📄 Tablebench Data Analysis

This TableBench dataset is a Comprehensive and Complex Benchmark for Table Question Answering. For more details, refer to https://tablebench.github.io/

Tags: modality:table, urls:{'arxiv': 'https://www.arxiv.org/pdf/2408.09174'}, languages:['english'], category:dataset

cards.tablebench_data_analysis

TaskCard(
    loader=LoadHF(
        path="Multilingual-Multimodal-NLP/TableBench",
        revision="90593ad8",
        data_classification_policy=[
            "public",
        ],
        splits=[
            "test",
        ],
        filtering_lambda="lambda x: x['instruction_type'] == 'DP' and x['qtype'] in ['DataAnalysis']",
    ),
    preprocess_steps=[
        SplitRandomMix(
            mix={
                "train": "test[20%]",
                "validation": "test[20%]",
                "test": "test[60%]",
            },
        ),
        LoadJson(
            field="table",
        ),
        Rename(
            field_to_field={
                "table/columns": "table/header",
                "table/data": "table/rows",
            },
        ),
        Set(
            fields={
                "context_type": "Table",
            },
        ),
        Rename(
            field_to_field={
                "table": "context",
                "answer": "answers",
            },
        ),
        RemoveFields(
            fields=[
                "instruction",
            ],
        ),
    ],
    task=Task(
        input_fields={
            "context": "Table",
            "context_type": "str",
            "question": "str",
            "answer_formatter": "str",
        },
        reference_fields={
            "answers": "str",
        },
        prediction_type="str",
        metrics=[
            "metrics.rouge",
        ],
        augmentable_inputs=[
            "context",
            "question",
        ],
    ),
    templates=[
        InputOutputTemplate(
            instruction="You are a table analyst. Your task is to answer questions based on the table content. {answer_formatter}
Output only the final answer without any explanations, extra information, or introductory text.
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="{context_type}: {context} 
Question: {question}",
            target_prefix="Final Answer: ",
            output_format="{answers}",
            postprocessors=[
                "processors.take_first_non_empty_line",
                "processors.lower_case",
                "processors.remove_punctuations",
                "processors.remove_articles",
                "processors.fix_whitespace",
            ],
        ),
    ],
)
[source]

from unitxt.loaders import LoadHF
from unitxt.operators import RemoveFields, Rename, Set
from unitxt.splitters import SplitRandomMix
from unitxt.struct_data_operators import LoadJson
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 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:

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

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

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

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 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 RemoveFields

Remove specified fields from each instance in a stream.

Args:

fields (List[str]): The fields to remove from each instance.

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 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 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.

References: processors.take_first_non_empty_line, processors.remove_punctuations, processors.remove_articles, processors.fix_whitespace, processors.lower_case, metrics.rouge

Read more about catalog usage here.