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1856027 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 | # -*- coding: utf-8 -*-
# Copyright 2024 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
"""Model-based Pointwise Metric."""
from typing import Union
from vertexai.evaluation import constants
from vertexai.evaluation.metrics import _base
from vertexai.evaluation.metrics import (
metric_prompt_template as metric_prompt_template_base,
)
class PointwiseMetric(_base._ModelBasedMetric): # pylint: disable=protected-access
"""A Model-based Pointwise Metric.
A model-based evaluation metric that evaluate a single generative model's
response.
For more details on when to use model-based pointwise metrics, see
[Evaluation methods and metrics](https://cloud.google.com/vertex-ai/generative-ai/docs/models/determine-eval).
Usage Examples:
```
candidate_model = GenerativeModel("gemini-1.5-pro")
eval_dataset = pd.DataFrame({
"prompt" : [...],
})
fluency_metric = PointwiseMetric(
metric="fluency",
metric_prompt_template=MetricPromptTemplateExamples.get_prompt_template('fluency'),
)
pointwise_eval_task = EvalTask(
dataset=eval_dataset,
metrics=[
fluency_metric,
MetricPromptTemplateExamples.Pointwise.GROUNDEDNESS,
],
)
pointwise_result = pointwise_eval_task.evaluate(
model=candidate_model,
)
```
"""
def __init__(
self,
*,
metric: str,
metric_prompt_template: Union[
metric_prompt_template_base.PointwiseMetricPromptTemplate, str
],
):
"""Initializes a pointwise evaluation metric.
Args:
metric: The pointwise evaluation metric name.
metric_prompt_template: Pointwise metric prompt template for performing
the model-based evaluation. A freeform string is also accepted.
"""
super().__init__(
metric_prompt_template=metric_prompt_template,
metric=metric,
)
class Comet(_base._TranslationMetric): # pylint: disable=protected-access
"""A COMET metric.
Evaluates a score for the given instance using
https://huggingface.co/Unbabel/wmt22-comet-da
"""
_metric_name = constants.Metric.COMET
def __init__(
self,
*,
version: str = "COMET_22_SRC_REF",
source_language: str = None,
target_language: str = None,
):
"""Initializes the COMET metric.
Args:
version: The COMET version to use for evaluation eg.
"COMET_22_SRC_REF".
source_language: Optional. The source language of the translation.
target_language: Optional. The target language of the translation.
"""
super().__init__(
name=Comet._metric_name,
version=version,
source_language=source_language,
target_language=target_language,
)
class MetricX(_base._TranslationMetric): # pylint: disable=protected-access
"""A MetricX metric.
Evaluates a score for the given instance using
https://github.com/google-research/metricx
"""
_metric_name = constants.Metric.METRICX
def __init__(
self,
*,
version: str = "METRICX_24_SRC_REF",
source_language: str = None,
target_language: str = None,
):
"""Initializes the MetricX metric.
Args:
version: The MetricX version to use for evaluation. Can be one of
"METRICX_24_SRC_REF", "METRICX_24_SRC", or "METRICX_24_REF".
source_language: Optional. The source language of the translation.
target_language: Optional. The target language of the translation.
"""
super().__init__(
name=MetricX._metric_name,
version=version,
source_language=source_language,
target_language=target_language,
)
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