📄 Scigen¶

SciGen is a dataset for the task of reasoning-aware data-to-text generation. It consists of tables from scientific articles(mostly containing numerical values) and their corresponding text descriptions.

Tags: modality:table, urls:{'arxiv': 'https://arxiv.org/pdf/2104.08296'}, languages:['english'], category:dataset

cards.scigen

TaskCard(
    loader=LoadHF(
        path="kasnerz/scigen",
        data_classification_policy=[
            "public",
        ],
    ),
    preprocess_steps=[
        FilterByCondition(
            values={
                "table_content_values": "[]",
            },
            condition="ne",
        ),
        ConstructTableFromRowsCols(
            fields=[
                "table_column_names",
                "table_content_values",
            ],
            to_field="input_a",
        ),
        Rename(
            field_to_field={
                "table_caption": "input_b",
                "text": "output",
            },
        ),
        Set(
            fields={
                "type_of_input_a": "table",
                "type_of_input_b": "caption",
                "type_of_output": "text description",
            },
        ),
    ],
    task="tasks.generation.from_pair",
    templates=[
        "templates.generation.from_pair.default[postprocessors=[processors.lower_case]]",
    ],
)

[source]

from unitxt.loaders import LoadHF
from unitxt.operators import FilterByCondition, Rename, Set
from unitxt.struct_data_operators import ConstructTableFromRowsCols

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 FilterByCondition¶

Filters a stream, yielding only instances in which the values in required fields follow the required condition operator.

Raises an error if a required field name is missing from the input instance.

Args:
values (Dict[str, Any]): Field names and respective Values that instances must match according the condition, to be included in the output.

condition: the name of the desired condition operator between the specified (sub) field’s value and the provided constant value. Supported conditions are (“gt”, “ge”, “lt”, “le”, “ne”, “eq”, “in”,”not in”)

error_on_filtered_all (bool, optional): If True, raises an error if all instances are filtered out. Defaults to True.

Examples:

FilterByCondition(values = {"a":4}, condition = "gt") will yield only instances where field "a" contains a value > 4

FilterByCondition(values = {"a":4}, condition = "le") will yield only instances where "a"<=4

FilterByCondition(values = {"a":[4,8]}, condition = "in") will yield only instances where "a" is 4 or 8

FilterByCondition(values = {"a":[4,8]}, condition = "not in") will yield only instances where "a" is different from 4 or 8

FilterByCondition(values = {"a/b":[4,8]}, condition = "not in") will yield only instances where "a" is a dict in which key "b" is mapped to a value that is neither 4 nor 8

FilterByCondition(values = {"a[2]":4}, condition = "le") will yield only instances where “a” is a list whose 3-rd element is <= 4

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 ConstructTableFromRowsCols¶

Maps column and row field into single table field encompassing both header and rows.

field[0] = header string as List field[1] = rows string as List[List] field[2] = table caption string(optional)

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}}]

References: templates.generation.from_pair.default, tasks.generation.from_pair, processors.lower_case