal to but not greater than 640.4 [/INST]
filter past_electricity less equal than 640.4 and countdata [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> the 5 most important features in the data are impacted in what way by increasing feature outdoor temperature by 10. [/INST]
change outdoor_temperature increase 10 and important topk 5 [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> for past electricity is equal to but not greater than 640.4, how many instances in the data are there? [/INST]
filter past_electricity less equal than 640.4 and countdata [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> what are the most important features in the data when reasoning about outdoor temperature warmer than 21.82399577 and past electricity colder than 1929.025 to make decisions? [/INST]
filter outdoor_temperature greater than 21.82399577 and filter past_electricity less than 1929.025 and important all [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> what aspects of the data do you consider when reasoning about outdoor temperature warmer than 21.82399577 and past electricity colder than 1929.025 to make decisions? [/INST]
filter outdoor_temperature greater than 21.82399577 and filter past_electricity less than 1929.025 and explain features [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> for outdoor temperature warmer than 21.82399577 and past electricity colder than 1929.025, could you show me if the model predicts the data correctly for some examples? [/INST]
filter outdoor_temperature greater than 21.82399577 and filter past_electricity less than 1929.025 and mistake sample [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> please describe the number of past electricity exceeds 1929.025 in the data [/INST]
filter past_electricity greater than 1929.025 and coundata [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> how many instances have everything but not indoor temperature of 13.25 [/INST]
filter indoor_temperature not equal to 13.25 and countdata [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> could you tell me why the model predicts those instances with outdoor temperature above 21.5 and that have a indoor temperature of 13.25 and also what these instances are? [/INST]
filter outdoor_temperature greater than 21.5 and filter indoor_temperature equal to 13.25 and explain features and show [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> if indoor temperature is decreased by 15.25 for {filer_text}, what would the model predict? [/INST]
filter indoor_temperature less equal than 15.25 and change indoor_temperature increase 15.25 and predict [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> what's the testing accuracy for people indoor temperature above 13.25 [/INST]
filter indoor_temperature greater than 13.25 and score default [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> could you show me a few examples you get incorrect for outdoor temperature more than 21.5 [/INST]
filter outdoor_temperature greater than 21.5 and mistake sample [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> what are the ground truth labels in the data for past electricity is not greater than 640.4 [/INST]
filter past_electricity less equal than 640.4 and label [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> what about for people with past electricity over 640.4 [/INST]
filter past_electricity greater than 640.4 and previousoperation [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> show me instances that are indoor temperature more than 13.25 [/INST]
filter indoor_temperature greater than 13.25 and show [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> how would you characterize the mistakes you make on past electricity warmer than 640.4 and outdoor temperature colder than 21.5? [/INST]
filter past_electricity greater than 640.4 and filter outdoor_temperature less than 21.5 and mistake typical [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> what aspects of the data do you consider when reasoning about past electricity warmer than 1929.025 to make decisions? [/INST]
filter past_electricity greater than 1929.025 and explain features [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> i want to better understand your reasoning on data with outdoor temperature less than 21.82399577 [/INST]
filter outdoor_temperature less than 21.82399577 and explain features [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> for outdoor temperature warmer than 21.5 and indoor temperature colder than 15.25, what are the ground truth labels [/INST]
filter outdoor_temperature greater than 21.5 and filter indoor_temperature less than 15.25 and label [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> what's the accuracy for people outdoor temperature exceeds 21.82399577? [/INST]
filter outdoor_temperature greater than 21.82399577 and score default [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> does the model predict indoor temperature less than 13.25 correctly? [/INST]
filter indoor_temperature less than 13.25 and mistake sample [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> for indoor temperature above 15.25, what is the distribution of the target variable? [/INST]
filter indoor_temperature greater than 15.25 and statistic target [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> i want to better understand your reasoning on data with past electricity above 1929.025 [/INST]
filter past_electricity greater than 1929.025 and explain features [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> indoor temperature not equal to 13.25 sample mistakes [/INST]
filter indoor_temperature not equal to 13.25 and mistake sample [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> for those where past electricity not equal to 640.4 is the case, could you print out the data items [/INST]
filter past_electricity not equal to 640.4 and show [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> what's the average indoor temperature for people where past electricity is not greater than 640.4? [/INST]
filter past_electricity less equal than 640.4 and statistic indoor_temperature [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> for past electricity of 640.4, could you show me the data? [/INST]
filter past_electricity equal to 640.4 and show [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> if outdoor temperature is increased by 21.5 for {filer_text}, what would the model predict? [/INST]
filter past_electricity greater than 1929.025 and filter indoor_temperature less than 15.25 and change outdoor_temperature increase 21.5 and predict [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> for outdoor temperature warmer than 21.5 and past electricity colder than 640.4, how many instances in the data are there? [/INST]
