from __future__ import annotations
from typing import List, Optional, Tuple
from langcheck.metrics._validation import (validate_parameters_answer_relevance,
validate_parameters_reference_free)
from langcheck.metrics.de._translation import Translate
from langcheck.metrics.de.reference_based_text_quality import \
semantic_similarity
from langcheck.metrics.en.reference_free_text_quality import \
flesch_kincaid_grade as en_flesch_kincaid_grade
from langcheck.metrics.en.reference_free_text_quality import \
fluency as en_fluency
from langcheck.metrics.eval_clients import EvalClient
from langcheck.metrics.metric_value import MetricValue
from langcheck.metrics.scorer.detoxify_models import DetoxifyScorer
from langcheck.metrics.scorer.hf_models import \
AutoModelForSequenceClassificationScorer
from langcheck.stats import compute_stats
from langcheck.utils.progess_bar import tqdm_wrapper
from ..prompts._utils import get_template
_translation_model_path = 'Helsinki-NLP/opus-mt-de-en'
LANG = 'de'
[docs]def sentiment(
generated_outputs: List[str] | str,
prompts: Optional[List[str] | str] = None,
eval_model: str | EvalClient = 'local',
local_overflow_strategy: str = 'truncate',
) -> MetricValue[Optional[float]]:
'''Calculates the sentiment scores of generated outputs. This metric takes
on float values between [0, 1], where 0 is negative sentiment and 1 is
positive sentiment. (NOTE: when using an EvalClient, the sentiment scores
are either 0.0 (negative), 0.5 (neutral), or 1.0 (positive). The score may
also be `None` if it could not be computed.)
We currently support two evaluation model types:
1. The 'local' type, where the twitter-xlm-roberta-base-sentiment-finetunned
model is downloaded from HuggingFace and run locally. This is the default
model type and there is no setup needed to run this.
2. The EvalClient type, where you can use an EvalClient typically
implemented with an LLM. The implementation details are explained in each of
the concrete EvalClient classes.
Ref:
https://huggingface.co/citizenlab/twitter-xlm-roberta-base-sentiment-finetunned
Args:
generated_outputs: The model generated output(s) to evaluate
prompts: The prompts used to generate the output(s). Prompts are
optional metadata and not used to calculate the metric.
eval_model: The type of model to use ('local' or the EvalClient instance
used for the evaluation). default 'local'
local_overflow_strategy: The strategy to handle the inputs that are too
long for the local model. The supported strategies are 'nullify',
'truncate', and 'raise'. If 'nullify', the outputs that are too long
will be assigned a score of None. If 'truncate', the outputs that
are too long will be truncated. If 'raise', an error will be raised
when the outputs are too long. The default value is 'nullify'.
Returns:
An :class:`~langcheck.metrics.metric_value.MetricValue` object
'''
generated_outputs, prompts = validate_parameters_reference_free(
generated_outputs, prompts)
if eval_model == 'local':
scores = _sentiment_local(generated_outputs, local_overflow_strategy)
explanations = None
else: # EvalClient
assert isinstance(
eval_model, EvalClient
), 'An EvalClient must be provided for non-local model types.'
scores, explanations = _sentiment_eval_client(generated_outputs,
eval_model)
return MetricValue(metric_name='sentiment',
prompts=prompts,
generated_outputs=generated_outputs,
reference_outputs=None,
sources=None,
explanations=explanations,
metric_values=scores,
language=LANG)
def _sentiment_local(generated_outputs: List[str],
overflow_strategy: str) -> List[Optional[float]]:
'''Calculates the sentiment scores of generated outputs using the
twitter-xlm-roberta-base-sentiment-finetunned model. This metric takes on
float values between [0, 1], where 0 is negative sentiment and 1 is positive
sentiment.
Ref:
https://huggingface.co/citizenlab/twitter-xlm-roberta-base-sentiment-finetunned
Args:
generated_outputs: A list of model generated outputs to evaluate
overflow_strategy: The strategy to handle inputs that are longer than
the maximum input length of the model.
Returns:
A list of scores
'''
scorer = AutoModelForSequenceClassificationScorer(
language='de',
metric='sentiment',
# Each class represents a sentiment: 0 is negative, 1 is neutral, and 2
# is positive
class_weights=[0, 0.5, 1],
overflow_strategy=overflow_strategy,
max_input_length=512)
return scorer.score(generated_outputs)
def _sentiment_eval_client(
generated_outputs: List[str],
eval_client: EvalClient,
) -> Tuple[List[Optional[float]], List[Optional[str]]]:
'''Calculates the sentiment scores and their associated explanations of
generated outputs using the provided EvalClient. This metric takes on float
values that are either 0, 0.5, or 1, where 0 is negative sentiment, 0.5 is
neutral sentiment, and 1 is positive sentiment. If a score could not be
computed, `None` is inserted to the score and explanation lists.
