from __future__ import annotations
from typing import List, Optional
import hanlp
from transformers.pipelines import pipeline
from langcheck.metrics._validation import validate_parameters_reference_free
from langcheck.metrics.en.reference_free_text_quality import \
_toxicity_eval_client
from langcheck.metrics.en.reference_free_text_quality import \
sentiment as en_sentiment
from langcheck.metrics.eval_clients import EvalClient
from langcheck.metrics.metric_value import MetricValue
[docs]def sentiment(
generated_outputs: List[str] | str,
prompts: Optional[List[str] | str] = None,
eval_model: str | EvalClient = 'local',
) -> 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 IDEA-CCNL/Erlangshen-Roberta-110M-Sentiment
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/IDEA-CCNL/Erlangshen-Roberta-110M-Sentiment
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 eval_model != 'local': # EvalClient
assert isinstance(
eval_model, EvalClient
), 'An EvalClient must be provided for non-local model types.'
# This reuses the English prompt.
# TODO: Update this to use a Chinese prompt.
metric_value = en_sentiment(generated_outputs, prompts, eval_model)
metric_value.language = 'zh'
return metric_value
# {0:"Negative", 1:'Positive'}
from langcheck.metrics.model_manager import manager
tokenizer, model = manager.fetch_model(language='zh', metric='sentiment')
_sentiment_pipeline = pipeline(
'sentiment-analysis',
model=model, # type: ignore[reportGeneralTypeIssues]
tokenizer=tokenizer # type: ignore[reportGeneralTypeIssues]
)
_model_id2label = _sentiment_pipeline.model.config.id2label
_predict_result = _sentiment_pipeline(
generated_outputs
) # type: ignore[reportGeneralTypeIssues] # NOQA: E501
# if predicted result is 'Positive', use the score directly
# else, use 1 - score as the sentiment score
# yapf: disable
scores = [
1 - x['score'] if x['label'] == _model_id2label[0] else x['score'] # type: ignore[reportGeneralTypeIssues] # NOQA: E501
for x in _predict_result # type: ignore[reportGeneralTypeIssues] # NOQA: E501
]
# yapf: enable
return MetricValue(
metric_name='sentiment',
prompts=prompts,
generated_outputs=generated_outputs,
reference_outputs=None,
sources=None,
explanations=None,
metric_values=scores, # type: ignore[reportGeneralTypeIssues]
language='zh')
[docs]def toxicity(
generated_outputs: List[str] | str,
prompts: Optional[List[str] | str] = None,
eval_model: str | EvalClient = 'local') -> 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 a model file is downloaded from HuggingFace and
run locally. This is the default model type and there is no setup needed to
run this.
The model (alibaba-pai/pai-bert-base-zh-llm-risk-detection) is a
risky detection model for LLM generated content released by Alibaba group.
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/alibaba-pai/pai-bert-base-zh-llm-risk-detection
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 eval_model == 'local':
scores = _toxicity_local(generated_outputs)
explanations = None
else:
assert isinstance(
eval_model, EvalClient
), 'An EvalClient must be provided for non-local model types.'
# This reuses the English prompt.
# TODO: Update this to use a Chinese prompt.
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='zh')
def _toxicity_local(generated_outputs: List[str]) -> List[float]:
'''Calculates the toxicity scores of generated outputs using a fine-tuned
model from `alibaba-pai/pai-bert-base-zh-llm-risk-detection`. This metric
takes on float values between [0, 1], where 0 is low toxicity and 1 is high
toxicity.
Ref:
https://huggingface.co/alibaba-pai/pai-bert-base-zh-llm-risk-detection
Args:
generated_outputs: A list of model generated outputs to evaluate
Returns:
A list of scores
'''
