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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: '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 above 15.25 were to have indoor temperature increased by 13.25, what would the top 5 most important features be? [/INST] | filter indoor_temperature greater 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 some common mistakes you make when predicting past electricity more than 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>>
please describe the number of indoor temperature above 13.25 in the data [/INST] | filter indoor_temperature greater than 13.25 and coundata [e] | [
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[INST] <<SYS>>
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<</SYS>>
show me the test set performance [/INST] | 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>>
outdoor temperature equal to or below 21.82399577 sample mistakes [/INST] | filter outdoor_temperature less 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>>
what's the average outdoor temperature for people where past electricity is not greater than 640.4? [/INST] | filter past_electricity less equal than 640.4 and statistic outdoor_temperature [e] | [
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[INST] <<SYS>>
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for everything but not past electricity of 1929.025, could you show me if the model predicts the data correctly for some examples? [/INST] | filter past_electricity not equal to 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 number of indoor temperature warmer than 15.25 and outdoor temperature colder than 21.82399577 data points [/INST] | filter indoor_temperature greater than 15.25 and 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 data points does the model typically predict mistakenly on people with indoor temperature above 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 data where indoor temperature warmer than 15.25 and past electricity colder than 1929.025, what is the statistics of indoor temperature? [/INST] | filter indoor_temperature greater than 15.25 and filter past_electricity less than 1929.025 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 the ground truth labels in the data for past electricity more than 1929.025 [/INST] | filter past_electricity greater than 1929.025 and label [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
what's the average outdoor temperature for people where past electricity warmer than 640.4 and indoor temperature colder than 15.25? [/INST] | filter past_electricity greater than 640.4 and filter indoor_temperature less 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>>
just on instances where indoor temperature equal to or below 13.25, show me the labels? [/INST] | filter indoor_temperature less equal than 13.25 and label [e] | [
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[INST] <<SYS>>
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<</SYS>>
if people with outdoor temperature warmer than 21.82399577 and indoor temperature colder than 15.25 were to have past electricity increased by 1929.025, what would the top 5 most important features be? [/INST] | filter outdoor_temperature greater than 21.82399577 and filter indoor_temperature less than 15.25 and change past_electricity increase 1929.025 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 those with past electricity greater than 640.4, what are the top three most important features for the model's predictions? [/INST] | filter past_electricity greater than 640.4 and important topk 3 [e] | [
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[INST] <<SYS>>
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<</SYS>>
what are the most important features in the data when reasoning about outdoor temperature is equal to but not greater than 21.82399577 to make decisions? [/INST] | filter outdoor_temperature less equal than 21.82399577 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>>
outdoor temperature is not less than 21.5 show faulty predictions [/INST] | filter outdoor_temperature greater 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>>
show me people that have data points 89 and 90 [/INST] | filter id 89 and filter id 90 and show [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
what's the statistics of past electricity for data where outdoor temperature is not greater than 21.82399577? [/INST] | filter outdoor_temperature less equal than 21.82399577 and statistic past_electricity [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
what would the predictions be like for instances with outdoor temperature less than 21.5 if you were to increase indoor temperature by 15.25? [/INST] | filter outdoor_temperature less than 21.5 and change indoor_temperature increase 15.25 and predict [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
for past electricity above 640.4, what are some mistakes you typically make? [/INST] | filter past_electricity greater than 640.4 and mistake typical [e] | [
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[INST] <<SYS>>
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<</SYS>>
what's the accuracy for people outdoor temperature less than 21.5? [/INST] | filter outdoor_temperature less than 21.5 and score default [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
how many instances are indoor temperature not equal to 15.25 [/INST] | filter indoor_temperature not equal to 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>>
if outdoor temperature is increased by 21.82399577 for {filer_text}, what would the model predict? [/INST] | filter indoor_temperature greater equal than 15.25 and change outdoor_temperature increase 21.82399577 and predict [e] | [
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[INST] <<SYS>>
