📄 Financial Tweets¶
Tags: annotations_creators:other, flags:['finance', 'hedgefunds', 'markets', 'quant', 'stocks', 'twitter', 'wallstreet'], language:en, language_creators:other, license:mit, multilinguality:monolingual, region:us, size_categories:10K<n<100K, source_datasets:original, task_categories:text-classification, task_ids:multi-class-classification
Note
ID: cards.financial_tweets | Type: TaskCard
{
"__description__": "Dataset Description\nThe Twitter Financial News dataset is an English-language dataset containing an annotated corpus of finance-related tweets. This dataset is used to classify finance-related tweets for their topic.\nThe dataset holds 21,107 documents annotated with 20 labels:\ntopics = {\n\"LABEL_0\": \"Analyst Update\",\n\"LABEL_1\": \"Fed | Central Banks\",\n\"LABEL_2\": \"Company | Product News\",\n\"LABEL_3\": \"Treasuries | Corporate Debt\",\n\"LABEL_4\": \"Dividend\"… See the full description on the dataset page: https://huggingface.co/datasets/zeroshot/twitter-financial-news-topic.",
"__tags__": {
"annotations_creators": "other",
"flags": [
"finance",
"hedgefunds",
"markets",
"quant",
"stocks",
"twitter",
"wallstreet"
],
"language": "en",
"language_creators": "other",
"license": "mit",
"multilinguality": "monolingual",
"region": "us",
"size_categories": "10K<n<100K",
"source_datasets": "original",
"task_categories": "text-classification",
"task_ids": "multi-class-classification"
},
"__type__": "task_card",
"loader": {
"__type__": "load_hf",
"path": "zeroshot/twitter-financial-news-topic"
},
"preprocess_steps": [
{
"__type__": "shuffle",
"page_size": 9223372036854775807
},
{
"__type__": "split_random_mix",
"mix": {
"test": "validation",
"train": "train[85%]",
"validation": "train[15%]"
}
},
{
"__type__": "map_instance_values",
"mappers": {
"label": {
"0": "analyst update",
"1": "fed and central banks",
"10": "gold, metals and materials",
"11": "initial public offering",
"12": "legal and regulation",
"13": "mergers, acquisitions and investments",
"14": "macro",
"15": "markets",
"16": "politics",
"17": "personnel change",
"18": "stock commentary",
"19": "stock movement",
"2": "company and product news",
"3": "treasuries and corporate debt",
"4": "dividend",
"5": "earnings",
"6": "energy and oil",
"7": "financials",
"8": "currencies",
"9": "general News and opinion"
}
}
},
{
"__type__": "set",
"fields": {
"classes": [
"analyst update",
"fed and central banks",
"company and product news",
"treasuries and corporate debt",
"dividend",
"earnings",
"energy and oil",
"financials",
"currencies",
"general News and opinion",
"gold, metals and materials",
"initial public offering",
"legal and regulation",
"mergers, acquisitions and investments",
"macro",
"markets",
"politics",
"personnel change",
"stock commentary",
"stock movement"
],
"text_type": "tweet"
}
}
],
"task": "tasks.classification.multi_class.topic_classification",
"templates": "templates.classification.multi_class.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 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 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 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, str]]): The mappers to use for mapping instance values.
Keys are the names of the fields to be mapped, 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.
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 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.
References: tasks.classification.multi_class.topic_classification, templates.classification.multi_class.all
Read more about catalog usage here.