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import datasets
import evaluate
# from harim_scorer import Harimplus_Scorer #no plan to package it to pip
import torch
import torch.nn.functional as F
from transformers import (AutoModelForSeq2SeqLM,
AutoTokenizer,
PreTrainedTokenizer,
PreTrainedTokenizerFast,
)
from transformers.tokenization_utils_base import BatchEncoding # for custom tokenizer other than huggingface
import pandas as pd
from tqdm import tqdm
from typing import List, Dict, Union
from collections import defaultdict
from functools import partial
logger = evaluate.logging.get_logger(__name__)
CODEBASE_URL='https://huggingface.co/spaces/NCSOFT/harim_plus'
PAPER_URL='https://arxiv.org/abs/2211.12118'
_CITATION = """\
@inproceedings{son-etal-2022-harim,
title = "{H}a{R}i{M}$^+$: Evaluating Summary Quality with Hallucination Risk",
author = "Son, Seonil (Simon) and
Park, Junsoo and
Hwang, Jeong-in and
Lee, Junghwa and
Noh, Hyungjong and
Lee, Yeonsoo",
booktitle = "Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing",
month = nov,
year = "2022",
address = "Online only",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.aacl-main.66",
pages = "895--924",
abstract = "One of the challenges of developing a summarization model arises from the difficulty in measuring the factual inconsistency of the generated text. In this study, we reinterpret the decoder overconfidence-regularizing objective suggested in (Miao et al., 2021) as a hallucination risk measurement to better estimate the quality of generated summaries. We propose a reference-free metric, HaRiM+, which only requires an off-the-shelf summarization model to compute the hallucination risk based on token likelihoods. Deploying it requires no additional training of models or ad-hoc modules, which usually need alignment to human judgments. For summary-quality estimation, HaRiM+ records state-of-the-art correlation to human judgment on three summary-quality annotation sets: FRANK, QAGS, and SummEval. We hope that our work, which merits the use of summarization models, facilitates the progress of both automated evaluation and generation of summary.",
}
"""
_DESCRIPTION = f"""HaRiM+ is a reference-less evaluation metric (i.e. requires only article-summary pair, no reference summary) for summarization which leverages the power of summarization model.
Summarization model inside the HaRiM+ will read and evaluate how good the quality of a summary given the paired article.
It will work great for ranking the summary-article pairs according to its quality.
HaRiM+ is proved effective for benchmarking summarization systems (system-level performance) as well as ranking the article-summary pairs (segment-level performance) in comprehensive aspect such as factuality, consistency, coherency, fluency, and relevance. For details, refer to our [paper]({PAPER_URL}) published in AACL2022.
NOTE that for HaRiM+...
* predictions = summaries (List[str])
* references = articles (List[str])
Also Note that
* higher score = better quality
"""
_KWARGS_DESCRIPTION = """
HaRiM+ score.
Args:
For scorer = evaluate.load():
`pretrained_name` (str or pathlib.Path): summarization model checkpoint or path, loaded by transformers.AutoModelForSeq2SeqLM.from_pretrained(). Defaults to Yale-LILY/brio-cnndm-uncased.
`tokenizer`: (use when your tokenizer cannot be loaded by from_pretrained)Tokenizer function compatible with transformers.PreTrainedTokenizer. It requires tokenizer.pad_token|eos_token|bos_token and tokenizer.__call__() method for HaRiM+ score computation.