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could you inform me how you reasoned about people in the data where 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 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 upper than 13.25 show bad predictions [/INST] | filter indoor_temperature greater 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>>
what aspects of the data do you consider when reasoning about outdoor temperature warmer than 21.5 and indoor temperature colder than 15.25 to make decisions? [/INST] | filter outdoor_temperature greater than 21.5 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>>
if past electricity is increased by 1929.025 for {filer_text}, what would the model predict? [/INST] | filter past_electricity greater than 640.4 and filter indoor_temperature less than 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>>
what's the gold label for past electricity not equal to 1929.025 [/INST] | filter past_electricity not equal to 1929.025 and label [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
please describe the number of outdoor temperature warmer than 21.5 in the data [/INST] | filter outdoor_temperature greater 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>>
how about indoor temperature warmer than 13.25 and past electricity colder than 640.4 [/INST] | filter indoor_temperature greater than 13.25 and filter past_electricity less than 640.4 and previousoperation [e] | [
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[INST] <<SYS>>
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<</SYS>>
for outdoor temperature is not greater than 21.82399577, what are the ground truth labels [/INST] | filter outdoor_temperature less equal than 21.82399577 and label [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
if indoor temperature is increased by 15.25 for {filer_text}, what would the model predict? [/INST] | filter past_electricity less equal than 640.4 and change indoor_temperature increase 15.25 and predict [e] | [
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[INST] <<SYS>>
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how many instances have outdoor temperature warmer than 21.5 [/INST] | filter outdoor_temperature greater than 21.5 and countdata [e] | [
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[INST] <<SYS>>
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<</SYS>>
if indoor temperature is decreased by 13.25 for {filer_text}, what would the model predict? [/INST] | filter past_electricity greater than 1929.025 and change indoor_temperature increase 13.25 and predict [e] | [
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[INST] <<SYS>>
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<</SYS>>
for outdoor temperature exceeds 21.5, please help me understand your reasoning process [/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>>
for outdoor temperature more than 21.82399577, what is the distribution of outdoor temperature? [/INST] | filter outdoor_temperature greater 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>>
if past electricity is decreased by 640.4 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 past_electricity increase 640.4 and predict [e] | [
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[INST] <<SYS>>
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<</SYS>>
if we were to increase outdoor temperature by 21.5, how would the feature importances for past electricity and 1929.025 compare? [/INST] | change outdoor_temperature increase 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>>
what are the data labels for indoor temperature less than 15.25 [/INST] | 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>>
can you provide me with reasoning for the model's predictions on outdoor temperature less than 21.5? [/INST] | 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>>
for outdoor temperature warmer than 21.5 and past electricity colder than 640.4, what is the distribution of the label? [/INST] | filter outdoor_temperature greater than 21.5 and filter past_electricity less than 640.4 and statistic target [e] | [
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[INST] <<SYS>>
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<</SYS>>
for instances with a past electricity less than 640.4 is outdoor temperature less important than indoor temperature [/INST] | filter past_electricity less than 640.4 and important outdoor_temperature 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>>
what does the model predict for past electricity of 640.4? [/INST] | filter past_electricity equal to 640.4 and predict [e] | [
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[INST] <<SYS>>
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<</SYS>>
for those where outdoor temperature colder than 21.5 is the case, could you tell me the total number of item in the data [/INST] | filter outdoor_temperature less than 21.5 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>>
return the data where indoor temperature above 13.25 people [/INST] | filter indoor_temperature greater than 13.25 and show [e] | [
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[INST] <<SYS>>
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<</SYS>>
for past electricity colder than 1929.025, what are the nine most important features for the model's predictions? [/INST] | filter past_electricity less than 1929.025 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>>
what's the accuracy performance of the model on the training data for individuals with data points 89 and 90 [/INST] | filter id 89 and filter id 90 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 ground truth labels in the data for outdoor temperature less than 21.5 [/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>>
