📄 20 Newsgroups¶

This is a version of the 20 newsgroups dataset that is provided in Scikit-learn. From the Scikit-learn docs: “The 20 newsgroups dataset comprises around 18000 newsgroups posts on 20 topics split in two subsets: one for training (or development) and the other one for testing (or for performance evaluation). The split between the train and test set is based upon a message posted before and after a specific date.” See the full description on the dataset page: https://huggingface.co/datasets/SetFit/20_newsgroups.

Tags: region:us

cards.20_newsgroups

type: TaskCard
loader: 
  type: LoadHF
  path: SetFit/20_newsgroups
  streaming: True
preprocess_steps: 
  - type: FilterByCondition
    values: 
      text: 
    condition: ne
  - type: SplitRandomMix
    mix: 
      train: train[90%]
      validation: train[10%]
      test: test
  - type: Rename
    field_to_field: 
      label_text: label
  - type: MapInstanceValues
    mappers: 
      label: 
        alt.atheism: atheism
        comp.graphics: computer graphics
        comp.os.ms-windows.misc: microsoft windows
        comp.sys.ibm.pc.hardware: pc hardware
        comp.sys.mac.hardware: mac hardware
        comp.windows.x: windows x
        misc.forsale: for sale
        rec.autos: cars
        rec.motorcycles: motorcycles
        rec.sport.baseball: baseball
        rec.sport.hockey: hockey
        sci.crypt: cryptography
        sci.electronics: electronics
        sci.med: medicine
        sci.space: space
        soc.religion.christian: christianity
        talk.politics.guns: guns
        talk.politics.mideast: middle east
        talk.politics.misc: politics
        talk.religion.misc: religion
  - type: Set
    fields: 
      classes: 
        - atheism
        - computer graphics
        - microsoft windows
        - pc hardware
        - mac hardware
        - windows x
        - for sale
        - cars
        - motorcycles
        - baseball
        - hockey
        - cryptography
        - electronics
        - medicine
        - space
        - christianity
        - guns
        - middle east
        - politics
        - religion
task: tasks.classification.multi_class.topic_classification
templates: templates.classification.multi_class.all
[source]

Explanation about TaskCard¶

TaskCard delineates the phases in transforming the source dataset into 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 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 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’.

Explanation about LoadHF¶

Loads datasets from the HuggingFace Hub.

It supports loading with or without streaming, and it can filter datasets upon loading.
Args:
path: The path or identifier of the dataset on the HuggingFace Hub. name: An optional dataset name. data_dir: Optional directory to store downloaded data. split: Optional specification of which split to load. data_files: Optional specification of particular data files to load. revision: Optional. The revision of the dataset. Often the commit id. Use in case you want to set the dataset version. streaming: Bool indicating if streaming should be used. filtering_lambda: A lambda function for filtering the data after loading. num_proc: Optional integer to specify the number of processes to use for parallel dataset loading.

Example:
Loading glue’s mrpc dataset
load_hf = LoadHF(path='glue', name='mrpc')

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 Set¶

Adds specified fields to each instance in a given stream or all streams (default) If fields exist, updates them.

Args:

fields (Dict[str, object]): The fields to add to each instance.
Use ‘/’ to access inner fields

use_deepcopy (bool) : Deep copy the input value to avoid later modifications

Examples:
# Add a ‘classes’ field with a value of a list “positive” and “negative” to all streams Set(fields={“classes”: [“positive”,”negatives”]})

# Add a ‘start’ field under the ‘span’ field with a value of 0 to all streams Set(fields={“span/start”: 0}

# Add a ‘classes’ field with a value of a list “positive” and “negative” to ‘train’ stream Set(fields={“classes”: [“positive”,”negatives”], apply_to_stream=[“train”]})

# Add a ‘classes’ field on a given list, prevent modification of original list # from changing the instance. Set(fields={“classes”: alist}), use_deepcopy=True) # if now alist is modified, still the instances remain intact.

Explanation about Rename¶

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:
Rename(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}]

Rename(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}}]

Rename(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}}]

Rename(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 MapInstanceValues¶

A class used to map instance values into other values.

This class is a type of InstanceOperator, it maps values of instances in a stream using predefined mappers.

Attributes:

mappers (Dict[str, Dict[str, Any]]): The mappers to use for mapping instance values.
Keys are the names of the fields to undergo mapping, and values are dictionaries that define the mapping from old values to new values.

strict (bool): If True, the mapping is applied strictly. That means if a value
does not exist in the mapper, it will raise a KeyError. If False, values that are not present in the mapper are kept as they are.

process_every_value (bool): If True, all fields to be mapped should be lists, and the mapping
is to be applied to their individual elements. If False, mapping is only applied to a field containing a single value.

Examples:
MapInstanceValues(mappers={“a”: {“1”: “hi”, “2”: “bye”}}) replaces ‘1’ with ‘hi’ and ‘2’ with ‘bye’ in field ‘a’ in all instances of all streams: instance {“a”:”1”, “b”: 2} becomes {“a”:”hi”, “b”: 2}.

MapInstanceValues(mappers={“a”: {“1”: “hi”, “2”: “bye”}}, process_every_value=True) Assuming field ‘a’ is a list of values, potentially including “1”-s and “2”-s, this replaces each such “1” with “hi” and “2” – with “bye” in all instances of all streams: instance {“a”: [“1”, “2”], “b”: 2} becomes {“a”: [“hi”, “bye”], “b”: 2}.

MapInstanceValues(mappers={“a”: {“1”: “hi”, “2”: “bye”}}, strict=True) To ensure that all values of field ‘a’ are mapped in every instance, use strict=True. Input instance {“a”:”3”, “b”: 2} will raise an exception per the above call, because “3” is not a key in the mapper of “a”.

MapInstanceValues(mappers={“a”: {str([1,2,3,4]): ‘All’, str([]): ‘None’}}, strict=True) replaces a list [1,2,3,4] with the string ‘All’ and an empty list by string ‘None’. Note that mapped values are defined by their string representation, so mapped values must be converted to strings.

References: tasks.classification.multi_class.topic_classification, templates.classification.multi_class.all