📄 Pol Latn¶
Tags: arxiv:2308.16884, language:['af', 'am', 'ar', 'az', 'as', 'bm', 'bn', 'bo', 'bg', 'ca', 'cs', 'ku', 'da', 'de', 'el', 'en', 'es', 'et', 'eu', 'fi', 'fr', 'ff', 'om', 'gu', 'gn', 'ht', 'ha', 'he', 'hi', 'hr', 'hu', 'hy', 'ig', 'id', 'it', 'is', 'jv', 'ja', 'ka', 'kn', 'kk', 'mn', 'km', 'rw', 'ky', 'ko', 'lo', 'ln', 'lt', 'lg', 'lv', 'ml', 'mr', 'mk', 'mt', 'mi', 'my', 'nl', 'no', 'ne', 'ny', 'or', 'pa', 'ps', 'fa', 'mg', 'pl', 'pt', 'ro', 'ru', 'sn', 'si', 'sl', 'sv', 'sk', 'sd', 'sw', 'ta', 'te', 'tg', 'tl', 'th', 'ti', 'tn', 'ts', 'tr', 'uk', 'ur', 'uz', 'vi', 'wo', 'xh', 'yo', 'zh', 'ms', 'zu'], license:cc-by-sa-4.0, region:us, size_categories:100K<n<1M, task_categories:['question-answering', 'zero-shot-classification', 'text-classification', 'multiple-choice']
Note
ID: cards.belebele.pol_latn | Type: TaskCard
{
"__description__": "Belebele is a multiple-choice machine reading comprehension (MRC) dataset spanning 122 language variants. This dataset enables the evaluation of mono- and multi-lingual models in high-, medium-, and low-resource languages. Each question has four multiple-choice answers and is linked to a short passage from the FLORES-200 dataset. The human annotation procedure was carefully curated to create questions that… See the full description on the dataset page: https://huggingface.co/datasets/facebook/belebele.",
"__tags__": {
"arxiv": "2308.16884",
"language": [
"af",
"am",
"ar",
"az",
"as",
"bm",
"bn",
"bo",
"bg",
"ca",
"cs",
"ku",
"da",
"de",
"el",
"en",
"es",
"et",
"eu",
"fi",
"fr",
"ff",
"om",
"gu",
"gn",
"ht",
"ha",
"he",
"hi",
"hr",
"hu",
"hy",
"ig",
"id",
"it",
"is",
"jv",
"ja",
"ka",
"kn",
"kk",
"mn",
"km",
"rw",
"ky",
"ko",
"lo",
"ln",
"lt",
"lg",
"lv",
"ml",
"mr",
"mk",
"mt",
"mi",
"my",
"nl",
"no",
"ne",
"ny",
"or",
"pa",
"ps",
"fa",
"mg",
"pl",
"pt",
"ro",
"ru",
"sn",
"si",
"sl",
"sv",
"sk",
"sd",
"sw",
"ta",
"te",
"tg",
"tl",
"th",
"ti",
"tn",
"ts",
"tr",
"uk",
"ur",
"uz",
"vi",
"wo",
"xh",
"yo",
"zh",
"ms",
"zu"
],
"license": "cc-by-sa-4.0",
"region": "us",
"size_categories": "100K<n<1M",
"task_categories": [
"question-answering",
"zero-shot-classification",
"text-classification",
"multiple-choice"
]
},
"__type__": "task_card",
"loader": {
"__type__": "load_hf",
"name": "default",
"path": "facebook/belebele",
"split": "pol_Latn"
},
"preprocess_steps": [
{
"__type__": "rename_splits",
"mapper": {
"pol_Latn": "test"
}
},
{
"__type__": "list_field_values",
"fields": [
"mc_answer1",
"mc_answer2",
"mc_answer3",
"mc_answer4"
],
"to_field": "choices"
},
{
"__type__": "rename_fields",
"field_to_field": {
"correct_answer_num": "answer",
"flores_passage": "context"
}
},
{
"__type__": "cast_fields",
"fields": {
"answer": "int"
}
},
{
"__type__": "add_constant",
"add": -1,
"field": "answer"
},
{
"__type__": "set",
"fields": {
"context_type": "passage"
}
}
],
"task": "tasks.qa.multiple_choice.with_context",
"templates": "templates.qa.multiple_choice.with_context.no_intro.all"
}
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 AddConstant¶
Adds a constant, being argument ‘add’, to the processed value.
- Args:
add: the constant to add.
Explanation about LoadHF¶
Loads datasets from the Huggingface Hub.
It supports loading with or without streaming, and 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. 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 ListFieldValues¶
Concatenates values of multiple fields into a list, and assigns it to a new field.
Explanation about CastFields¶
Casts specified fields to specified types.
- Args:
use_nested_query (bool): Whether to cast nested fields, expressed in dpath. Defaults to False. fields (Dict[str, str]): A dictionary mapping field names to the names of the types to cast the fields to.
e.g: “int”, “str”, “float”, “bool”. Basic names of types
defaults (Dict[str, object]): A dictionary mapping field names to default values for cases of casting failure. process_every_value (bool): If true, all fields involved must contain lists, and each value in the list is then casted. Defaults to False.
- Examples:
- CastFields(
fields={“a/d”: “float”, “b”: “int”}, failure_defaults={“a/d”: 0.0, “b”: 0}, process_every_value=True, use_nested_query=True
)
- would process the input instance: {“a”: {“d”: [“half”, “0.6”, 1, 12]}, “b”: [“2”]}
into {“a”: {“d”: [0.0, 0.6, 1.0, 12.0]}, “b”: [2]}
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 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}}]
References: templates.qa.multiple_choice.with_context.no_intro.all, tasks.qa.multiple_choice.with_context
Read more about catalog usage here.