📄 Turl Col Type¶

This TURL dataset is a large-scale dataset based on WikiTables corpus for the task of column type annotation. Given a table T and a set of semantic types L, the task is to annotate a column in T with l ∈ L so that all entities in the column have type l. Note that a column can have multiple types. See the full description on the dataset page: https://github.com/sunlab-osu/TURL

Tags: modality:table, urls:{'arxiv': 'https://arxiv.org/pdf/2006.14806'}, languages:['english'], category:dataset

cards.turl_col_type

TaskCard(
    loader=LoadHF(
        path="ibm/turl_table_col_type",
        data_classification_policy=[
            "public",
        ],
        streaming=True,
    ),
    task=Task(
        input_fields={
            "page_title": "str",
            "section_title": "str",
            "table_caption": "str",
            "table": "Table",
            "vocab": "List[str]",
            "colname": "str",
        },
        reference_fields={
            "annotations": "List[str]",
        },
        prediction_type="List[str]",
        metrics=[
            "metrics.f1_micro_multi_label",
            "metrics.accuracy",
            "metrics.f1_macro_multi_label",
        ],
        augmentable_inputs=[
            "page_title",
            "section_title",
            "table_caption",
            "table",
            "colname",
            "vocab",
        ],
    ),
    templates=[
        InputOutputTemplate(
            instruction="This is a column type annotation task. The goal of this task is to choose the correct types for one selected column of the given input table from the given candidate types. The Wikipedia page, section and table caption (if any) provide important information for choosing the correct column types.
                    Candidate Types: {vocab}

Output only the correct column types from the candidate list for the mentioned columns. Do not include any explanations, extra information, or introductory text—only the final answer.

Here are some input-output examples. Read the examples carefully to figure out the mapping. The output of the last example is not given, and your job is to figure out what it is.",
            input_format="
Column name: {colname}
Page Title: {page_title}
Section Title: {section_title}
Table caption: {table_caption}
Table:
{table}
Selected Column: {colname} ",
            output_format="{annotations}",
            postprocessors=[
                "processors.take_first_non_empty_line",
                "processors.lower_case",
                "processors.to_list_by_comma",
            ],
        ),
    ],
)

[source]

from unitxt.loaders import LoadHF
from unitxt.task import Task
from unitxt.templates import InputOutputTemplate

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 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 InputOutputTemplate¶

Generate field ‘source’ from fields designated as input, and fields ‘target’ and ‘references’ from fields designated as output, of the processed instance.

Args specify the formatting strings with which to glue together the input and reference fields of the processed instance into one string (‘source’ and ‘target’), and into a list of strings (‘references’).

Explanation about Task¶

Task packs the different instance fields into dictionaries by their roles in the task.

Args:

input_fields (Union[Dict[str, str], List[str]]):
Dictionary with string names of instance input fields and types of respective values. In case a list is passed, each type will be assumed to be Any.

reference_fields (Union[Dict[str, str], List[str]]):
Dictionary with string names of instance output fields and types of respective values. In case a list is passed, each type will be assumed to be Any.

metrics (List[str]):
List of names of metrics to be used in the task.

prediction_type (Optional[str]):
Need to be consistent with all used metrics. Defaults to None, which means that it will be set to Any.

defaults (Optional[Dict[str, Any]]):
An optional dictionary with default values for chosen input/output keys. Needs to be consistent with names and types provided in ‘input_fields’ and/or ‘output_fields’ arguments. Will not overwrite values if already provided in a given instance.

The output instance contains three fields:

“input_fields” whose value is a sub-dictionary of the input instance, consisting of all the fields listed in Arg ‘input_fields’.

“reference_fields” – for the fields listed in Arg “reference_fields”.

“metrics” – to contain the value of Arg ‘metrics’

References: processors.take_first_non_empty_line, metrics.f1_micro_multi_label, metrics.f1_macro_multi_label, processors.to_list_by_comma, processors.lower_case, metrics.accuracy