Datasets:
krkv
/
energy-prompt2query

Dataset card Viewer Files Community
1
prompt
stringlengths
928
1.13k
query
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8
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input_ids
sequencelengths
260
390
attention_mask
sequencelengths
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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 indoor temperature is not less than 13.25 [/INST]
filter indoor_temperature greater equal than 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>> where outdoor temperature colder than 21.82399577, show the distribution of the ground truth values? [/INST]
filter outdoor_temperature less than 21.82399577 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 are the ground truth labels in the data for indoor temperature warmer than 15.25 and outdoor temperature colder than 21.5 [/INST]
filter indoor_temperature greater than 15.25 and filter outdoor_temperature less 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>> what's your motivation for deciding the predictions of outdoor temperature above 21.5 [/INST]
filter outdoor_temperature greater 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>> outdoor temperature not equal to 21.5 sample mistakes [/INST]
filter outdoor_temperature not equal to 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>> for outdoor temperature equal to or below 21.82399577, what is the distribution of the target variable? [/INST]
filter outdoor_temperature less equal than 21.82399577 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>> just on instances where outdoor temperature less than 21.5, show me the labels? [/INST]
filter outdoor_temperature less 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>> could you show me a few examples you get incorrect for past electricity of 640.4 [/INST]
filter past_electricity equal to 640.4 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 outdoor temperature colder than 21.82399577, how many instances in the data are there? [/INST]
filter outdoor_temperature less than 21.82399577 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 some typical mistakes on outdoor temperature colder than 21.5 [/INST]
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>> show me people that have past electricity exceeds 640.4 [/INST]
filter past_electricity greater than 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>> for outdoor temperature above 21.5, what is the distribution of past electricity? [/INST]
filter outdoor_temperature greater than 21.5 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 those with a past electricity of 640.4 would the predictions change if indoor temperature is decreased by 13.25? [/INST]
filter past_electricity equal to 640.4 and change indoor_temperature decrease 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 past electricity above 640.4, what is the distribution of outdoor temperature? [/INST]
filter past_electricity greater than 640.4 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>> could you show me a few examples you get incorrect for past electricity warmer than 640.4 and outdoor temperature colder than 21.82399577 [/INST]
filter past_electricity greater than 640.4 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>> if past electricity is increased by 1929.025 for {filer_text}, what would the model predict? [/INST]
filter indoor_temperature not equal to 13.25 and change past_electricity increase 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>> where indoor temperature equal to or below 13.25, show the distribution of the ground truth values? [/INST]
filter indoor_temperature less equal than 13.25 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>> for indoor temperature not equal to 15.25, what is the distribution of indoor temperature? [/INST]
filter indoor_temperature not equal to 15.25 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 the average outdoor temperature for people where indoor temperature colder than 13.25? [/INST]
filter indoor_temperature less than 13.25 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>> if past electricity is decreased by 640.4 for {filer_text}, what would the model predict? [/INST]
filter id 78 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>> for those with 640.4 or more past electricity, what would happen to the model's predictions if indoor temperature were decreased by 15.25? [/INST]
filter past_electricity greater equal than 640.4 and change indoor_temperature decrease 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>> past electricity is not less than 640.4 sample mistakes [/INST]
filter past_electricity greater equal than 640.4 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>> show me all the data where outdoor temperature more than 21.82399577 [/INST]
filter outdoor_temperature greater than 21.82399577 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 indoor temperature equal to or below 15.25, how well, in terms of accuracy, does the model do on the training and test data [/INST]
filter indoor_temperature less equal than 15.25 and score default 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>> for data where outdoor temperature the same or more than 21.5, what is the statistics of outdoor temperature? [/INST]
filter outdoor_temperature greater equal than 21.5 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>> how about indoor temperature warmer than 15.25 and past electricity colder than 640.4 [/INST]
filter indoor_temperature greater than 15.25 and filter past_electricity less 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>> what are some mistakes on outdoor temperature the same or more than 21.82399577 the model typically makes [/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>> if outdoor temperature is increased by 21.5 for {filer_text}, what would the model predict? [/INST]
filter outdoor_temperature greater than 21.5 and filter past_electricity less than 1929.025 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>> what are some common mistakes you make when predicting indoor temperature is not less than 13.25? [/INST]
filter indoor_temperature greater equal 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>> for past electricity is not less than 1929.025, what is the distribution of the target variable? [/INST]
filter past_electricity greater equal than 1929.025 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 are the ground truth labels in the data for outdoor temperature warmer than 21.5 and indoor temperature colder than 15.25 [/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 are the labels for outdoor temperature warmer than 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>> what aspects of the data do you consider when reasoning about past electricity is not greater than 1929.025 to make decisions? [/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>> what's the accuracy for people past electricity is not less than 1929.025? [/INST]
filter past_electricity greater equal 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>> for data where outdoor temperature warmer than 21.5 and past electricity colder than 1929.025, what is the average values of past electricity? [/INST]
