File size: 3,758 Bytes
e1acc28 15203ea 9caf36c ca10b7a ba7196e c7691bd ca10b7a c71da99 88480b4 1b07110 c71da99 ba31342 d2c1791 766a080 ca10b7a 3f0bfaf 15203ea ca10b7a 1a85f63 1af0fb9 c7691bd 9caf36c ca10b7a 9caf36c 15203ea 7ffcb05 1a85f63 ca10b7a 1bfeb06 ca10b7a ba31342 ca10b7a 15203ea ba7196e 1a85f63 1bfeb06 ca10b7a 15203ea 3f0bfaf ca10b7a f88c6c5 2f69840 1bfeb06 ca10b7a 1232c97 ba7196e 1a85f63 1bfeb06 15203ea f1c2ac1 ca10b7a 1bfeb06 ca10b7a b123d50 e1acc28 1bfeb06 ca10b7a 1bfeb06 ca10b7a e1acc28 1bfeb06 1af0fb9 1bfeb06 bd95969 e1acc28 bd95969 e1acc28 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 |
from typing import Dict, Iterable, List
import evaluate
from .api import __file__ as _
from .artifact import __file__ as _
from .augmentors import __file__ as _
from .benchmark import __file__ as _
from .blocks import __file__ as _
from .card import __file__ as _
from .catalog import __file__ as _
from .collections import __file__ as _
from .collections_operators import __file__ as _
from .dataclass import __file__ as _
from .dataset_utils import __file__ as _
from .deprecation_utils import __file__ as _
from .dialog_operators import __file__ as _
from .dict_utils import __file__ as _
from .error_utils import __file__ as _
from .eval_utils import __file__ as _
from .file_utils import __file__ as _
from .formats import __file__ as _
from .fusion import __file__ as _
from .generator_utils import __file__ as _
from .hf_utils import __file__ as _
from .hf_utils import verify_versions_compatibility
from .image_operators import __file__ as _
from .inference import __file__ as _
from .instructions import __file__ as _
from .llm_as_judge import __file__ as _
from .loaders import __file__ as _
from .logging_utils import __file__ as _
from .metric_utils import UNITXT_METRIC_SCHEMA
from .metric_utils import __file__ as _
from .metric_utils import _compute
from .metrics import __file__ as _
from .normalizers import __file__ as _
from .operator import __file__ as _
from .operators import __file__ as _
from .parsing_utils import __file__ as _
from .processors import __file__ as _
from .random_utils import __file__ as _
from .recipe import __file__ as _
from .register import __file__ as _
from .schema import __file__ as _
from .serializers import __file__ as _
from .settings_utils import __file__ as _
from .settings_utils import get_constants
from .span_lableing_operators import __file__ as _
from .split_utils import __file__ as _
from .splitters import __file__ as _
from .standard import __file__ as _
from .stream import __file__ as _
from .stream_operators import __file__ as _
from .string_operators import __file__ as _
from .struct_data_operators import __file__ as _
from .system_prompts import __file__ as _
from .task import __file__ as _
from .templates import __file__ as _
from .text_utils import __file__ as _
from .type_utils import __file__ as _
from .types import __file__ as _
from .utils import __file__ as _
from .utils import is_package_installed
from .validate import __file__ as _
from .version import __file__ as _
constants = get_constants()
class Metric(evaluate.Metric):
calc_confidence_intervals: bool = True
VERSION = constants.version
def _info(self):
return evaluate.MetricInfo(
description="_DESCRIPTION",
citation="_CITATION",
features=UNITXT_METRIC_SCHEMA,
codebase_urls=[constants.codebase_url],
reference_urls=[constants.website_url],
)
def _compute(
self,
predictions: List[str],
references: Iterable,
flatten: bool = False,
split_name: str = "all",
):
if is_package_installed("unitxt"):
verify_versions_compatibility("metric", self.VERSION)
from unitxt.metric_utils import _compute as _compute_installed
return _compute_installed(
predictions=predictions,
references=references,
flatten=flatten,
split_name=split_name,
calc_confidence_intervals=self.calc_confidence_intervals,
)
return _compute(
predictions=predictions,
references=references,
flatten=flatten,
split_name=split_name,
calc_confidence_intervals=self.calc_confidence_intervals,
)
|