Datasets:
krkv
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energy-prompt2query

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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 warmer than 21.5 and indoor temperature colder than 13.25? [/INST]
filter outdoor_temperature greater than 21.5 and filter indoor_temperature less than 13.25 and explain features [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> what are the 3 most important features in the data when reasoning about past electricity equal to or below 1929.025 to make decisions? [/INST]
filter past_electricity less equal than 1929.025 and important topk 3 [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> what's the gold label for 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 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 types of errors do you make here? [/INST]
previousfilter and mistake typical [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> for outdoor temperature equal to or below 21.5, what is the distribution of outdoor temperature? [/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>> why does my model predict those with outdoor temperature not less than 21.5 as true? [/INST]
filter outdoor_temperature greater equal than 21.5 and explain features [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> what's the statistics of outdoor temperature for data where everything but not indoor temperature of 13.25? [/INST]
filter indoor_temperature not equal to 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>> what's the average past electricity for people where indoor temperature the same or more than 13.25? [/INST]
filter indoor_temperature greater equal 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>> please tell me what the model predicts instances with 13.25 indoor temperature or 640.4 past electricity or 21.5 outdoor temperature? [/INST]
filter indoor_temperature equal to 13.25 or 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>> in the case that feature outdoor temperature is decreased by 21.82399577, what would the top 10 most important features be in the model's prediction? [/INST]
change outdoor_temperature decrease 21.82399577 and important topk 10 [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, how well, in terms of accuracy, does the model do on the training and test data [/INST]
filter past_electricity greater than 1929.025 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 are the feature interactions for the model's predictions on the data? [/INST]
interact [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 predictions of people more than 15.25 indoor temperature if they had 21.82399577 fewer outdoor temperature [/INST]
filter indoor_temperature greater than 15.25 and change outdoor_temperature decrease 21.82399577 and predict [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> could you show me predictions for instances with 13.25 indoor temperature or 640.4 past electricity? [/INST]
filter indoor_temperature equal to 13.25 or filter past_electricity equal to 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>> show me all the data where past electricity the same or more than 1929.025 [/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>> what are some mistakes on past electricity exceeds 640.4 the model typically makes [/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>> what aspects of the data do you consider when reasoning about indoor temperature warmer than 15.25 and outdoor temperature colder than 21.5 to make decisions? [/INST]
filter indoor_temperature greater than 15.25 and filter outdoor_temperature less than 21.5 and explain features [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> please show the items of indoor temperature is not less than 15.25 in the data [/INST]
filter indoor_temperature greater equal than 15.25 and show [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> what about if they had 640.4 less past electricity [/INST]
previousfilter and change past_electricity decrease 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's your motivation for deciding the predictions of past electricity not equal to 1929.025 [/INST]
filter past_electricity not equal to 1929.025 and explain features [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> indoor temperature is equal to but not greater than 13.25 return incorrect predictions [/INST]
filter indoor_temperature less equal than 13.25 and mistake sample [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> for samples with outdoor temperature greater that 21.5, how did the model come to the conclusion on their predictions? [/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>> how many instances are indoor temperature colder than 15.25 [/INST]
filter indoor_temperature less than 15.25 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>> i want to better understand your reasoning on data with indoor temperature warmer than 13.25 and outdoor temperature colder than 21.5 [/INST]
filter indoor_temperature greater than 13.25 and filter outdoor_temperature less than 21.5 and 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 number of indoor temperature equal to or below 13.25 data points [/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>> for data where indoor temperature equal to or below 13.25, what is the average values of outdoor temperature? [/INST]
filter indoor_temperature less equal 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 outdoor temperature were increased by 21.82399577, tell me about the importance of indoor temperature and 13.25 [/INST]
change outdoor_temperature increase 21.82399577 and important indoor_temperature and important 13.25 [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 warmer than 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>> could you inform me how you reasoned about people in the data where indoor temperature warmer than 13.25 and outdoor temperature colder than 21.5 [/INST]
filter indoor_temperature greater than 13.25 and filter outdoor_temperature less than 21.5 and 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 statistics of indoor temperature for data where indoor temperature more than 13.25? [/INST]
filter indoor_temperature greater than 13.25 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 does the model predict 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 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 gold label for indoor temperature the same or more than 15.25 [/INST]
filter indoor_temperature greater equal than 15.25 and label [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> what's the accuracy for people past electricity not equal to 1929.025? [/INST]
