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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
how about outdoor temperature warmer than 21.5 [/INST] | filter outdoor_temperature greater than 21.5 and previousoperation [e] | [
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[INST] <<SYS>>
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<</SYS>>
if outdoor temperature were increased by 21.5, tell me about the importance of indoor temperature and 13.25 [/INST] | change outdoor_temperature increase 21.5 and important indoor_temperature and important 13.25 [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
what's the accuracy performance of the model on the training data for individuals with past electricity less than 1929.025 [/INST] | filter past_electricity less than 1929.025 and score default [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
for instances past electricity upper than 640.4, show statistics for the past electricity feature [/INST] | filter past_electricity greater than 640.4 and statistic past_electricity [e] | [
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[INST] <<SYS>>
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<</SYS>>
please show the items of indoor temperature warmer than 13.25 and past electricity colder than 640.4 in the data [/INST] | filter indoor_temperature greater than 13.25 and filter past_electricity less than 640.4 and show [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
for outdoor temperature above 21.5, how well, in terms of accuracy, does the model do on the training and test data [/INST] | filter outdoor_temperature greater than 21.5 and score default and score default [e] | [
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[INST] <<SYS>>
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what's the statistics of outdoor temperature for data where outdoor temperature upper than 21.82399577? [/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>>
show me people that have outdoor temperature the same or more than 21.82399577 [/INST] | filter outdoor_temperature greater equal 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>>
if people with indoor temperature warmer than 13.25 and past electricity colder than 1929.025 were to have outdoor temperature increased by 21.5, what would the top 5 most important features be? [/INST] | filter indoor_temperature greater than 13.25 and filter past_electricity less than 1929.025 and change outdoor_temperature increase 21.5 and important topk 5 [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
what are the most important features in the data when reasoning about indoor temperature is not less than 13.25 to make decisions? [/INST] | filter indoor_temperature greater equal than 13.25 and important all [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
what are the 3 most important features in the data when reasoning about outdoor temperature exceeds 21.82399577 to make decisions? [/INST] | filter outdoor_temperature greater than 21.82399577 and important topk 3 [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
what are some common mistakes you make when predicting past electricity upper than 1929.025? [/INST] | filter past_electricity greater than 1929.025 and mistake typical [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
what's the most important feature for the model predicting past electricity not equal to 1929.025? [/INST] | filter past_electricity not equal to 1929.025 and important topk 1 [e] | [
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[INST] <<SYS>>
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<</SYS>>
can you provide me with reasoning for the model's predictions on indoor temperature equal to or below 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>>
outdoor temperature upper than 21.82399577 return incorrect predictions [/INST] | filter outdoor_temperature greater than 21.82399577 and mistake sample [e] | [
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[INST] <<SYS>>
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<</SYS>>
what are some mistakes on outdoor temperature warmer than 21.82399577 and past electricity colder than 1929.025 the model typically makes [/INST] | filter outdoor_temperature greater than 21.82399577 and filter past_electricity less than 1929.025 and mistake typical [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
how would you characterize the mistakes you make on 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>>
what are the data labels for indoor temperature warmer than 15.25 [/INST] | filter indoor_temperature greater 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>>
if indoor temperature is decreased by 13.25 for {filer_text}, what would the model predict? [/INST] | filter outdoor_temperature greater than 21.5 and filter past_electricity less than 640.4 and change indoor_temperature increase 13.25 and predict [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
for past electricity not equal to 640.4, how many instances in the data are there? [/INST] | filter past_electricity not equal to 640.4 and countdata [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
what aspects of the data do you consider when reasoning about outdoor temperature warmer than 21.82399577 and indoor temperature colder than 13.25 to make decisions? [/INST] | filter outdoor_temperature greater than 21.82399577 and filter indoor_temperature less than 13.25 and explain features [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
show me instances that are outdoor temperature more than 21.5 [/INST] | filter outdoor_temperature greater than 21.5 and show [e] | [
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[INST] <<SYS>>
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<</SYS>>
for past electricity exceeds 640.4, could you show me the data? [/INST] | filter past_electricity greater than 640.4 and show [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
what needs to happen for this to change? [/INST] | previousfilter and explain cfe [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: '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 less than 15.25? [/INST] | filter indoor_temperature less 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 equal to or below 15.25, what are some mistakes you typically make? [/INST] | filter indoor_temperature less equal than 15.25 and mistake typical [e] | [
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[INST] <<SYS>>