For scorer.compute():
`predictions` (list of str): generated summaries
`references` (list of str): source articles to be summarized
`use_aggregator` (bool=False): if True, average of the scores are returned
`bsz` (int=32): batch size for harim to iterate through the given pairs
`return_details` (bool=False): whether to show more than harim+ score (returns logppl, harim term. refer to the paper for detail)
`tokenwise_score` (bool=False): whether to show tokenwise scores for input pairs (if return_details=False, this is ignored)
Returns:
'results' (list of float): harim+ score for each summary-article pair
Examples:
>>> summaries = ["hello there", "hello there"]
>>> articles = ["hello, this is the article to be summarized", "hello, this is the article to be summarized"]
>>> scorer = evaluate.load("NCSOFT/harim_plus") #, pretrained_name='PRETRAINEDNAME', tokenizer=TOKENIZER # optional
>>> results = scorer.compute(predictions=summaries, references=articles) # use_aggregator=True # optional
>>> print([round(v, 2) for v in results["harim+"]])
[float, float]
"""
class Harimplus_Scorer:
def __init__(self,
pretrained_name:str='none',
tokenizer:Union[PreTrainedTokenizer, PreTrainedTokenizerFast]=None,
mixing_factor:float=7., # same as lambda in the paper
device:str='cuda',
src_maxlen=1024,
tgt_maxlen=110,
):
self._pretrained_name = pretrained_name
self._lambda = mixing_factor
self._device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
self._encdec_model = AutoModelForSeq2SeqLM.from_pretrained(self._pretrained_name)
if tokenizer is None:
self._tokenizer = AutoTokenizer.from_pretrained(self._pretrained_name)
else:
self._tokenizer = tokenizer
self._encdec_model.to(self._device)
self._encdec_model.eval()
self._src_maxlen = src_maxlen
self._tgt_maxlen = tgt_maxlen
def _prep_input(self, src_tgt_txts, src_or_tgt='src'):
L = self._src_maxlen if src_or_tgt=='src' else self._tgt_maxlen
if isinstance(src_tgt_txts, pd.Series):
src_tgt_txts=src_tgt_txts.tolist()
if src_or_tgt == 'src':
src_tgt_txts = [ s.replace("\n", " ") for s in src_tgt_txts ]
return self._tokenizer(src_tgt_txts, padding=True, truncation=True, max_length=L, return_tensors='pt') # ModelInput dataclass
'''below are helper functions w/o dependency to the self, but included inside the class for ease of use'''
def likelihoods(self, logits, force_decode_indices, tgt_mask):
probs = F.softmax(logits, dim=-1)
probs_force_decode_ = probs.gather(-1, force_decode_indices.unsqueeze(-1)).squeeze()
probs_force_decode= probs_force_decode_ * tgt_mask
assert probs_force_decode.shape == force_decode_indices.shape
return probs_force_decode
def log_likelihoods(self, logits, force_decode_indices, tgt_mask):
ll = F.log_softmax(logits, dim=-1)
ll_force_decode_ = ll.gather(-1, force_decode_indices.unsqueeze(-1)).squeeze()
ll_force_decode = ll_force_decode_ * tgt_mask
return ll_force_decode
def harim(self, s2s_logits, lm_logits, force_decode_indices, tgt_mask ):
p_s2s, p_lm = self.likelihoods(s2s_logits, force_decode_indices, tgt_mask), \
self.likelihoods(lm_logits, force_decode_indices, tgt_mask)
delta = p_s2s - p_lm
margin_linear = (1-delta) / 2
harim = -(1-p_s2s) * margin_linear + 1
return harim # this is -1 * hallucination risk
def make_minibatches(self, exs:List[str], bsz:int=32):
idx=0
minibatches = []
while True:
start = idx
end = idx+bsz
if start >= len(exs):
break
minibatches.append( exs[start:end] )
idx += bsz
return minibatches
def make_empty_minibatches(self, minibatches:List[List[str]]):
e_minibatches = minibatches.copy()
for i, mb in enumerate(e_minibatches):
e_minibatches[i] = ['' for ex in mb]
return e_minibatches
def compute(self, predictions:List[str],
references:List[str],
bsz:int=32,
use_aggregator:bool=False,
return_details:bool=False,
# tokenwise_score:bool=False,
):
'''
returns harim+ score (List[float]) for predictions (summaries) and references (articles)
**Note**
- here, predictions = generated summaries to be evaluated, references = article to be summarized (but to follow the convention of the evaluate, we named kwarg as "references")
- log_ppl equals to bartscore (yuan et al., neurips 2021)
if tokenwise_score:
returns minibatch chunks of harim+ scores and log-likelihoods with tokenized predictions (List[str])
if use_aggregator:
returning scores are aggregated (mean) over given test set
'''
# tokenize/prep src/tgts
make_minibatches_bsz = partial(self.make_minibatches, bsz=bsz)
summaries = predictions
