📄 Sst2¶

The Stanford Sentiment Treebank consists of sentences from movie reviews and human annotations of their sentiment. The task is to predict the sentiment of a given sentence. It uses the two-way (positive/negative) class split, with only sentence-level labels… See the full description on the dataset page: https://huggingface.co/datasets/nyu-mll/glue.

Tags: annotations_creators:other, arxiv:1804.07461, flags:['coreference-nli', 'paraphrase-identification', 'qa-nli'], language:en, language_creators:other, license:other, multilinguality:monolingual, region:us, size_categories:10K<n<100K, source_datasets:original, task_categories:text-classification, task_ids:['acceptability-classification', 'natural-language-inference', 'semantic-similarity-scoring', 'sentiment-classification', 'text-scoring'], category:dataset

cards.sst2

TaskCard(
    loader=LoadHF(
        path="stanfordnlp/sst2",
    ),
    preprocess_steps=[
        "splitters.small_no_test",
        MapInstanceValues(
            mappers={
                "label": {
                    "0": "negative",
                    "1": "positive",
                },
            },
        ),
        Rename(
            field="sentence",
            to_field="text",
        ),
        Set(
            fields={
                "text_type": "sentence",
                "type_of_class": "sentiment",
                "classes": [
                    "negative",
                    "positive",
                ],
            },
        ),
    ],
    task="tasks.classification.multi_class",
    templates="templates.classification.multi_class.all",
)

[source]

from unitxt.loaders import LoadHF
from unitxt.operators import MapInstanceValues, Rename, Set

Explanation about TaskCard¶

TaskCard delineates the phases in transforming the source dataset into model input, and specifies the metrics for evaluation of model output.

Args:

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 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 (str, optional):
A lambda function for filtering the data after loading.

num_proc (int, optional):
Specifies 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 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.

Args:

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. Note that mapped values are defined by their string representation, so mapped values are converted to strings before being looked up in the mappers.

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}. Note that the value of "b" remained intact, since field-name "b" does not participate in the mappers, and that 1 was casted to "1" before looked up in the mapper of "a".

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".

Explanation about Set¶

Sets specified fields in each instance, in a given stream or all streams (default), with specified values. If fields exist, updates them, if do not exist – adds 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:
# Set a value of a list consisting of “positive” and “negative” do field “classes” to each and every instance of all streams Set(fields={"classes": ["positive","negatives"]})

# In each and every instance of all streams, field “span” is to become a dictionary containing a field “start”, in which the value 0 is to be set Set(fields={"span/start": 0}

# In all instances of stream “train” only, Set field “classes” to have the value of a list consisting of “positive” and “negative” Set(fields={"classes": ["positive","negatives"], apply_to_stream=["train"]})

# Set field “classes” to have the value of a given list, preventing 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.

References: templates.classification.multi_class.all, tasks.classification.multi_class, splitters.small_no_test