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for data where indoor temperature colder than 13.25, what is the average values of indoor temperature? [/INST] | filter indoor_temperature less than 13.25 and statistic indoor_temperature [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
show me all the data where indoor temperature warmer than 15.25 and past electricity colder than 1929.025 [/INST] | filter indoor_temperature greater than 15.25 and filter past_electricity less than 1929.025 and show [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
for indoor temperature warmer than 13.25 and outdoor temperature colder than 21.5, what is the distribution of outdoor temperature? [/INST] | filter indoor_temperature greater than 13.25 and filter outdoor_temperature less than 21.5 and statistic outdoor_temperature [e] | [
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[INST] <<SYS>>
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<</SYS>>
for instances past electricity is equal to but not greater than 1929.025, show statistics for the indoor temperature feature [/INST] | filter past_electricity less equal than 1929.025 and statistic indoor_temperature [e] | [
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[INST] <<SYS>>
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<</SYS>>
for past electricity warmer than 1929.025, what is the distribution of indoor temperature? [/INST] | filter past_electricity greater 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>>
can you provide me with reasoning for the model's predictions on outdoor temperature is equal to but not greater than 21.82399577? [/INST] | filter outdoor_temperature less equal than 21.82399577 and explain features [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
what's the number of data points 89 and 90 data points [/INST] | filter id 89 and filter id 90 and countdata [e] | [
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[INST] <<SYS>>
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what aspects of the data do you consider when reasoning about everything but not past electricity of 1929.025 to make decisions? [/INST] | filter past_electricity not equal to 1929.025 and explain features [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
if indoor temperature is increased by 13.25 for {filer_text}, what would the model predict? [/INST] | filter outdoor_temperature less equal than 21.5 and change indoor_temperature increase 13.25 and predict [e] | [
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[INST] <<SYS>>
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<</SYS>>
for outdoor temperature warmer than 21.82399577 and past electricity colder than 640.4, what is the distribution of the target variable? [/INST] | filter outdoor_temperature greater than 21.82399577 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>>
if people with indoor temperature warmer than 13.25 and outdoor temperature colder than 21.5 were to have outdoor temperature increased by 21.5, what would the top 5 most important features be? [/INST] | filter indoor_temperature greater than 13.25 and filter outdoor_temperature less than 21.5 and change outdoor_temperature increase 21.5 and important topk 5 [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
what are some mistakes on outdoor temperature upper than 21.82399577 that the model makes [/INST] | filter outdoor_temperature greater 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>>
can you provide me with reasoning for the model's predictions on outdoor temperature warmer than 21.82399577 and past electricity colder than 640.4? [/INST] | filter outdoor_temperature greater than 21.82399577 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>>
just on instances where past electricity more than 1929.025, show me the labels? [/INST] | filter past_electricity greater than 1929.025 and label [e] | [
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[INST] <<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>>
for instances outdoor temperature warmer than 21.82399577 and indoor temperature colder than 13.25, show statistics for the outdoor temperature feature [/INST] | filter outdoor_temperature greater than 21.82399577 and filter indoor_temperature less than 13.25 and statistic outdoor_temperature [e] | [
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[INST] <<SYS>>
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<</SYS>>
what aspects of the data do you consider when reasoning about outdoor temperature not equal to 21.82399577 to make decisions [/INST] | filter outdoor_temperature not equal to 21.82399577 and explain features [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
indoor temperature not equal to 13.25 show faulty predictions [/INST] | filter indoor_temperature not equal to 13.25 and mistake sample [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
what are the 3 most important features in the data when reasoning about past electricity warmer than 1929.025 to make decisions? [/INST] | filter past_electricity greater than 1929.025 and important topk 3 [e] | [
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[INST] <<SYS>>
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<</SYS>>
indoor temperature warmer than 13.25 sample mistakes [/INST] | filter indoor_temperature greater than 13.25 and mistake sample [e] | [
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[INST] <<SYS>>
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<</SYS>>
if people with outdoor temperature equal to or below 21.5 were to have indoor temperature increased by 13.25, what would the top 5 most important features be? [/INST] | filter outdoor_temperature less equal than 21.5 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>>
show me people that have indoor temperature is not less than 15.25 [/INST] | filter indoor_temperature greater equal than 15.25 and show [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
what would the predictions be like for instances with indoor temperature less than 15.25 if you were to increase outdoor temperature by 21.5? [/INST] | filter indoor_temperature less than 15.25 and change outdoor_temperature increase 21.5 and predict [e] | [
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[INST] <<SYS>>
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just on instances where outdoor temperature warmer than 21.82399577, show me the gold labels? [/INST] | filter outdoor_temperature greater 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>>