filter outdoor_temperature greater than 21.5 and filter past_electricity less than 1929.025 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 outdoor temperature of 21.82399577, please help me understand your reasoning process [/INST]
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>> outdoor temperature is equal to but not greater than 21.5 return incorrect 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>> how would the predictions change on the data if indoor temperature is reduced by 15.25? [/INST]
predict and change indoor_temperature decreased 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>> for indoor temperature is equal to but not greater than 13.25, what is the distribution of the label? [/INST]
filter indoor_temperature less equal than 13.25 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>> for those with 21.5 or more outdoor temperature, what would happen to the distribution of model predictions if past electricity were increased by 1929.025? [/INST]
filter outdoor_temperature greater equal than 21.5 and change past_electricity increase 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>> for those where past electricity is not less than 1929.025 is the case, could you print out the data items [/INST]
filter past_electricity greater equal than 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>> if indoor temperature is increased by 13.25 for {filer_text}, what would the model predict? [/INST]
filter indoor_temperature greater than 15.25 and filter past_electricity less than 640.4 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>> what data points does the model typically predict mistakenly on people with outdoor temperature exceeds 21.82399577? [/INST]
filter outdoor_temperature greater 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>> could you let me know why you predicted 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>> for samples with 640.4 less than past electricity, if outdoor temperature were increased by 21.5, in which ways would the predictions change? [/INST]
filter past_electricity less than 640.4 and predict 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>> please tell me what the model predicts instances with 1929.025 past electricity or 15.25 indoor temperature or 21.82399577 outdoor temperature? [/INST]
filter past_electricity equal to 1929.025 or filter indoor_temperature equal to 15.25 or filter outdoor_temperature equal to 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 statistics of outdoor temperature for data where outdoor temperature is not greater than 21.5? [/INST]
filter outdoor_temperature less equal than 21.5 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>> for data where outdoor temperature equal to or below 21.5, what is the average values of indoor temperature? [/INST]
filter outdoor_temperature less equal than 21.5 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 the meaning of the outdoor temperature feature? [/INST]
define 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>> for past electricity warmer than 640.4 and outdoor temperature colder than 21.5, what is the distribution of outdoor temperature? [/INST]
filter past_electricity greater than 640.4 and filter outdoor_temperature less than 21.5 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 statistics of indoor temperature for data where everything but not outdoor temperature of 21.82399577? [/INST]
filter outdoor_temperature not equal to 21.82399577 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 datapoints with 13.25 indoor temperature or 1929.025 past electricity, please tell me what the model predicts? [/INST]
filter indoor_temperature equal to 13.25 or filter past_electricity equal to 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>> show me all the data where outdoor temperature upper than 21.82399577 [/INST]
filter outdoor_temperature greater than 21.82399577 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 important is past electricity for the predictions? [/INST]
important 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 statistics of past electricity for data where outdoor temperature warmer than 21.5 and past electricity colder than 1929.025? [/INST]
filter outdoor_temperature greater than 21.5 and filter past_electricity less than 1929.025 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>> how would you characterize the mistakes you make on everything but not indoor temperature of 15.25? [/INST]
filter indoor_temperature not equal to 15.25 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>> what are the most important features in the data when reasoning about indoor temperature less than 15.25 to make decisions? [/INST]
filter indoor_temperature less than 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 data points does the model typically predict mistakenly on people with past electricity above 640.4? [/INST]
filter past_electricity greater than 640.4 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>> can you provide me with reasoning for the model's predictions on outdoor temperature more than 21.82399577? [/INST]
filter outdoor_temperature greater 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>> take all the instances with more than 13.25 indoor temperature, reduce their past electricity by 640.4, and tell me the predictions [/INST]
filter indoor_temperature greater than 13.25 and change past_electricity decrease 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>> for datapoints with 13.25 indoor temperature or 21.5 outdoor temperature, please tell me what the model predicts? [/INST]
filter indoor_temperature equal to 13.25 or filter outdoor_temperature equal to 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>> please tell me what the model predicts instances with 640.4 past electricity or 21.5 outdoor temperature? [/INST]
filter past_electricity equal to 640.4 or filter outdoor_temperature equal to 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>> what are some common mistakes you make when predicting outdoor temperature warmer than 21.82399577 and indoor temperature colder than 13.25? [/INST]
filter outdoor_temperature greater than 21.82399577 and 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>> indoor temperature equal to or below 15.25 sample mistakes [/INST]
filter indoor_temperature less equal than 15.25 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 data where past electricity warmer than 1929.025 and outdoor temperature colder than 21.5, what is the statistics of indoor temperature? [/INST]
filter past_electricity greater than 1929.025 and filter outdoor_temperature less than 21.5 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 ids with a outdoor temperature over 21.82399577 is indoor temperature less important than past electricity [/INST]
filter outdoor_temperature greater than 21.82399577 and important indoor_temperature and important 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>> past electricity warmer than 1929.025 and outdoor temperature colder than 21.5 show bad predictions [/INST]