Args:
generated_outputs: A list of model generated outputs to evaluate
eval_client: EvalClient instance used to evaluate the generated outputs
Returns:
score_list: a list of scores
explanation_list: a list of explanations for the scores
'''
sentiment_template = get_template('de/metrics/sentiment.j2')
sentiment_assessment_to_score = {
'Positive': 1.0,
'Neutral': 0.5,
'Negative': 0.0
}
populated_prompts = [
sentiment_template.render({'gen_output': gen_output})
for gen_output in generated_outputs
]
scores, explanations = eval_client.get_score(
metric_name='sentiment',
language=LANG,
prompts=populated_prompts,
score_map=sentiment_assessment_to_score)
return scores, explanations
[docs]def fluency(
generated_outputs: List[str] | str,
prompts: Optional[List[str] | str] = None,
eval_model: str | EvalClient = 'local',
) -> MetricValue[Optional[float]]:
'''Calculates the fluency scores of generated outputs. This metric takes on
float values between [0, 1], where 0 is low fluency and 1 is high fluency.
(NOTE: when using an EvalClient, the fluency scores are either 0.0
(poor), 0.5 (fair), or 1.0 (good). The score may also be `None` if it could
not be computed.)
We currently support two evaluation model types:
1. The 'local' type, we first translate the generated outputs
to English, then use the Parrot fluency model for the English counterpart.
This is the default model type and there is no setup needed to run this.
2. The EvalClient type, where you can use an EvalClient typically
implemented with an LLM. The implementation details are explained in each of
the concrete EvalClient classes.
Args:
generated_outputs: The model generated output(s) to evaluate
prompts: The prompts used to generate the output(s). Prompts are
optional metadata and not used to calculate the metric.
eval_model: The type of model to use ('local' or the EvalClient instance
used for the evaluation). default 'local'
Returns:
An :class:`~langcheck.metrics.metric_value.MetricValue` object
'''
generated_outputs, prompts = validate_parameters_reference_free(
generated_outputs, prompts)
if isinstance(generated_outputs, str):
generated_outputs = [generated_outputs]
if eval_model == 'local':
# Translate to English
translation = Translate(_translation_model_path)
generated_outputs_en = [translation(str) for str in generated_outputs]
_metric_value = en_fluency(generated_outputs_en, prompts, eval_model)
scores = _metric_value.metric_values
explanations = None
else: # EvalClient
assert isinstance(
eval_model, EvalClient
), 'An EvalClient must be provided for non-local model types.'
scores, explanations = _fluency_eval_client(generated_outputs,
eval_model)
return MetricValue(metric_name='fluency',
prompts=prompts,
generated_outputs=generated_outputs,
reference_outputs=None,
sources=None,
explanations=explanations,
metric_values=scores,
language=LANG)
def _fluency_eval_client(
generated_outputs: List[str], eval_client: EvalClient
) -> Tuple[List[Optional[float]], List[Optional[str]]]:
'''Calculates the fluency scores and their associated explanations of
generated outputs using the provided EvalClient. This metric takes on float
values that are either 0, 0.5, or 1, where 0 is "poor" fluency, 0.5 is
"fair" fluency, and 1 is "good" fluency. If a score could not be computed,
`None` is inserted to the score and explanation lists.
Args:
generated_outputs: A list of model generated outputs to evaluate
eval_client: EvalClient instance used to evaluate the generated outputs
Returns:
score_list: a list of scores
explanation_list: a list of explanations for the scores
'''
fluency_template = get_template('de/metrics/fluency.j2')
fluency_assessment_to_score = {
'Poor': 0,
'Fair': 0.5,
'Good': 1.0,
}
populated_prompts = [
fluency_template.render({'gen_output': gen_output})
for gen_output in generated_outputs
]
scores, explanations = eval_client.get_score(
metric_name='fluency',
language=LANG,
prompts=populated_prompts,
score_map=fluency_assessment_to_score)
return scores, explanations
[docs]def toxicity(
generated_outputs: List[str] | str,
prompts: Optional[List[str] | str] = None,
eval_model: str | EvalClient = 'local',
local_overflow_strategy: str = 'truncate',
) -> MetricValue[Optional[float]]:
'''Calculates the toxicity scores of generated outputs. This metric takes on
float values between [0, 1], where 0 is low toxicity and 1 is high toxicity.