tell me the grand total for the number of data points where indoor temperature is less than 13.25? [/INST] | filter indoor_temperature less than 13.25 and countdata [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
could you show me a few examples you get incorrect for indoor temperature is not less than 13.25 [/INST] | filter indoor_temperature greater equal than 13.25 and mistake sample [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
if outdoor temperature is increased by 21.82399577 for {filer_text}, what would the model predict? [/INST] | filter past_electricity equal to 640.4 and change outdoor_temperature increase 21.82399577 and predict [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
what are some mistakes on indoor temperature warmer than 15.25 that the model makes [/INST] | filter indoor_temperature greater than 15.25 and mistake sample [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
for past electricity warmer than 1929.025 and indoor temperature colder than 13.25, how many instances in the data are there? [/INST] | filter past_electricity greater than 1929.025 and filter indoor_temperature less than 13.25 and countdata [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
please show the items of indoor temperature of 15.25 in the data [/INST] | filter indoor_temperature equal to 15.25 and show [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
what are the 3 most important features in the data when reasoning about indoor temperature upper than 15.25 to make decisions? [/INST] | filter indoor_temperature greater than 15.25 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>>
what aspects of the data do you consider when reasoning about indoor temperature of 15.25 to make decisions [/INST] | filter indoor_temperature equal to 15.25 and explain features [e] | [
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[INST] <<SYS>>
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give me the accuracy on the data [/INST] | score accuracy [e] | [
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[INST] <<SYS>>
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<</SYS>>
what data points does the model typically predict wrongly on people with past electricity warmer than 1929.025? [/INST] | filter past_electricity greater than 1929.025 and mistake typical [e] | [
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[INST] <<SYS>>
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please show the items of everything but not outdoor temperature of 21.82399577 in the data [/INST] | filter outdoor_temperature not equal to 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 would the prediction be if you set indoor temperature to 15.25 for instance id 115? [/INST] | filter id 115 and change indoor_temperature set 15.25 and predict [e] | [
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[INST] <<SYS>>
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[INST] <<SYS>>
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<</SYS>>
how many instances are outdoor temperature warmer than 21.5 and past electricity colder than 640.4 [/INST] | filter outdoor_temperature greater than 21.5 and filter past_electricity less than 640.4 and countdata [e] | [
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[INST] <<SYS>>
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<</SYS>>
what are the 3 most important features in the data when reasoning about everything but not indoor temperature of 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>>
how would you characterize the mistakes you make on 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>>
tell me how many ids in the data have indoor temperature upper than 15.25 [/INST] | filter indoor_temperature greater than 15.25 and countdata [e] | [
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[INST] <<SYS>>
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<</SYS>>
past electricity the same or more than 1929.025 show mistakes [/INST] | filter past_electricity greater equal than 1929.025 and mistake sample [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
what's the gold label for past electricity less than 640.4 [/INST] | filter past_electricity less than 640.4 and label [e] | [
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[INST] <<SYS>>
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<</SYS>>
how many instances have indoor temperature above 15.25 [/INST] | filter indoor_temperature greater than 15.25 and countdata [e] | [
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[INST] <<SYS>>
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<</SYS>>
how many instances are outdoor temperature is not greater than 21.5 [/INST] | filter outdoor_temperature less equal than 21.5 and countdata [e] | [
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[INST] <<SYS>>
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<</SYS>>
does the model predict past electricity above 640.4 correctly? [/INST] | filter past_electricity greater than 640.4 and mistake sample [e] | [
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[INST] <<SYS>>
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<</SYS>>
what's your motivation for deciding the predictions of outdoor temperature exceeds 21.82399577 [/INST] | filter outdoor_temperature greater than 21.82399577 and explain features [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
for data where everything but not indoor temperature of 15.25, what is the average values of past electricity? [/INST] | filter indoor_temperature not equal to 15.25 and statistic past_electricity [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
what's the gold label for outdoor temperature warmer than 21.5 and indoor temperature colder than 13.25 [/INST] | filter outdoor_temperature greater than 21.5 and filter indoor_temperature less than 13.25 and label [e] | [
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[INST] <<SYS>>
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what's the average past electricity for people where indoor temperature colder than 13.25? [/INST] | filter indoor_temperature less than 13.25 and statistic past_electricity [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
if past electricity were increased by 1929.025, tell me about the importance of indoor temperature and 15.25 [/INST] | change past_electricity increase 1929.025 and important indoor_temperature and important 15.25 [e] | [
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[INST] <<SYS>>
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<</SYS>>