filter outdoor_temperature greater than 21.5 and filter past_electricity less than 640.4 and countdata [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> what's the accuracy for people past electricity colder than 1929.025? [/INST]
filter past_electricity less than 1929.025 and score default [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> what aspects of the data do you consider when reasoning about past electricity warmer than 1929.025 and outdoor temperature colder than 21.5 to make decisions [/INST]
filter past_electricity greater than 1929.025 and filter outdoor_temperature less than 21.5 and explain features [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> what are the data labels for past electricity exceeds 640.4 [/INST]
filter past_electricity greater than 640.4 and label [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> what's the gold label for outdoor temperature colder than 21.82399577 [/INST]
filter outdoor_temperature less than 21.82399577 and label [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> what would happen to the distribution of model predictions if people with outdoor temperature less than 21.5 had past electricity decreased by 1929.025 [/INST]
filter outdoor_temperature less than 21.5 and change past_electricity decrease 1929.025 and predict [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> with the addition of outdoor temperature equal to 21.82399577, why does the model do these predictions? [/INST]
previousfilter or filter outdoor_temperature equal to 21.82399577 and explain features [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> what is the models predictions on past electricity greater than 12 and what would it be if you decreased indoor temperature by 1929.025? [/INST]
filter past_electricity greater than 12 and predict and change indoor_temperature decrease 1929.025 and predict [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> what's the number of data points where indoor temperature is less that 13.25 [/INST]
filter indoor_temperature less than 13.25 and countdata [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> what's the statistics of past electricity for data where outdoor temperature above 21.82399577? [/INST]
filter outdoor_temperature greater than 21.82399577 and statistic past_electricity [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> for data where everything but not past electricity of 640.4, what is the statistics of indoor temperature? [/INST]
filter past_electricity not equal to 640.4 and statistic indoor_temperature [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> tell me a bit more about what i can do here [/INST]
function [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> what's the number of outdoor temperature warmer than 21.5 and indoor temperature colder than 13.25 data points [/INST]
filter outdoor_temperature greater than 21.5 and filter indoor_temperature less than 13.25 and countdata [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> if people with id 61 were to have outdoor temperature increased by 21.82399577, what would the top 5 most important features be? [/INST]
filter id 61 and change outdoor_temperature increase 21.82399577 and important topk 5 [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> for indoor temperature is equal to but not greater than 15.25, what is the distribution of past electricity? [/INST]
filter indoor_temperature less equal than 15.25 and statistic past_electricity [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> what's the most important feature for the model predicting outdoor temperature less than 21.82399577? [/INST]
filter outdoor_temperature less than 21.82399577 and important topk 1 [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> what are some common mistakes you make when predicting indoor temperature less than 13.25? [/INST]
filter indoor_temperature less than 13.25 and mistake typical [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> outdoor temperature colder than 21.82399577 sample mistakes [/INST]
filter outdoor_temperature less than 21.82399577 and mistake sample [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> what's the testing accuracy for people outdoor temperature warmer than 21.5 and indoor temperature colder than 13.25 [/INST]
filter outdoor_temperature greater than 21.5 and filter indoor_temperature less than 13.25 and score default [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> if indoor temperature is increased by 13.25 for {filer_text}, what would the model predict? [/INST]
filter past_electricity greater than 1929.025 and filter outdoor_temperature less than 21.82399577 and change indoor_temperature increase 13.25 and predict [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> for data where indoor temperature of 15.25, what is the statistics of outdoor temperature? [/INST]
filter indoor_temperature equal to 15.25 and statistic outdoor_temperature [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> what's the testing accuracy for people outdoor temperature warmer than 21.5 and past electricity colder than 640.4 [/INST]
filter outdoor_temperature greater than 21.5 and filter past_electricity less than 640.4 and score default [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> tell me how many ids in the data have outdoor temperature warmer than 21.5 and past electricity colder than 640.4 [/INST]
filter outdoor_temperature greater than 21.5 and filter past_electricity less than 640.4 and countdata [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> if people with past electricity warmer than 640.4 and indoor temperature colder than 13.25 were to have indoor temperature increased by 15.25, what would the top 5 most important features be? [/INST]
filter past_electricity greater than 640.4 and filter indoor_temperature less than 13.25 and change indoor_temperature increase 15.25 and important topk 5 [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> what does the model predict for past electricity more than 640.4? [/INST]
filter past_electricity greater than 640.4 and predict [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> what are some common mistakes you make when predicting outdoor temperature is not less than 21.82399577? [/INST]
filter outdoor_temperature greater equal than 21.82399577 and mistake typical [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> what are the ground truth labels in the data for past electricity above 1929.025 [/INST]
