📄 Talbanken¶

Universal Named Entity Recognition (UNER) aims to fill a gap in multilingual NLP: high quality NER datasets in many languages with a shared tagset. UNER is modeled after the Universal Dependencies project, in that it is intended to be a large community annotation effort with language-universal guidelines. Further, we use the same text corpora as Universal Dependencies… See the full description on the dataset page: https://huggingface.co/datasets/universalner/universal_ner

Tags: arxiv:2311.09122, language:['ceb', 'da', 'de', 'en', 'hr', 'pt', 'ru', 'sk', 'sr', 'sv', 'tl', 'zh'], license:cc-by-sa-4.0, region:us, task_categories:token-classification, category:dataset

cards.universal_ner.sv.talbanken

type: TaskCard
loader: 
  type: LoadHF
  path: universalner/universal_ner
  name: sv_talbanken
  requirements_list: 
    - conllu
preprocess_steps: 
  - type: Shuffle
    page_size: 9223372036854775807
  - type: Rename
    field_to_field: 
      ner_tags: labels
  - type: GetItemByIndex
    field: labels
    items_list: 
      - O
      - B-PER
      - I-PER
      - B-ORG
      - I-ORG
      - B-LOC
      - I-LOC
    process_every_value: True
  - type: IobExtractor
    labels: 
      - Person
      - Organization
      - Location
    begin_labels: 
      - B-PER
      - B-ORG
      - B-LOC
    inside_labels: 
      - I-PER
      - I-ORG
      - I-LOC
    outside_label: O
  - type: Copy
    field_to_field: 
      spans/*/start: spans_starts
      spans/*/end: spans_ends
      spans/*/label: labels
    get_default: []
    not_exist_ok: True
  - type: Set
    fields: 
      entity_types: 
        - Person
        - Organization
        - Location
task: tasks.span_labeling.extraction
templates: templates.span_labeling.extraction.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.

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.

default_template:
a default template for tasks with very specific task dataset specific template

Explanation about Copy¶

Copies values from specified fields to specified fields.

Args (of parent class):
field_to_field (Union[List[List], Dict[str, str]]): A list of lists, where each sublist contains the source field and the destination field, or a dictionary mapping source fields to destination fields.

Examples:
An input instance {“a”: 2, “b”: 3}, when processed by Copy(field_to_field={"a": "b"}) would yield {“a”: 2, “b”: 2}, and when processed by Copy(field_to_field={"a": "c"}) would yield {“a”: 2, “b”: 3, “c”: 2}

with field names containing / , we can also copy inside the field: Copy(field="a/0",to_field="a") would process instance {“a”: [1, 3]} into {“a”: 1}

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 IobExtractor¶

A class designed to extract entities from sequences of text using the Inside-Outside-Beginning (IOB) tagging convention. It identifies entities based on IOB tags and categorizes them into predefined labels such as Person, Organization, and Location.

Args:

labels (List[str]):
A list of entity type labels, e.g., [“Person”, “Organization”, “Location”].

begin_labels (List[str]):
A list of labels indicating the beginning of an entity, e.g., [“B-PER”, “B-ORG”, “B-LOC”].

inside_labels (List[str]):
A list of labels indicating the continuation of an entity, e.g., [“I-PER”, “I-ORG”, “I-LOC”].

outside_label (str):
The label indicating tokens outside of any entity, typically “O”.

The extraction process identifies spans of text corresponding to entities and labels them according to their entity type. Each span is annotated with a start and end character offset, the entity text, and the corresponding label.

Example of instantiation and usage:
operator = IobExtractor(
    labels=["Person", "Organization", "Location"],
    begin_labels=["B-PER", "B-ORG", "B-LOC"],
    inside_labels=["I-PER", "I-ORG", "I-LOC"],
    outside_label="O",
)

instance = {
    "labels": ["B-PER", "I-PER", "O", "B-ORG", "I-ORG"],
    "tokens": ["John", "Doe", "works", "at", "OpenAI"]
}
processed_instance = operator.process(instance)
print(processed_instance["spans"])
# Output: [{'start': 0, 'end': 8, 'text': 'John Doe', 'label': 'Person'}, ...]
For more details on the IOB tagging convention, see: https://en.wikipedia.org/wiki/Inside-outside-beginning_(tagging)

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 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 GetItemByIndex¶

Get from the item list by the index in the field.

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')

References: templates.span_labeling.extraction.all, tasks.span_labeling.extraction