# this pipeline output predict probability for each text on each label.
# the output format is List[List[Dict(str)]]
from langcheck.metrics.model_manager import manager
tokenizer, model = manager.fetch_model(language='zh', metric="toxicity")
_toxicity_pipeline = pipeline(
'text-classification',
model=model, # type: ignore[reportOptionalIterable]
tokenizer=tokenizer, # type: ignore[reportOptionalIterable]
top_k=5)
# {'Normal': 0, 'Pulp': 1, 'Sex': 2, 'Other Risk': 3, 'Adult': 4}
_model_id2label = _toxicity_pipeline.model.config.id2label
_predict_results = _toxicity_pipeline(
generated_outputs # type: ignore[reportGeneralTypeIssues]
)
# labels except Normal are all risky, toxicity_score = 1-score['Normal']
toxicity_scores = []
for item_predict_proba in _predict_results: # type: ignore[reportOptionalIterable] # NOQA: E501
for label_proba in item_predict_proba: # type: ignore[reportGeneralTypeIssues] # NOQA: E501
# yapf: disable
if label_proba['label'] == _model_id2label[0]: # type: ignore[reportGeneralTypeIssues] # NOQA: E501
toxicity_scores.append(1 - label_proba['score']) # type: ignore[reportGeneralTypeIssues] # NOQA: E501
# yapf: enable
return toxicity_scores # type: ignore[reportGeneralTypeIssues]
[docs]def xuyaochen_report_readability(
generated_outputs: List[str] | str,
prompts: Optional[List[str] | str] = None) -> MetricValue[float]:
'''Calculates the readability scores of generated outputs introduced in
"中文年报可读性"(Chinese annual report readability). This metric calculates
average words per sentence as r1, average of the sum of the numbers of
adverbs and coordinating conjunction words in a sentence in given generated
outputs as r2, then, refer to the Fog Index that combine r1 with r2 by
arithmetic mean as the final outputs. This function uses HanLP Tokenizer and
POS at the same time, POS in CTB style
https://hanlp.hankcs.com/docs/annotations/pos/ctb.html.
The lower the score is, the better the readability. The score is mainly
influenced by r1, the average number of words in sentences.
Ref:
Refer Chinese annual report readability: measurement and test
Link: https://www.tandfonline.com/doi/full/10.1080/21697213.2019.1701259
Args:
generated_outputs: A list of model generated outputs to evaluate
prompts: The prompts used to generate the output(s). Prompts are
optional metadata and not used to calculate the metric.
Returns:
A list of scores
'''
# split generated_outputs into sentence
generated_outputs, prompts = validate_parameters_reference_free(
generated_outputs, prompts=prompts)
# yapf: disable
tokenizer = hanlp.load(
hanlp.pretrained.tok.FINE_ELECTRA_SMALL_ZH # type: ignore[reportGeneralTypeIssues] # NOQA: E501
)
postagger = hanlp.load(
hanlp.pretrained.pos.CTB9_POS_RADICAL_ELECTRA_SMALL # type: ignore[reportGeneralTypeIssues] # NOQA: E501
)
pos_pipeline = hanlp.pipeline().\
append(hanlp.utils.rules.split_sentence) # type: ignore[reportGeneralTypeIssues] # NOQA: E501
pos_pipeline = pos_pipeline.append(tokenizer).append(postagger)
tokenize_pipeline = hanlp.pipeline().\
append(hanlp.utils.rules.split_sentence) # type: ignore[reportGeneralTypeIssues] # NOQA: E501
tokenize_pipeline = tokenize_pipeline.append(tokenizer)
# OUTPUT: List[List[List[TOKEN]]]
output_tokens = list(map(tokenize_pipeline, generated_outputs))
# List[List[List[POS]]]
output_pos = list(map(pos_pipeline, generated_outputs))
def count_tokens(sent_tokens: List[str]) -> int:
count = sum([
not hanlp.utils.string_util.ispunct(token) for token in # type: ignore[reportGeneralTypeIssues] # NOQA: E501
sent_tokens
])
return count
def count_postags(sent_poses: List[str]) -> int:
# AD: adverb, CC: coordinating conjunction,
# CS: subordinating conjunction
count = sum([pos in ['AD', 'CC', 'CS'] for pos in sent_poses])
return count
def calc_r1(content: List[List[str]]) -> float:
token_count_by_sentence = list(map(count_tokens, content))
if len(token_count_by_sentence) == 0:
return 0
else:
return sum(token_count_by_sentence) / len(token_count_by_sentence)
def calc_r2(content: List[List[str]]) -> float:
pos_count_by_sentence = list(map(count_postags, content))
if len(pos_count_by_sentence) == 0:
return 0
else:
return sum(pos_count_by_sentence) / len(pos_count_by_sentence)
r1 = list(map(calc_r1, output_tokens)) # type: ignore[reportGeneralTypeIssues] # NOQA: E501
r2 = list(map(calc_r2, output_pos)) # type: ignore[reportGeneralTypeIssues] # NOQA: E501
r3 = [(r1_score + r2_score) * 0.5 for r1_score, r2_score in zip(r1, r2)]
# yapf: enable
return MetricValue(metric_name='readability',
prompts=prompts,
generated_outputs=generated_outputs,
sources=None,
reference_outputs=None,
explanations=None,
metric_values=r3,
language='zh')
