📄 ugnayan¶
Note
ID: cards.universal_ner.tl.ugnayan | Type: TaskCard
{
"loader": {
"name": "tl_ugnayan",
"path": "universalner/universal_ner",
"requirements_list": [
"conllu"
],
"type": "load_hf"
},
"preprocess_steps": [
{
"field_to_field": {
"ner_tags": "labels"
},
"type": "rename_fields"
},
{
"field": "labels",
"items_list": [
"O",
"B-PER",
"I-PER",
"B-ORG",
"I-ORG",
"B-LOC",
"I-LOC"
],
"process_every_value": true,
"type": "get_item_by_index"
},
{
"begin_labels": [
"B-PER",
"B-ORG",
"B-LOC"
],
"inside_labels": [
"I-PER",
"I-ORG",
"I-LOC"
],
"labels": [
"Person",
"Organization",
"Location"
],
"outside_label": "O",
"type": "iob_extractor"
},
{
"field_to_field": {
"spans/*/end": "spans_ends",
"spans/*/label": "labels",
"spans/*/start": "spans_starts"
},
"get_default": [],
"not_exist_ok": true,
"type": "copy_fields"
},
{
"fields": {
"class_type": "entity type",
"classes": [
"Person",
"Organization",
"Location"
],
"text_type": "text"
},
"type": "add_fields"
}
],
"task": "tasks.span_labeling.extraction",
"templates": "templates.span_labeling.extraction.all",
"type": "task_card"
}
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 GetItemByIndex¶
Get from the item list by the index in the field.
Explanation about CopyFields¶
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 CopyField(field_to_field={“a”: “b”} would yield {“a”: 2, “b”: 2}, and when processed by CopyField(field_to_field={“a”: “c”} would yield {“a”: 2, “b”: 3, “c”: 2}
with field names containing / , we can also copy inside the field: CopyFields(field_to_field={“a/0”: “a”}) would process instance {“a”: [1, 3]} into {“a”: 1}
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}}]
Explanation about AddFields¶
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 AddFields(fields={“classes”: [“positive”,”negatives”]})
# Add a ‘start’ field under the ‘span’ field with a value of 0 to all streams AddFields(fields={“span/start”: 0}
# Add a ‘classes’ field with a value of a list “positive” and “negative” to ‘train’ stream AddFields(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. AddFields(fields={“classes”: alist}), use_deepcopy=True) # if now alist is modified, still the instances remain intact.
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.
- Attributes:
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: ```python 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)
References: templates.span_labeling.extraction.all, tasks.span_labeling.extraction
Read more about catalog usage here.