what would happen to the distribution of model predictions if individuals with past electricity greater than 640.4 had indoor temperature decreased by 13.25 [/INST] | filter past_electricity greater than 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>>
what's the average indoor temperature for people where indoor temperature warmer than 15.25 and outdoor temperature colder than 21.82399577? [/INST] | filter indoor_temperature greater than 15.25 and filter outdoor_temperature less than 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>>
what are the labels for past electricity the same or more than 640.4 [/INST] | filter past_electricity greater equal than 640.4 and label [e] | [
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[INST] <<SYS>>
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<</SYS>>
for outdoor temperature is not greater than 21.82399577, what is the distribution of the label? [/INST] | filter outdoor_temperature less equal than 21.82399577 and statistic target [e] | [
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[INST] <<SYS>>
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<</SYS>>
for outdoor temperature less than 21.5, what is the distribution of indoor temperature? [/INST] | 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>>
does the model predict past electricity is equal to but not greater than 1929.025 correctly? [/INST] | filter past_electricity less equal than 1929.025 and mistake sample [e] | [
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[INST] <<SYS>>
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what are the feature interaction effects for people with outdoor temperature over 21.82399577? [/INST] | filter outdoor_temperature greater than 21.82399577 and 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 most important features in the data when reasoning about outdoor temperature not equal to 21.5 to make decisions? [/INST] | filter outdoor_temperature not equal to 21.5 and important all [e] | [
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[INST] <<SYS>>
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what's the accuracy performance of the model on the training data for individuals with past electricity of 640.4 [/INST] | filter past_electricity equal to 640.4 and score default [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
what aspects of the data do you consider when reasoning about past electricity colder than 640.4 to make decisions? [/INST] | filter past_electricity less than 640.4 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 everything but not past electricity of 640.4? [/INST] | filter past_electricity not equal to 640.4 and score default [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
for data where past electricity is not greater than 640.4, what is the average values of outdoor temperature? [/INST] | filter past_electricity less equal 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>>
for outdoor temperature not equal to 21.5, what is the distribution of the target variable? [/INST] | filter outdoor_temperature not equal to 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>>
for past electricity less than 1929.025, what are some mistakes you typically make? [/INST] | 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>>
for everything but not past electricity of 1929.025, could you show me the data? [/INST] | filter past_electricity not equal to 1929.025 and show [e] | [
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[INST] <<SYS>>
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<</SYS>>
for those where indoor temperature the same or more than 15.25 is the case, could you tell me the total number of item in the data [/INST] | filter indoor_temperature greater equal 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>>
how would the importances compare if indoor temperature is 13.25 of past electricity and outdoor temperature? [/INST] | change indoor_temperature set 13.25 and important past_electricity and important outdoor_temperature [e] | [
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[INST] <<SYS>>
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<</SYS>>
could you show me a few examples you get incorrect for 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 mistake sample [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
for indoor temperature upper than 15.25, what is the distribution of outdoor temperature? [/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>>
if people with indoor temperature less than 13.25 were to have outdoor temperature increased by 21.82399577, what would the top 5 most important features be? [/INST] | filter indoor_temperature less than 13.25 and change outdoor_temperature increase 21.82399577 and important topk 5 [e] | [
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[INST] <<SYS>>
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<</SYS>>
past electricity colder than 640.4 show mistakes [/INST] | filter past_electricity less 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>>
can you provide me with reasoning for the model's predictions on outdoor temperature is not less than 21.5? [/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>>
show me all the data where past electricity warmer than 1929.025 and indoor temperature colder than 13.25 [/INST] | filter past_electricity greater than 1929.025 and filter indoor_temperature less than 13.25 and show [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
what would the explanation be if you set indoor temperature to 13.25 for instance id 15255? [/INST] | filter id 15255 and change indoor_temperature set 13.25 and explain features [e] | [
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[INST] <<SYS>>
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<</SYS>>