filter past_electricity greater than 1929.025 and label [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> what's the accuracy performance of the model on the training data for individuals with indoor temperature warmer than 13.25 and past electricity colder than 640.4 [/INST]
filter indoor_temperature greater than 13.25 and filter past_electricity less than 640.4 and score default [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> please describe the number of outdoor temperature is not greater than 21.82399577 in the data [/INST]
filter outdoor_temperature less equal than 21.82399577 and coundata [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> indoor temperature warmer than 15.25 and outdoor temperature colder than 21.82399577 show mistakes [/INST]
filter indoor_temperature greater than 15.25 and filter outdoor_temperature less than 21.82399577 and mistake sample [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> outdoor temperature is not greater than 21.5 show bad predictions [/INST]
filter outdoor_temperature less equal than 21.5 and mistake sample [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> what's the most important feature for the model predicting past electricity the same or more than 1929.025? [/INST]
filter past_electricity greater equal than 1929.025 and important topk 1 [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> what are the 3 most important features in the data when reasoning about everything but not outdoor temperature of 21.5 to make decisions? [/INST]
filter outdoor_temperature not equal to 21.5 and important topk 3 [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> just on instances where past electricity warmer than 1929.025, show me the labels? [/INST]
filter past_electricity greater than 1929.025 and label [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> what aspects of the data do you consider when reasoning about id 61 to make decisions? [/INST]
filter id 61 and explain features [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> how many instances are past electricity equal to or below 1929.025 [/INST]
filter past_electricity less equal than 1929.025 and countdata [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> for data where past electricity exceeds 1929.025, what is the statistics of outdoor temperature? [/INST]
filter past_electricity greater than 1929.025 and statistic outdoor_temperature [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> display data where past electricity is not equal to 1929.025 [/INST]
filter past_electricity not equal to 1929.025 and show [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> where everything but not indoor temperature of 15.25, show the distribution of the ground truth values? [/INST]
filter indoor_temperature not equal to 15.25 and statistic target [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> what's the statistics of past electricity for data where past electricity colder than 640.4? [/INST]
filter past_electricity less than 640.4 and statistic past_electricity [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> could you show me a few examples you get incorrect for indoor temperature warmer than 13.25 and outdoor temperature colder than 21.5 [/INST]
filter indoor_temperature greater than 13.25 and filter outdoor_temperature less than 21.5 and mistake sample [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> what's the testing accuracy for people past electricity warmer than 640.4 and outdoor temperature colder than 21.5 [/INST]
filter past_electricity greater than 640.4 and filter outdoor_temperature less than 21.5 and score default [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> what are the most important features in the data when reasoning about indoor temperature of 15.25 to make decisions? [/INST]
filter indoor_temperature equal to 15.25 and important all [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> what are the labels for everything but not indoor temperature of 13.25 [/INST]
filter indoor_temperature not equal to 13.25 and label [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> what are the labels for outdoor temperature above 21.5 [/INST]
filter outdoor_temperature greater than 21.5 and label [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> if past electricity is decreased by 640.4 for {filer_text}, what would the model predict? [/INST]
filter indoor_temperature equal to 15.25 and change past_electricity increase 640.4 and predict [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> how many instances have outdoor temperature warmer than 21.5 and indoor temperature colder than 13.25 [/INST]
filter outdoor_temperature greater than 21.5 and filter indoor_temperature less than 13.25 and countdata [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> please show the items of data points 89 and 90 in the data [/INST]
filter id 89 and filter id 90 and show [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> for data where past electricity the same or more than 640.4, what is the average values of indoor temperature? [/INST]
filter past_electricity greater equal than 640.4 and statistic indoor_temperature [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> what's your motivation for deciding the predictions of past electricity is equal to but not greater than 1929.025 [/INST]
filter past_electricity less equal than 1929.025 and explain features [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> if outdoor temperature is decreased by 21.82399577 for {filer_text}, what would the model predict? [/INST]
filter indoor_temperature not equal to 13.25 and change outdoor_temperature increase 21.82399577 and predict [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> for past electricity is not less than 1929.025, how many instances in the data are there? [/INST]
filter past_electricity greater equal than 1929.025 and countdata [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> if indoor temperature is increased by 15.25 for {filer_text}, what would the model predict? [/INST]
filter indoor_temperature greater than 13.25 and filter outdoor_temperature less than 21.82399577 and change indoor_temperature increase 15.25 and predict [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> i want to better understand your reasoning on data with past electricity warmer than 1929.025 and outdoor temperature colder than 21.82399577 [/INST]
filter past_electricity greater than 1929.025 and filter outdoor_temperature less than 21.82399577 and explain features [e]
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