📄 Single Turn Gpt4 Judgement¶

cards.mt_bench.response_assessment.pairwise_comparison.single_turn_gpt4_judgement

type: TaskCard
loader: 
  type: LoadFromHFSpace
  space_name: lmsys/mt-bench
  revision: a4b674c
  data_files: 
    questions: data/mt_bench/question.jsonl
    model_answer: data/mt_bench/model_answer/*.jsonl
    judgment: data/mt_bench/model_judgment/gpt-4_pair.jsonl
preprocess_steps: 
  - operators.mt_bench.pairwise_hf_space_processing_steps
  - type: FilterByCondition
    values: 
      turn: 1
    condition: eq
  - type: FilterByCondition
    values: 
      reference: None
    condition: eq
  - type: FilterByCondition
    values: 
      winner: 
        - model_1
        - tie
        - model_2
    condition: in
  - type: MapInstanceValues
    mappers: 
      winner: 
        model_1: choice_a
        model_2: choice_b
        tie: tie
  - type: Rename
    field_to_field: 
      model_input: question
      model_1_output: answer_a
      model_2_output: answer_b
      category: group
  - type: Copy
    field: question/0
    to_field: question
  - type: Copy
    field: answer_a/0
    to_field: answer_a
  - type: Copy
    field: answer_b/0
    to_field: answer_b
task: tasks.response_assessment.pairwise_comparison.single_turn
templates: 
  - templates.response_assessment.pairwise_comparison.mt_bench_single_turn_with_shuffling
[source]

Explanation about TaskCard¶

TaskCard delineates the phases in transforming the source dataset into model input, and specifies the metrics for evaluation of model output.

Attributes:
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 MapInstanceValues¶

A class used to map instance values into other values.

This class is a type of InstanceOperator, it maps values of instances in a stream using predefined mappers.

Attributes:

mappers (Dict[str, Dict[str, Any]]): The mappers to use for mapping instance values.
Keys are the names of the fields to undergo mapping, and values are dictionaries that define the mapping from old values to new values.

strict (bool): If True, the mapping is applied strictly. That means if a value
does not exist in the mapper, it will raise a KeyError. If False, values that are not present in the mapper are kept as they are.

process_every_value (bool): If True, all fields to be mapped should be lists, and the mapping
is to be applied to their individual elements. If False, mapping is only applied to a field containing a single value.

Examples:
MapInstanceValues(mappers={“a”: {“1”: “hi”, “2”: “bye”}}) replaces ‘1’ with ‘hi’ and ‘2’ with ‘bye’ in field ‘a’ in all instances of all streams: instance {“a”:”1”, “b”: 2} becomes {“a”:”hi”, “b”: 2}.

MapInstanceValues(mappers={“a”: {“1”: “hi”, “2”: “bye”}}, process_every_value=True) Assuming field ‘a’ is a list of values, potentially including “1”-s and “2”-s, this replaces each such “1” with “hi” and “2” – with “bye” in all instances of all streams: instance {“a”: [“1”, “2”], “b”: 2} becomes {“a”: [“hi”, “bye”], “b”: 2}.

MapInstanceValues(mappers={“a”: {“1”: “hi”, “2”: “bye”}}, strict=True) To ensure that all values of field ‘a’ are mapped in every instance, use strict=True. Input instance {“a”:”3”, “b”: 2} will raise an exception per the above call, because “3” is not a key in the mapper of “a”.

MapInstanceValues(mappers={“a”: {str([1,2,3,4]): ‘All’, str([]): ‘None’}}, strict=True) replaces a list [1,2,3,4] with the string ‘All’ and an empty list by string ‘None’. Note that mapped values are defined by their string representation, so mapped values must be converted to strings.

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” 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 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 LoadFromHFSpace¶

Used to load data from HuggingFace Spaces.

Loaders firstly tries to download all files specified in the ‘data_files’ parameter from the given space and then reads them as a HuggingFace Dataset.
Args:
space_name (str): Name of the HuggingFace Space to be accessed. data_files (str | Sequence[str] | Mapping[str, str | Sequence[str]]): Relative

paths to files within a given repository. If given as a mapping, paths should be values, while keys should represent the type of respective files (training, testing etc.).

path (str, optional): Absolute path to a directory where data should be downloaded. revision (str, optional): ID of a Git branch or commit to be used. By default, it is

set to None, thus data is downloaded from the main branch of the accessed repository.

use_token (bool, optional): Whether a token is used for authentication when accessing
the HuggingFace Space. If necessary, the token is read from the HuggingFace config folder.

token_env (str, optional): Key of an env variable which value will be used for
authentication when accessing the HuggingFace Space - if necessary.

Example:
Loading from a HuggingFace Space
loader = LoadFromHFSpace(
    space_name="lmsys/mt-bench",
    data_files={
        "train": [
            "data/mt_bench/model_answer/gpt-3.5-turbo.jsonl",
            "data/mt_bench/model_answer/gpt-4.jsonl",
        ],
        "test": "data/mt_bench/model_answer/tulu-30b.jsonl",
    },
)

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.response_assessment.pairwise_comparison.mt_bench_single_turn_with_shuffling, tasks.response_assessment.pairwise_comparison.single_turn, operators.mt_bench.pairwise_hf_space_processing_steps