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: '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 we were to increase past electricity by 640.4, how would the importance of indoor temperature and outdoor temperature compare? [/INST] | change past_electricity increase 640.4 and important indoor_temperature and important 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 outdoor temperature for people where indoor temperature exceeds 13.25? [/INST] | filter indoor_temperature greater 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>>
what's the average outdoor temperature for people where outdoor temperature not equal to 21.5? [/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's the value of indoor temperature of id 25 [/INST] | filter id 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>>
i want to better understand your reasoning on data with indoor temperature is not greater than 13.25 [/INST] | filter indoor_temperature less equal 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 is not less than 1929.025 to make decisions? [/INST] | filter past_electricity greater 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>>
if indoor temperature is decreased by 15.25 for {filer_text}, what would the model predict? [/INST] | filter past_electricity greater than 640.4 and filter indoor_temperature less than 15.25 and change indoor_temperature increase 15.25 and predict [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
what's the most important feature for the model predicting indoor temperature warmer than 15.25 and outdoor temperature colder than 21.82399577? [/INST] | filter indoor_temperature greater than 15.25 and filter outdoor_temperature less than 21.82399577 and important topk 1 [e] | [
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[INST] <<SYS>>
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<</SYS>>
for past electricity is not less than 1929.025, please help me understand your reasoning process [/INST] | filter past_electricity greater 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 gold label for indoor temperature less than 15.25 [/INST] | filter indoor_temperature less than 15.25 and label [e] | [
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[INST] <<SYS>>
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<</SYS>>
what are the 3 most important features in the data when reasoning about indoor temperature not equal to 13.25 to make decisions? [/INST] | filter indoor_temperature not equal to 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 those where outdoor temperature above 21.82399577 is the case, could you print out the data items [/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>>
what does the model predict for indoor temperature less than 13.25? [/INST] | filter indoor_temperature less than 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 wrongly on people with outdoor temperature of 21.5? [/INST] | filter outdoor_temperature equal to 21.5 and mistake typical [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
can you provide me with reasoning for the model's predictions on 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>>
what aspects of the data do you consider when reasoning about indoor temperature is not greater than 13.25 to make decisions [/INST] | filter indoor_temperature less equal 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's the average past electricity for people where indoor temperature equal to or below 15.25? [/INST] | filter indoor_temperature less equal than 15.25 and statistic past_electricity [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
for indoor temperature not equal to 13.25, what is the distribution of past electricity? [/INST] | filter indoor_temperature not equal to 13.25 and statistic past_electricity [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
what's the statistics of past electricity for data where indoor temperature is equal to but not greater than 13.25? [/INST] | filter indoor_temperature less equal than 13.25 and statistic past_electricity [e] | [
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[INST] <<SYS>>
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<</SYS>>
if indoor temperature is decreased by 13.25 for {filer_text}, what would the model predict? [/INST] | filter id 89 and filter id 90 and change indoor_temperature increase 13.25 and predict [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
for those where outdoor temperature warmer than 21.82399577 and indoor temperature colder than 13.25 is the case, could you print out the data items [/INST] | filter outdoor_temperature greater than 21.82399577 and filter indoor_temperature less than 13.25 and show [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
for indoor temperature warmer than 13.25 and past electricity colder than 640.4, what is the distribution of the label? [/INST] | filter indoor_temperature greater than 13.25 and filter past_electricity less than 640.4 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 data points does the model typically predict wrongly on people with outdoor temperature is equal to but not greater than 21.5? [/INST] | filter outdoor_temperature less equal 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>>
could you tell me why the model predicts those instances with indoor temperature above 15.25 and that have a past electricity of 1929.025 and also what these instances are? [/INST] | filter indoor_temperature greater than 15.25 and filter past_electricity equal to 1929.025 and explain features and show [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
what's your motivation for deciding the predictions of past electricity more than 1929.025 [/INST] | filter past_electricity greater than 1929.025 and explain features [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
if people with indoor temperature equal to or below 13.25 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 13.25 and change outdoor_temperature increase 21.5 and important topk 5 [e] | [
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