📄 APIGen Function-Calling Datasets¶

This dataset contains 60,000 data points collected by APIGen, an automated data generation pipeline designed to produce verifiable high-quality datasets for function-calling applications. Each data point in the dataset is verified through three hierarchical stages: format checking, actual function executions, and semantic verification, ensuring its reliability and correctness.

Tags: annotations_creators:expert-generated, language:['en'], license:hf-gated, size_categories:['10K<n<100K'], task_categories:['question-answering', 'reading-comprehension', 'tool-calling', 'multi-turn-tool-calling'], task_ids:['tool-calling', 'multi-turn-tool-calling', 'reading-comprehension'], category:dataset

cards.xlam_function_calling_60k

TaskCard(
    loader=LoadHF(
        path="Salesforce/xlam-function-calling-60k",
        split="train",
        data_classification_policy=[
            "public",
        ],
    ),
    preprocess_steps=[
        RenameSplits(
            mapper={
                "train": "test",
            },
        ),
        Set(
            fields={
                "dialog": [
                    {
                    "role": "user",
                    },
                ],
            },
            use_deepcopy=True,
        ),
        Copy(
            field="query",
            to_field="dialog/0/content",
        ),
        LoadJson(
            field="answers",
            to_field="reference_calls",
        ),
        LoadJson(
            field="tools",
        ),
        Move(
            field="tools/*/parameters",
            to_field="properties",
        ),
        Copy(
            field="properties",
            to_field="tools/*/parameters/properties",
            set_every_value=True,
        ),
        Set(
            fields={
                "tools/*/parameters/type": "object",
            },
            use_deepcopy=True,
        ),
        ExecuteExpression(
            to_field="required",
            expression="[[p for p, c in tool['parameters']['properties'].items() if 'optional' not in c['type'].lower()] for tool in tools]",
        ),
        Copy(
            field="required",
            to_field="tools/*/parameters/required",
            set_every_value=True,
        ),
        FixJsonSchemaOfParameterTypes(
            main_field="tools",
        ),
    ],
    task="tasks.tool_calling.multi_turn",
    templates=[
        "templates.tool_calling.multi_turn",
    ],
    __title__="APIGen Function-Calling Datasets",
)

[source]

from unitxt.loaders import LoadHF
from unitxt.operators import Copy, ExecuteExpression, FixJsonSchemaOfParameterTypes, Move, Set
from unitxt.splitters import RenameSplits
from unitxt.struct_data_operators import LoadJson

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 ExecuteExpression¶

Compute an expression, specified as a string to be eval-uated, over the instance’s fields, and store the result in field to_field.

Raises an error if a field mentioned in the query is missing from the instance.

Args:
expression (str): an expression to be evaluated over the fields of the instance to_field (str): the field where the result is to be stored into imports_list (List[str]): names of imports needed for the eval of the query (e.g. ‘re’, ‘json’)

Examples:
When instance {“a”: 2, “b”: 3} is process-ed by operator ExecuteExpression(expression=”a+b”, to_field = “c”) the result is {“a”: 2, “b”: 3, “c”: 5}

When instance {“a”: “hello”, “b”: “world”} is process-ed by operator ExecuteExpression(expression = “a+’ ‘+b”, to_field = “c”) the result is {“a”: “hello”, “b”: “world”, “c”: “hello world”}

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 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: templates.tool_calling.multi_turn, tasks.tool_calling.multi_turn