articles = references
b_srcs, b_tgts = map(make_minibatches_bsz, [articles, summaries])
b_emps = self.make_empty_minibatches(b_srcs)
scores=defaultdict(list)
for mini_s, mini_e, mini_t in tqdm(zip(b_srcs, b_emps, b_tgts), total=len(b_tgts), desc=f"computing HaRiM+ {bsz=}, core={self._pretrained_name}"):
src_in = self._prep_input(mini_s, src_or_tgt='src')
emp_in = self._prep_input(mini_e, src_or_tgt='src')
tgt_in = self._prep_input(mini_t, src_or_tgt='tgt')
if emp_in.input_ids.shape[-1]==0: # emp_in.input_ids.shape == (32,0)
boseos = f"{self._tokenizer.bos_token}{self._tokenizer.eos_token}"
mini_e_ = [boseos for _ in range(len(mini_e))]
emp_in = self._prep_input( mini_e_, src_or_tgt='src' )
tgt_mask = tgt_in.attention_mask # torch.Tensor
# if not tokenizer loaded from huggingface, this might cause some problem (.to(device))
if not isinstance(src_in, BatchEncoding):
src_in = BatchEncoding(src_in)
if not isinstance(emp_in, BatchEncoding):
emp_in = BatchEncoding(emp_in)
if not isinstance(tgt_in, BatchEncoding):
tgt_in = BatchEncoding(tgt_in)
src_in = src_in.to(self._device)
emp_in = emp_in.to(self._device)
tgt_in = tgt_in.to(self._device)
tgt_mask = tgt_mask.to(self._device)
fill_ignore_mask = ~(tgt_mask.bool())
with torch.no_grad():
# token_type_ids attribute causes error
s2s_logits = self._encdec_model.forward(
input_ids = src_in.input_ids,
attention_mask = src_in.attention_mask,
labels = tgt_in.input_ids.masked_fill(fill_ignore_mask, -100),
return_dict=True).logits
lm_logits = self._encdec_model.forward(
input_ids = emp_in.input_ids,
attention_mask = emp_in.attention_mask,
labels = tgt_in.input_ids.masked_fill(fill_ignore_mask, -100),
return_dict=True).logits
sent_lengths = tgt_mask.sum(-1)
ll_tok = self.log_likelihoods(s2s_logits, tgt_in.input_ids, 1)#tgt_mask)
ll = ll_tok.sum(-1) / sent_lengths
harim_tok = self.harim(s2s_logits, lm_logits, tgt_in.input_ids, 1)#tgt_mask)
harim = harim_tok.sum(-1) / sent_lengths
harim_plus_normalized = (ll + self._lambda * harim) # loglikelihood + lambda * negative_harim (negative harim=-1* risk)
scores['harim+'].extend(harim_plus_normalized.tolist())
scores['harim'].extend(harim.tolist())
scores['log_ppl'].extend(ll.tolist())
# if tokenwise_score:
# scores['tok_harim+'].append(harim_tok*self._lambda + ll_tok)
# scores['tok_predictions'].append( [self._tokenizer.convert_ids_to_token(idxs) for idxs in src_in.labels] )
if use_aggregator: # after
for k, v in scores.items():
if not k.startswith('tok_'):
scores[k] = sum(v)/len(v) # aggregate (mean)
scores['lambda'] = self._lambda
if not return_details:
scores = scores['harim+']
return scores
@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class Harimplus(evaluate.Metric):
def __init__(self,
pretrained_name='facebook/bart-large-cnn',
tokenizer=None,
device='cuda',
**kwargs
):
super().__init__(**kwargs)
self.myconfig = dict(
pretrained_name=pretrained_name,
tokenizer=tokenizer,
device=device,
)
def _info(self):
return evaluate.MetricInfo(
description=_DESCRIPTION,
citation=_CITATION,
homepage=CODEBASE_URL,
inputs_description=_KWARGS_DESCRIPTION,
features=datasets.Features(
{
"predictions": datasets.Value("string", id="sequence"),
"references": datasets.Value("string", id="sequence"),
}
),
codebase_urls=[CODEBASE_URL],
reference_urls=[CODEBASE_URL, PAPER_URL],
)
def _download_and_prepare(self, dl_manager):
pretrained_name = self.myconfig['pretrained_name']
is_custom_tokenizer = self.myconfig['tokenizer'] is not None
logger.warning(
"Loading HaRiM+ score"
f"\tpretrained_name = {pretrained_name}"
)
if is_custom_tokenizer:
logger.warning(
f"tokenizer is overriden by \n\tself.myconfig['tokenizer']"
)
logger.warning(
"You can change checkpoints with `pretrained_name` kwarg in evaluate.load. Strongly recommend to use *-large or larger ones."
"Refrain from using checkpoints trained on noisy corpus such as bbc-XSUM.")
# download the model checkpoint specified by self.myconfig_name and set up the scorer
self.scorer = Harimplus_Scorer(**self.myconfig)
def _compute(self, predictions=None,
references=None,
use_aggregator=False,
bsz=32,
return_details=False):
# tokenwise_score=False,
summaries = predictions
articles = references
scores = self.scorer.compute(predictions=summaries,
references=articles,
use_aggregator=use_aggregator,
bsz=bsz, #tokenwise_score=tokenwise_score,
return_details=return_details)
return scores
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