filter past_electricity not equal to 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 indoor temperature of 15.25, please help me understand your reasoning process [/INST]
filter indoor_temperature equal to 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>> for data where indoor temperature of 15.25, what is the statistics of indoor temperature? [/INST]
filter indoor_temperature 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>> for data where past electricity warmer than 1929.025, what is the average values of past electricity? [/INST]
filter past_electricity greater 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 about everything but not outdoor temperature of 21.5 [/INST]
filter outdoor_temperature not equal to 21.5 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 past electricity warmer than 1929.025 and indoor temperature colder than 13.25 that the model makes [/INST]
filter past_electricity greater than 1929.025 and filter indoor_temperature less than 13.25 and mistake sample [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> outdoor temperature the same or more than 21.82399577 sample mistakes [/INST]
filter outdoor_temperature greater equal 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>> everything but not indoor temperature of 13.25 show faulty predictions [/INST]
filter indoor_temperature not equal to 13.25 and mistake sample [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> for outdoor temperature is not greater than 21.5, what is the distribution of the label? [/INST]
filter outdoor_temperature less equal 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>> past electricity warmer than 1929.025 and indoor temperature colder than 15.25 sample mistakes [/INST]
filter past_electricity greater than 1929.025 and filter indoor_temperature less 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>> if people with past electricity is not greater than 1929.025 were to have outdoor temperature increased by 21.5, what would the top 5 most important features be? [/INST]
filter past_electricity less equal than 1929.025 and change outdoor_temperature increase 21.5 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's the statistics of past electricity for data where indoor temperature warmer than 13.25 and outdoor temperature colder than 21.5? [/INST]
filter indoor_temperature greater than 13.25 and filter outdoor_temperature less than 21.5 and 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 3 most important features in the data when reasoning about indoor temperature warmer than 13.25 to make decisions? [/INST]
filter indoor_temperature greater than 13.25 and important topk 3 [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> for instances with a outdoor temperature over 21.5 is past electricity less important than indoor temperature? [/INST]
filter outdoor_temperature greater than 21.5 and important past_electricity and important 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>> if people with indoor temperature is not greater than 15.25 were to have outdoor temperature increased by 21.5, what would the top 5 most important features be? [/INST]
filter indoor_temperature less equal than 15.25 and change outdoor_temperature increase 21.5 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>> how about outdoor temperature more than 21.82399577 [/INST]
filter outdoor_temperature greater than 21.82399577 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>> for past electricity is not less than 640.4, what is the distribution of indoor temperature? [/INST]
filter past_electricity greater equal than 640.4 and statistic indoor_temperature [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> what's the number of everything but not past electricity of 1929.025 data points [/INST]
filter past_electricity not equal to 1929.025 and countdata [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> for instances outdoor temperature warmer than 21.5 and indoor temperature colder than 13.25, show statistics for the indoor temperature feature [/INST]
filter outdoor_temperature greater than 21.5 and filter indoor_temperature less than 13.25 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>> how many instances are past electricity exceeds 1929.025 [/INST]
filter past_electricity greater than 1929.025 and countdata [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> for outdoor temperature warmer than 21.82399577 and indoor temperature colder than 15.25, please help me understand your reasoning process [/INST]
filter outdoor_temperature greater than 21.82399577 and filter indoor_temperature less 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>> what's the statistics of outdoor temperature for data where indoor temperature upper than 15.25? [/INST]
filter indoor_temperature greater than 15.25 and statistic outdoor_temperature [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> indoor temperature warmer than 13.25 and past electricity colder than 1929.025 show faulty predictions [/INST]
filter indoor_temperature greater than 13.25 and filter past_electricity less than 1929.025 and mistake sample [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> what's the accuracy for people indoor temperature more than 13.25? [/INST]
filter indoor_temperature greater than 13.25 and score default [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> can you provide me with reasoning for the model's predictions on outdoor temperature warmer than 21.82399577 and past electricity colder than 1929.025? [/INST]
filter outdoor_temperature greater than 21.82399577 and filter past_electricity less than 1929.025 and explain features [e]
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[INST] <<SYS>> Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following: <</SYS>> what's the statistics of indoor temperature for data where 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 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 are some typical mistakes on indoor temperature warmer than 13.25 and past electricity colder than 1929.025 [/INST]
filter indoor_temperature greater than 13.25 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>> what are some typical mistakes on past electricity is not less than 1929.025 [/INST]
filter past_electricity greater equal 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 everything but not indoor temperature of 13.25, how many instances in the data are there? [/INST]
filter indoor_temperature not equal to 13.25 and countdata [e]
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