display data where indoor temperature is not equal to 15.25 [/INST] | filter indoor_temperature not equal to 15.25 and show [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
for indoor temperature warmer than 13.25, what are the ground truth labels [/INST] | filter indoor_temperature greater than 13.25 and label [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
please show the items of indoor temperature more than 15.25 in the data [/INST] | filter indoor_temperature greater than 15.25 and show [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
could you inform me how you reasoned about people in the data where outdoor temperature warmer than 21.5 and past electricity colder than 1929.025 [/INST] | filter outdoor_temperature greater than 21.5 and filter past_electricity less than 1929.025 and 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 past electricity is not greater than 640.4, could you show me the data? [/INST] | filter past_electricity less equal than 640.4 and show [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
for past electricity is equal to but not greater than 1929.025, please help me understand your reasoning process [/INST] | filter past_electricity less equal than 1929.025 and explain features [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
everything but not outdoor temperature of 21.82399577 sample mistakes [/INST] | filter outdoor_temperature not equal to 21.82399577 and mistake sample [e] | [
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[INST] <<SYS>>
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<</SYS>>
what data points does the model typically predict mistakenly on people with data points 89 and 90? [/INST] | filter id 89 and filter id 90 and mistake typical [e] | [
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[INST] <<SYS>>
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<</SYS>>
what's the number of indoor temperature exceeds 13.25 data points [/INST] | filter indoor_temperature greater than 13.25 and countdata [e] | [
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[INST] <<SYS>>
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<</SYS>>
just on instances where outdoor temperature the same or more than 21.5, show me the gold labels? [/INST] | filter outdoor_temperature greater equal than 21.5 and label [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
for data where indoor temperature equal to or below 15.25, what is the statistics of past electricity? [/INST] | filter indoor_temperature less equal than 15.25 and statistic past_electricity [e] | [
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[INST] <<SYS>>
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<</SYS>>
return the data where indoor temperature not equal to 13.25 people [/INST] | filter indoor_temperature not equal to 13.25 and show [e] | [
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[INST] <<SYS>>
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<</SYS>>
what's the testing accuracy for people indoor temperature exceeds 13.25 [/INST] | filter indoor_temperature greater than 13.25 and score default [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
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what's the gold label for past electricity is not greater than 1929.025 [/INST] | filter past_electricity less equal than 1929.025 and label [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
for data where past electricity is not greater than 1929.025, what is the average values of past electricity? [/INST] | filter past_electricity less equal than 1929.025 and statistic past_electricity [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
for data where indoor temperature is less than 15.25, what is the reasoning process of the model? [/INST] | 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>>
show me instances that are indoor temperature warmer than 13.25 and past electricity colder than 1929.025 [/INST] | filter indoor_temperature greater than 13.25 and filter past_electricity less than 1929.025 and show [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
for outdoor temperature warmer than 21.82399577 and indoor temperature colder than 13.25, could you show me the data? [/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>>
if indoor temperature were increased by 13.25, tell me about the importance of past electricity and 1929.025 [/INST] | change indoor_temperature increase 13.25 and important past_electricity and important 1929.025 [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
what are some common mistakes you make when predicting outdoor temperature is 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:
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is outdoor temperature less important than indoor temperature? [/INST] | important outdoor_temperature and important indoor_temperature [e] | [
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[INST] <<SYS>>
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<</SYS>>
what would happen to the distribution of model predictions if people with outdoor temperature less than 21.82399577 had past electricity decreased by 1929.025 [/INST] | filter outdoor_temperature less than 21.82399577 and change past_electricity decrease 1929.025 and predict [e] | [
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[INST] <<SYS>>
Your task is to convert the following chat message to a command composed in a custom query language which consists from a limited number of command tokens. The command tokens that you can use are: 'interact', 'countdata', 'filter', 'explain features', 'explain cfe', 'predict', 'self', 'previousfilter', 'previousoperation', 'data', 'followup', 'important', 'important topk', 'important all', 'show', 'change', 'model', 'function', 'score default', 'score accuracy', 'label', 'mistake typical', 'mistake sample', 'statistic', 'define'. You can also use special tokens as arguments to commands, such as: 'greater', 'less', 'than', 'equal to', 'and', 'increase', 'decrease', 'set'. You can also use dataset feature names: 'id', 'outdoor_temperature', 'indoor_temperature', 'past_electricity'. Every generated command sequence is closed with a special token '[e]'. The chat message is following:
<</SYS>>
just on instances where outdoor temperature warmer than 21.82399577 and past electricity colder than 1929.025, show me the gold labels? [/INST] | filter outdoor_temperature greater than 21.82399577 and filter past_electricity less than 1929.025 and label [e] | [
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