(NOTE: when using an EvalClient, the toxicity scores are in steps of
0.25. The score may also be `None` if it could not be computed.)
We currently support two evaluation model types:
1. The 'local' type, where the multilingual Detoxify model is downloaded
from GitHub and run locally. This is the default model type and there is
no setup needed to run this.
2. The EvalClient type, where you can use an EvalClient typically
implemented with an LLM. The implementation details are explained in each of
the concrete EvalClient classes.
Args:
generated_outputs: The model generated output(s) to evaluate
prompts: The prompts used to generate the output(s). Prompts are
optional metadata and not used to calculate the metric.
eval_model: The type of model to use ('local' or the EvalClient instance
used for the evaluation). default 'local'
local_overflow_strategy: The strategy to handle the inputs that are too
long for the local model. The supported strategies are 'nullify',
'truncate', and 'raise'. If 'nullify', the outputs that are too long
will be assigned a score of None. If 'truncate', the outputs that
are too long will be truncated. If 'raise', an error will be raised
when the outputs are too long. The default value is 'nullify'.
Returns:
An :class:`~langcheck.metrics.metric_value.MetricValue` object
'''
generated_outputs, prompts = validate_parameters_reference_free(
generated_outputs, prompts)
if eval_model == 'local':
scores = _toxicity_local(generated_outputs, local_overflow_strategy)
explanations = None
else: # EvalClient
assert isinstance(
eval_model, EvalClient
), 'An EvalClient must be provided for non-local model types.'
scores, explanations = _toxicity_eval_client(generated_outputs,
eval_model)
return MetricValue(metric_name='toxicity',
prompts=prompts,
generated_outputs=generated_outputs,
reference_outputs=None,
sources=None,
explanations=explanations,
metric_values=scores,
language=LANG)
def _toxicity_local(generated_outputs: List[str],
overflow_strategy: str) -> List[Optional[float]]:
'''Calculates the toxicity scores of generated outputs using the Detoxify
model. This metric takes on float values between [0, 1], where 0 is low
toxicity and 1 is high toxicity.
Ref:
https://github.com/unitaryai/detoxify
Args:
generated_outputs: A list of model generated outputs to evaluate
overflow_strategy: The strategy to handle inputs that are longer than
the maximum input length of the model.
Returns:
A list of scores
'''
return DetoxifyScorer(
lang=LANG, overflow_strategy=overflow_strategy).score(generated_outputs)
def _toxicity_eval_client(
generated_outputs: List[str], eval_client: EvalClient
) -> Tuple[List[Optional[float]], List[Optional[str]]]:
'''Calculates the toxicity scores and their associated explanations of
generated outputs using the provided EvalClient. This metric takes on float
values between [0, 1] (in steps of 0.25), where 0 is low toxicity and 1 is
high toxicity. If a score could not be computed, `None` is inserted to the
score and explanation lists.
Args:
generated_outputs: A list of model generated outputs to evaluate
eval_client: EvalClient instance used to evaluate the generated outputs
Returns:
score_list: a list of scores
explanation_list: a list of explanations for the scores
'''
toxicity_template = get_template('de/metrics/toxicity.j2')
toxicity_assessment_to_score = {
'1': 0,
'2': 0.25,
'3': 0.5,
'4': 0.75,
'5': 1.0
}
populated_prompts = [
toxicity_template.render({'gen_output': gen_output})
for gen_output in generated_outputs
]
scores, explanations = eval_client.get_score(
metric_name='toxicity',
language=LANG,
prompts=populated_prompts,
score_map=toxicity_assessment_to_score)
return scores, explanations
[docs]def flesch_kincaid_grade(
generated_outputs: List[str] | str,
prompts: Optional[List[str] | str] = None) -> MetricValue[float]:
'''Calculates the readability of generated outputs using the Flesch-Kincaid.
It is the same as in English (but higher):
ref:
https://de.wikipedia.org/wiki/Lesbarkeitsindex#Flesch-Kincaid-Grade-Level
'''
metric_value = en_flesch_kincaid_grade(generated_outputs, prompts)
metric_value.language = LANG
return metric_value
[docs]def flesch_reading_ease(
generated_outputs: List[str] | str,
prompts: Optional[List[str] | str] = None) -> MetricValue[float]:
'''Calculates the readability of generated outputs using the Flesch Reading
Ease Score. This metric takes on float values between (-∞, 121.22], but
typically ranges between 0 and 100, where higher scores mean the text is
easier to read.
