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from lm_eval.api.task import ConfigurableTask | |
from lm_eval.api.instance import Instance | |
# from lm_eval.api.registry import register_task | |
from lm_eval.api.metrics import mean | |
import torch | |
import sacrebleu | |
from rouge_score import rouge_scorer, scoring | |
def bleu(refs, preds): | |
""" | |
Returns `t5` style BLEU scores. See the related implementation: | |
https://github.com/google-research/text-to-text-transfer-transformer/blob/3d10afd51ba97ac29eb66ae701eca274488202f7/t5/evaluation/metrics.py#L41 | |
:param refs: | |
A `list` of `list` of reference `str`s. | |
:param preds: | |
A `list` of predicted `str`s. | |
""" | |
score = sacrebleu.corpus_bleu(preds, refs, smooth_method="exp", smooth_value=0.0, force=False, | |
lowercase=False, tokenize="intl", use_effective_order=False).score | |
return score | |
def rouge(refs, preds): | |
""" | |
Returns `t5` style ROUGE scores. See the related implementation: | |
https://github.com/google-research/text-to-text-transfer-transformer/blob/3d10afd51ba97ac29eb66ae701eca274488202f7/t5/evaluation/metrics.py#L68 | |
:param refs: | |
A `list` of reference `strs`. | |
:param preds: | |
A `list` of predicted `strs`. | |
""" | |
rouge_types = ["rouge1", "rouge2", "rougeLsum"] | |
scorer = rouge_scorer.RougeScorer(rouge_types) | |
# Add newlines between sentences to correctly compute `rougeLsum`. | |
def _prepare_summary(summary): | |
summary = summary.replace(" . ", ".\n") | |
return summary | |
# Accumulate confidence intervals. | |
aggregator = scoring.BootstrapAggregator() | |
for ref, pred in zip(refs, preds): | |
ref = _prepare_summary(ref) | |
pred = _prepare_summary(pred) | |
aggregator.add_scores(scorer.score(ref, pred)) | |
result = aggregator.aggregate() | |
return {type: result[type].mid.fmeasure * 100 for type in rouge_types} | |
# @register_task("xsum_v2") | |
class XSumv2(ConfigurableTask): | |
VERSION = 0 | |
DATASET_PATH = "EdinburghNLP/xsum" | |
DATASET_NAME = None | |
def __init__(self): | |
super().__init__(config={'metadata': {'version': self.VERSION}}) | |
self.factkb_tokenizer = None | |
self.factkb_model = None | |
self.bert_score = None | |
def maybe_init_factkb(self): | |
if self.factkb_tokenizer is None or self.factkb_model is None: | |
from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
self.factkb_tokenizer = AutoTokenizer.from_pretrained("roberta-base", padding="max_length", truncation=True) | |
self.factkb_model = AutoModelForSequenceClassification.from_pretrained("bunsenfeng/FactKB", num_labels=2, device_map="auto") | |
def maybe_init_bertscore(self): | |
if self.bert_score is None: | |
from evaluate import load | |
self.bert_score = load("bertscore") | |
def has_training_docs(self): | |
return True | |
def has_validation_docs(self): | |
return True | |
def has_test_docs(self): | |
return True | |
def training_docs(self): | |
return self.dataset["train"] | |
def validation_docs(self): | |
return self.dataset["validation"] | |
def test_docs(self): | |
return self.dataset["test"] | |
# def fewshot_delimiter(self): | |
# return "\n\n" | |
# From https://arxiv.org/abs/2305.14739 | |
def doc_to_text(self, doc): | |
return f'Article: {doc["document"]}\nSummarize the article in one sentence. Summary:' | |
def should_decontaminate(self): | |
return True | |
def doc_to_decontamination_query(self, doc): | |
return doc["document"] | |
def doc_to_target(self, doc): | |
return doc["summary"] | |
def construct_requests(self, doc, ctx, **kwargs): | |
"""Uses RequestFactory to construct Requests and returns an iterable of | |
Requests which will be sent to the LM. | |
:param doc: | |
The document as returned from training_docs, validation_docs, or test_docs. | |
:param ctx: str | |
The context string, generated by fewshot_context. This includes the natural | |
language description, as well as the few shot examples, and the question | |
part of the document for `doc`. | |
""" | |
return [ | |
Instance( | |
request_type="generate_until", | |
doc=doc, | |
# arguments=(ctx, {"until": ["\n", "."]}), | |
arguments=(ctx, {"until": ["\n"]}), | |
idx=0, | |
**kwargs | |
) | |
] | |
def process_results(self, doc, results): | |
completion = results[0] | |
# breakpoint() | |
document = doc["document"] | |
gold_summary = doc["summary"] | |
true_refs = [doc["summary"]] | |
all_refs = true_refs | |
# ROUGE-N | |
rouge_scores = [rouge([ref], [completion]) for ref in all_refs] | |
# ROUGE-1 | |
rouge1_scores = [score["rouge1"] for score in rouge_scores] | |
# ROUGE-2 | |
rouge2_scores = [score["rouge2"] for score in rouge_scores] | |
# ROUGE-L | |
rougeL_scores = [score["rougeLsum"] for score in rouge_scores] | |
self.maybe_init_factkb() | |
input_factkb = [[completion, document]] | |
factkb_tokens = self.factkb_tokenizer(input_factkb, return_tensors="pt", padding="max_length", truncation=True).to(self.factkb_model.device) | |
factkb_logits = self.factkb_model(**factkb_tokens).logits | |
factkb_res = torch.softmax(factkb_logits, dim=1) | |
self.maybe_init_bertscore() | |
bert_score_res = self.bert_score.compute(predictions=[completion], references=[gold_summary], model_type="microsoft/deberta-xlarge-mnli", lang="en") | |
res = { | |
"rouge1": rouge1_scores[0], | |
"rouge2": rouge2_scores[0], | |
"rougeL": rougeL_scores[0], | |
"factKB": float(factkb_res[0][1]), | |
"bertscore_precision": float(bert_score_res["precision"][0]), | |
"bertscore_recall": float(bert_score_res["recall"][0]), | |
"bertscore_f1": float(bert_score_res["f1"][0]), | |
} | |
# breakpoint() | |
return res | |
def aggregation(self): | |
""" | |
:returns: {str: [float] -> float} | |
A dictionary where keys are the names of submetrics and values are | |
functions that aggregate a list of metrics | |
""" | |
return {k: mean for k in ["rouge1", "rouge2", "rougeL", "factKB", "bertscore_precision", "bertscore_recall", "bertscore_f1"]} | |
def higher_is_better(self): | |
""" | |
:returns: {str: bool} | |
A dictionary where keys are the names of submetrics and values are | |
whether a higher value of the submetric is better | |
""" | |
return {k: True for k in ["rouge1", "rouge2", "rougeL", "factKB", "bertscore_precision", "bertscore_recall", "bertscore_f1"]} | |