📄 es¶
Note
ID: cards.almost_evil.es | Type: TaskCard
{
"loader": {
"path": "0x22almostEvil/multilingual-wikihow-qa-16k",
"type": "load_hf"
},
"preprocess_steps": [
{
"_argv": [
"METADATA"
],
"function": "json.loads",
"to_field": "metadata",
"type": "apply"
},
{
"field_to_field": [
[
"metadata/language",
"extracted_language"
]
],
"type": "copy_fields"
},
{
"condition": "eq",
"type": "filter_by_condition",
"values": {
"extracted_language": "es"
}
},
{
"fields": [
"extracted_language",
"metadata"
],
"type": "remove_fields"
},
{
"mix": {
"test": "train[5%]",
"train": "train[90%]",
"validation": "train[5%]"
},
"type": "split_random_mix"
},
{
"field_to_field": {
"INSTRUCTION": "question"
},
"type": "rename_fields"
},
{
"fields": [
"RESPONSE"
],
"to_field": "answers",
"type": "list_field_values"
}
],
"task": "tasks.qa.open",
"templates": "templates.qa.open.all",
"type": "task_card"
}
Explanation about TaskCard¶
TaskCard delineates the phases in transforming the source dataset into a model-input, and specifies the metrics for evaluation of model-output.
- Attributes:
loader: specifies the source address and the loading operator that can access that source and transform it into a unitxt multistream.
preprocess_steps: list of unitxt operators to process the data source into a model-input.
task: specifies the fields (of the already (pre)processed instance) making the inputs, the fields making the outputs, and the metrics to be used for evaluating the model output.
templates: format strings to be applied on the input fields (specified by the task) and the output fields. The template also carries the instructions and the list of postprocessing steps, to be applied to the model output.
Explanation about FilterByCondition¶
Filters a stream, yielding only instances in which the values in required fields follow the required condition operator.
Raises an error if a required field name is missing from the input instance.
- Args:
values (Dict[str, Any]): Field names and respective Values that instances must match according the condition, to be included in the output. condition: the name of the desired condition operator between the specified (sub) field’s value and the provided constant value. Supported conditions are (“gt”, “ge”, “lt”, “le”, “ne”, “eq”, “in”,”not in”) error_on_filtered_all (bool, optional): If True, raises an error if all instances are filtered out. Defaults to True.
- Examples:
FilterByCondition(values = {“a”:4}, condition = “gt”) will yield only instances where field “a” contains a value > 4 FilterByCondition(values = {“a”:4}, condition = “le”) will yield only instances where “a”<=4 FilterByCondition(values = {“a”:[4,8]}, condition = “in”) will yield only instances where “a” is 4 or 8 FilterByCondition(values = {“a”:[4,8]}, condition = “not in”) will yield only instances where “a” different from 4 or 8 FilterByCondition(values = {“a/b”:[4,8]}, condition = “not in”) will yield only instances where “a” is
a dict in which key “b” is mapped to a value that is neither 4 nor 8
- FilterByCondition(values = {“a[2]”:4}, condition = “le”) will yield only instances where “a” is a list whose 3-rd
element is <= 4
Explanation about Apply¶
A class used to apply a python function and store the result in a field.
- Args:
function (str): name of function. to_field (str): the field to store the result additional arguments are field names passed to the function
Examples: Store in field “b” the uppercase string of the value in field “a” Apply(“a”, function=str.upper, to_field=”b”)
Dump the json representation of field “t” and store back in the same field. Apply(“t”, function=json.dumps, to_field=”t”)
Set the time in a field ‘b’. Apply(function=time.time, to_field=”b”)
Explanation about RemoveFields¶
Remove specified fields from each instance in a stream.
- Args:
fields (List[str]): The fields to remove from each instance.
Explanation about CopyFields¶
Copies values from specified fields to specified fields.
- Args (of parent class):
field_to_field (Union[List[List], Dict[str, str]]): A list of lists, where each sublist contains the source field and the destination field, or a dictionary mapping source fields to destination fields.
- Examples:
An input instance {“a”: 2, “b”: 3}, when processed by CopyField(field_to_field={“a”: “b”} would yield {“a”: 2, “b”: 2}, and when processed by CopyField(field_to_field={“a”: “c”} would yield {“a”: 2, “b”: 3, “c”: 2}
with field names containing / , we can also copy inside the field: CopyFields(field_to_field={“a/0”: “a”}) would process instance {“a”: [1, 3]} into {“a”: 1}
Explanation about ListFieldValues¶
Concatenates values of multiple fields into a list, and assigns it to a new field.
Explanation about RenameFields¶
Renames fields.
Move value from one field to another, potentially, if field name contains a /, from one branch into another. Remove the from field, potentially part of it in case of / in from_field.
- Examples:
RenameFields(field_to_field={“b”: “c”}) will change inputs [{“a”: 1, “b”: 2}, {“a”: 2, “b”: 3}] to [{“a”: 1, “c”: 2}, {“a”: 2, “c”: 3}]
RenameFields(field_to_field={“b”: “c/d”}) will change inputs [{“a”: 1, “b”: 2}, {“a”: 2, “b”: 3}] to [{“a”: 1, “c”: {“d”: 2}}, {“a”: 2, “c”: {“d”: 3}}]
RenameFields(field_to_field={“b”: “b/d”}) will change inputs [{“a”: 1, “b”: 2}, {“a”: 2, “b”: 3}] to [{“a”: 1, “b”: {“d”: 2}}, {“a”: 2, “b”: {“d”: 3}}]
RenameFields(field_to_field={“b/c/e”: “b/d”}) will change inputs [{“a”: 1, “b”: {“c”: {“e”: 2, “f”: 20}}}] to [{“a”: 1, “b”: {“c”: {“f”: 20}, “d”: 2}}]
Explanation about SplitRandomMix¶
Splits a multistream into new streams (splits), whose names, source input stream, and amount of instances, are specified by arg ‘mix’.
The keys of arg ‘mix’, are the names of the new streams, the values are of the form: ‘name-of-source-stream[percentage-of-source-stream]’ Each input instance, of any input stream, is selected exactly once for inclusion in any of the output streams.
Examples: When processing a multistream made of two streams whose names are ‘train’ and ‘test’, by SplitRandomMix(mix = { “train”: “train[99%]”, “validation”: “train[1%]”, “test”: “test” }) the output is a multistream, whose three streams are named ‘train’, ‘validation’, and ‘test’. Output stream ‘train’ is made of randomly selected 99% of the instances of input stream ‘train’, output stream ‘validation’ is made of the remaining 1% instances of input ‘train’, and output stream ‘test’ is made of the whole of input stream ‘test’.
When processing the above input multistream by SplitRandomMix(mix = { “train”: “train[50%]+test[0.1]”, “validation”: “train[50%]+test[0.2]”, “test”: “test[0.7]” }) the output is a multistream, whose three streams are named ‘train’, ‘validation’, and ‘test’. Output stream ‘train’ is made of randomly selected 50% of the instances of input stream ‘train’ + randomly selected 0.1 (i.e., 10%) of the instances of input stream ‘test’. Output stream ‘validation’ is made of the remaining 50% instances of input ‘train’+ randomly selected 0.2 (i.e., 20%) of the original instances of input ‘test’, that were not selected for output ‘train’, and output stream ‘test’ is made of the remaining instances of input ‘test’.
References: templates.qa.open.all, tasks.qa.open
Read more about catalog usage here.