📄 Banking77¶
Dataset composed of online banking queries annotated with their corresponding intents. BANKING77 dataset provides a very fine-grained set of intents in a banking domain. It comprises 13,083 customer service queries labeled with 77 intents. It focuses on fine-grained single-domain intent detection. See the full description on the dataset page: https://huggingface.co/datasets/PolyAI/banking77
Tags: annotations_creators:expert-generated, arxiv:2003.04807, language:en, language_creators:expert-generated, license:cc-by-4.0, multilinguality:monolingual, region:us, size_categories:10K<n<100K, source_datasets:original, task_categories:text-classification, task_ids:['intent-classification', 'multi-class-classification'], category:dataset
cards.banking77
TaskCard(
loader=LoadHF(
path="PolyAI/banking77",
),
preprocess_steps=[
Shuffle(
page_size=9223372036854775807,
),
SplitRandomMix(
mix={
"train": "train[85%]",
"validation": "train[15%]",
"test": "test",
},
),
MapInstanceValues(
mappers={
"label": {
"0": "activate my card",
"1": "age limit",
"2": "apple pay or google pay",
"3": "atm support",
"4": "automatic top up",
"5": "balance not updated after bank transfer",
"6": "balance not updated after cheque or cash deposit",
"7": "beneficiary not allowed",
"8": "cancel transfer",
"9": "card about to expire",
"10": "card acceptance",
"11": "card arrival",
"12": "card delivery estimate",
"13": "card linking",
"14": "card not working",
"15": "card payment fee charged",
"16": "card payment not recognised",
"17": "card payment wrong exchange rate",
"18": "card swallowed",
"19": "cash withdrawal charge",
"20": "cash withdrawal not recognised",
"21": "change pin",
"22": "compromised card",
"23": "contactless not working",
"24": "country support",
"25": "declined card payment",
"26": "declined cash withdrawal",
"27": "declined transfer",
"28": "direct debit payment not recognised",
"29": "disposable card limits",
"30": "edit personal details",
"31": "exchange charge",
"32": "exchange rate",
"33": "exchange via app",
"34": "extra charge on statement",
"35": "failed transfer",
"36": "fiat currency support",
"37": "get disposable virtual card",
"38": "get physical card",
"39": "getting spare card",
"40": "getting virtual card",
"41": "lost or stolen card",
"42": "lost or stolen phone",
"43": "order physical card",
"44": "passcode forgotten",
"45": "pending card payment",
"46": "pending cash withdrawal",
"47": "pending top up",
"48": "pending transfer",
"49": "pin blocked",
"50": "receiving money",
"51": "Refund not showing up",
"52": "request refund",
"53": "reverted card payment?",
"54": "supported cards and currencies",
"55": "terminate account",
"56": "top up by bank transfer charge",
"57": "top up by card charge",
"58": "top up by cash or cheque",
"59": "top up failed",
"60": "top up limits",
"61": "top up reverted",
"62": "topping up by card",
"63": "transaction charged twice",
"64": "transfer fee charged",
"65": "transfer into account",
"66": "transfer not received by recipient",
"67": "transfer timing",
"68": "unable to verify identity",
"69": "verify my identity",
"70": "verify source of funds",
"71": "verify top up",
"72": "virtual card not working",
"73": "visa or mastercard",
"74": "why verify identity",
"75": "wrong amount of cash received",
"76": "wrong exchange rate for cash withdrawal",
},
},
),
Set(
fields={
"classes": [
"activate my card",
"age limit",
"apple pay or google pay",
"atm support",
"automatic top up",
"balance not updated after bank transfer",
"balance not updated after cheque or cash deposit",
"beneficiary not allowed",
"cancel transfer",
"card about to expire",
"card acceptance",
"card arrival",
"card delivery estimate",
"card linking",
"card not working",
"card payment fee charged",
"card payment not recognised",
"card payment wrong exchange rate",
"card swallowed",
"cash withdrawal charge",
"cash withdrawal not recognised",
"change pin",
"compromised card",
"contactless not working",
"country support",
"declined card payment",
"declined cash withdrawal",
"declined transfer",
"direct debit payment not recognised",
"disposable card limits",
"edit personal details",
"exchange charge",
"exchange rate",
"exchange via app",
"extra charge on statement",
"failed transfer",
"fiat currency support",
"get disposable virtual card",
"get physical card",
"getting spare card",
"getting virtual card",
"lost or stolen card",
"lost or stolen phone",
"order physical card",
"passcode forgotten",
"pending card payment",
"pending cash withdrawal",
"pending top up",
"pending transfer",
"pin blocked",
"receiving money",
"Refund not showing up",
"request refund",
"reverted card payment?",
"supported cards and currencies",
"terminate account",
"top up by bank transfer charge",
"top up by card charge",
"top up by cash or cheque",
"top up failed",
"top up limits",
"top up reverted",
"topping up by card",
"transaction charged twice",
"transfer fee charged",
"transfer into account",
"transfer not received by recipient",
"transfer timing",
"unable to verify identity",
"verify my identity",
"verify source of funds",
"verify top up",
"virtual card not working",
"visa or mastercard",
"why verify identity",
"wrong amount of cash received",
"wrong exchange rate for cash withdrawal",
],
"text_type": "utterance",
"type_of_class": "intent",
},
),
],
task="tasks.classification.multi_class",
templates="templates.classification.multi_class.all",
)
[source]from unitxt.loaders import LoadHF
from unitxt.operators import MapInstanceValues, Set, Shuffle
from unitxt.splitters import SplitRandomMix
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 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 that1was 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, usestrict=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 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. When you provide a list of data_files to Hugging Face’s load_dataset function without explicitly specifying the split argument, these files are automatically placed into the train split.
- 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 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.
Explanation about Shuffle¶
Shuffles the order of instances in each page of a stream.
- Args (of superclass):
page_size (int): The size of each page in the stream. Defaults to 1000.
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.classification.multi_class.all, tasks.classification.multi_class
Read more about catalog usage here.