📄 human_eval¶

Note

ID: cards.human_eval | Type: TaskCard

{
    "loader": {
        "path": "openai_humaneval",
        "split": "test",
        "type": "load_hf"
    },
    "preprocess_steps": [
        {
            "expression": "[t for t in re.findall(r\"assert.*?(?=\\n\\s*assert|$)\", test.replace(\"candidate\", entry_point), re.DOTALL)]",
            "imports_list": [
                "re"
            ],
            "to_field": "test_list",
            "type": "execute_expression"
        }
    ],
    "task": {
        "inputs": [
            "prompt"
        ],
        "metrics": [
            "metrics.bleu"
        ],
        "outputs": [
            "prompt",
            "canonical_solution",
            "test_list"
        ],
        "type": "form_task"
    },
    "templates": {
        "items": [
            {
                "input_format": "{prompt}\n",
                "output_format": "{prompt}\n{canonical_solution}",
                "type": "input_output_template"
            }
        ],
        "type": "templates_list"
    },
    "type": "task_card"
}

Explanation about TaskCard¶

TaskCard delineates the phases in transforming the source dataset into a 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 a 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 FormTask¶

FormTask packs the different instance fields into dictionaries by their roles in the task.

The output instance contains three fields:
“inputs” whose value is a sub-dictionary of the input instance, consisting of all the fields listed in Arg ‘inputs’. “outputs” – for the fields listed in Arg “outputs”. “metrics” – to contain the value of Arg ‘metrics’

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 output designated fields of the processed instance into one string (‘source’ and ‘target’), and into a list of strings (‘references’).

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”}

References: metrics.bleu