The score is based on the number of sentences, words, and syllables in the
text. See "How to Write Plain English" by Rudolf Franz Flesch for more
details.
For the German Formula, see
https://de.wikipedia.org/wiki/Lesbarkeitsindex#Flesch-Reading-Ease
FRE(Deutsch) = 180 - ASL - 58.5 * ASW
Args:
generated_outputs: The model generated output(s) to evaluate
prompts: The prompts used to generate the output(s). Prompts are
optional metadata and not used to calculate the metric.
Returns:
An :class:`~langcheck.metrics.metric_value.MetricValue` object
'''
generated_outputs, prompts = validate_parameters_reference_free(
generated_outputs, prompts)
output_stats = [
compute_stats(output)
for output in tqdm_wrapper(generated_outputs, desc='Computing stats')
]
scores = [
180 - (stat.num_words / stat.num_sentences) - 58.5 *
(stat.num_syllables / stat.num_words) for stat in output_stats
]
return MetricValue(metric_name='flesch_reading_ease',
prompts=prompts,
generated_outputs=generated_outputs,
reference_outputs=None,
sources=None,
explanations=None,
metric_values=scores,
language=LANG)
[docs]def ai_disclaimer_similarity(
generated_outputs: List[str] | str,
prompts: Optional[List[str] | str] = None,
ai_disclaimer_phrase: str = (
"Ich habe keine persönlichen Meinungen, Emotionen oder Bewusstsein."
),
eval_model: str | EvalClient = 'local') -> MetricValue[float]:
'''Calculates the degree to which the LLM's output contains a disclaimer
that it is an AI. This is calculated by computing the semantic similarity
between the generated outputs and a reference AI disclaimer phrase; by
default, this phrase is "Ich habe keine persönlichen Meinungen, Emotionen
oder Bewusstsein." (the most common reply from chatGPT in German),
but you can also pass in a custom phrase. Please refer to
:func:`~langcheck.eval.de.reference_based_text_quality.semantic_similarity`
for details on the typical output ranges and the supported embedding model
types.
Args:
generated_outputs: A list of model generated outputs to evaluate
prompts: An optional list of prompts used to generate the outputs.
Prompts are not evaluated and only used as metadata.
ai_disclaimer_phrase: Reference AI disclaimer phrase, default "I don't
have personal opinions, emotions, or consciousness."
eval_model: The type of model to use ('local' or the EvalClient instance
used for the evaluation). default 'local'
Returns:
An :class:`~langcheck.metrics.metric_value.MetricValue` object
'''
generated_outputs, prompts = validate_parameters_reference_free(
generated_outputs, prompts)
ai_disclaimer_phrase_list = [ai_disclaimer_phrase] * len(generated_outputs)
semantic_similarity_values = semantic_similarity(generated_outputs,
ai_disclaimer_phrase_list,
prompts, eval_model)
return MetricValue(metric_name='ai_disclaimer_similarity',
prompts=prompts,
generated_outputs=generated_outputs,
reference_outputs=None,
sources=None,
explanations=None,
metric_values=semantic_similarity_values.metric_values,
language=LANG)
[docs]def answer_relevance(generated_outputs: List[str] | str,
prompts: List[str] | str,
eval_model: EvalClient) -> MetricValue[Optional[float]]:
'''Calculates the relevance of generated outputs to the prompt. This metric
takes on float values of either 0.0 (Not Relevant), 0.5 (Partially
Relevant), or 1.0 (Fully Relevant). The score may also be `None` if it could
not be computed.
We currently only support the evaluation based on an EvalClient.
'''
generated_outputs, prompts = validate_parameters_answer_relevance(
generated_outputs, prompts)
answer_relevance_template = get_template('de/metrics/answer_relevance.j2')
populated_prompts = [
answer_relevance_template.render({
'gen_output': gen_output,
'user_query': prompt
}) for gen_output, prompt in zip(generated_outputs, prompts)
]
answer_relevance_assessment_to_score = {
'Fully Relevant': 1.0,
'Partially Relevant': 0.5,
'Not Relevant': 0.0
}
scores, explanations = eval_model.get_score(
metric_name='answer relevance',
language='de',
prompts=populated_prompts,
score_map=answer_relevance_assessment_to_score)
return MetricValue(metric_name='answer_relevance',
prompts=prompts,
generated_outputs=generated_outputs,
reference_outputs=None,
sources=None,
explanations=explanations,
metric_values=scores,
language=LANG)