filter past_electricity greater than 1929.025 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>> show me all the data where outdoor temperature not equal to 21.5 [/INST]
filter outdoor_temperature not equal to 21.5 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 decreased by 21.82399577 for {filer_text}, what would the model predict? [/INST]
filter outdoor_temperature greater equal than 21.82399577 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>> if people with indoor temperature is equal to but not greater than 15.25 were to have indoor temperature increased by 13.25, what would the top 5 most important features be? [/INST]
filter indoor_temperature less equal than 15.25 and change indoor_temperature increase 13.25 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 warmer than 1929.025 and outdoor temperature colder than 21.5 [/INST]
filter past_electricity greater than 1929.025 and filter outdoor_temperature less 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>> for instances past electricity warmer than 1929.025 and outdoor temperature colder than 21.82399577, show statistics for the outdoor temperature feature [/INST]
filter past_electricity greater than 1929.025 and filter outdoor_temperature less than 21.82399577 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 accuracy performance of the model on the training data for individuals with indoor temperature is not greater than 13.25 [/INST]
filter indoor_temperature less equal 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>> for those where past electricity warmer than 1929.025 is the case, could you print out the data items [/INST]
filter past_electricity greater than 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>> what are some common mistakes you make when predicting indoor temperature more than 13.25? [/INST]
filter indoor_temperature greater 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>> for indoor temperature warmer than 15.25, please help me understand your reasoning process [/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>> where outdoor temperature above 21.82399577, show the distribution of the ground truth values? [/INST]
filter outdoor_temperature greater than 21.82399577 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>> for instances indoor temperature above 13.25, show statistics for the past electricity feature [/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>> i want to better understand your reasoning on data with past electricity is 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 people with outdoor temperature warmer than 21.82399577 were to have past electricity increased by 640.4, what would the top 5 most important features be? [/INST]
filter outdoor_temperature greater than 21.82399577 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>> for indoor temperature is equal to but not greater than 13.25, how many instances in the data are there? [/INST]
filter indoor_temperature less equal 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>> just on instances where outdoor temperature above 21.5, show me the labels? [/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>> can you provide me with reasoning for the model's predictions on indoor temperature not equal to 13.25? [/INST]
filter indoor_temperature not equal to 13.25 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>> for outdoor temperature is not greater than 21.5, how well, in terms of accuracy, does the model do on the training and test data [/INST]
filter outdoor_temperature less equal than 21.5 and score default 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's the statistics of past electricity for data where past electricity not equal to 1929.025? [/INST]
filter past_electricity not equal to 1929.025 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>> outdoor temperature exceeds 21.5 show bad predictions [/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>> for outdoor temperature less than 21.5, what is the distribution of the target variable? [/INST]
filter outdoor_temperature less than 21.5 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 happens to the predictions for data with indoor temperature of 13.25 where outdoor temperature is decreased by 21.5? [/INST]
filter indoor_temperature equal to 13.25 and change outdoor_temperature decrease 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 indoor temperature warmer than 15.25 and past electricity colder than 1929.025, what are some mistakes you typically make? [/INST]
filter indoor_temperature greater than 15.25 and filter past_electricity less than 1929.025 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 describe the number of past electricity warmer than 1929.025 and outdoor temperature colder than 21.5 in the data [/INST]
filter past_electricity greater than 1929.025 and filter outdoor_temperature less than 21.5 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>> for past electricity warmer than 1929.025 and outdoor temperature colder than 21.5, could you show me the data? [/INST]
filter past_electricity greater than 1929.025 and filter outdoor_temperature less than 21.5 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 past electricity warmer than 1929.025 and indoor temperature colder than 13.25, what are the nine most important features for the model's predictions? [/INST]
filter past_electricity greater than 1929.025 and filter indoor_temperature less than 13.25 and important topk 9 [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 exceeds 1929.025 is the case, could you print out the data items [/INST]
filter past_electricity greater than 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>> what's the average past electricity for people where everything but not outdoor temperature of 21.5? [/INST]
filter outdoor_temperature not equal to 21.5 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 the labels for indoor temperature is equal to but not greater than 13.25 [/INST]
filter indoor_temperature less equal than 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>> display most important features [/INST]
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>> for instances everything but not outdoor temperature of 21.5, show statistics for the outdoor temperature feature [/INST]
filter outdoor_temperature not equal to 21.5 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 are some typical mistakes on past electricity warmer than 1929.025 [/INST]
filter past_electricity greater than 1929.025 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 instances past electricity upper than 1929.025, show statistics for the outdoor temperature feature [/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>> if people with outdoor temperature warmer than 21.82399577 were to have outdoor temperature increased by 21.5, what would the top 5 most important features be? [/INST]
filter outdoor_temperature greater than 21.82399577 and change outdoor_temperature increase 21.5 and important topk 5 [e]
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