📄 Berkeley Function Calling Leaderboard (Multi Turn Setup) - Live Irrelevance V3¶

The Berkeley function calling leaderboard is a live leaderboard to evaluate the ability of different LLMs to call functions (also referred to as tools). We built this dataset from our learnings to be representative of most users’ function calling use-cases, for example, in agents, as a part of enterprise workflows, etc. To this end, our evaluation dataset spans diverse categories, and across multiple languages.

Tags: annotations_creators:expert-generated, language:['en'], license:apache-2.0, 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.bfcl.multi_turn.live_irrelevance_v3

TaskCard(
    loader=LoadJsonFile(
        files={
            "test": "https://raw.githubusercontent.com/ShishirPatil/gorilla/70b6a4a2144597b1f99d1f4d3185d35d7ee532a4/berkeley-function-call-leaderboard/data/BFCL_v3_live_irrelevance.json",
        },
        lines=True,
        data_classification_policy=[
            "public",
        ],
    ),
    preprocess_steps=[
        Copy(
            field="question/*/0",
            to_field="dialog",
        ),
        Copy(
            field="function",
            to_field="tools",
        ),
        "operators.fix_json_schema",
        Set(
            fields={
                "reference_calls": [],
            },
        ),
    ],
    task="tasks.tool_calling.multi_turn",
    templates=[
        "templates.tool_calling.multi_turn",
    ],
    __title__="Berkeley Function Calling Leaderboard (Multi Turn Setup) - Live Irrelevance V3",
)

[source]

from unitxt.loaders import LoadJsonFile
from unitxt.operators import Copy, Set

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 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 LoadJsonFile¶

Loads data from JSON files.

Supports streaming and can handle large files by loading them in chunks.
Args:
files (Dict[str, str]): A dictionary mapping names to file paths. chunksize : Size of the chunks to load at a time. loader_limit: Optional integer to specify a limit on the number of records to load. streaming: Bool indicating if streaming should be used. lines: Bool indicate if it is json lines file structure. Otherwise, assumes a single json object in the file. data_field: optional field within the json object, that contains the list of instances.

Example:
Loading json lines
load_csv = LoadJsonFile(files={'train': 'path/to/train.jsonl'}, line=True, chunksize=100)

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, operators.fix_json_schema