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import html
import re
from datasets import load_metric
def general_detokenize(string):
string = re.sub(r"\s+([.,;:!?)])", r"\1", string)
string = re.sub(r"(\s+|^)\(\s+([^)]+)\s+\)", r"\1(\2)", string)
string = re.sub(r"(\s+|^)\[\s+([^)]+)\s+\]", r"\1[\2]", string)
string = re.sub(r'(\s+|^)"\s+([^"]+)\s+"', r'\1"\2"', string)
string = re.sub(r"(\s+|^)'\s+([^']+)\s+'", r"\1'\2'", string)
return string
def process_doc(string):
string = html.unescape(string)
string = general_detokenize(string)
return string
def process_wic_docs(dataset):
def _helper(doc):
# there's some issues with the encoding on this one
doc["sentence1"] = (
process_doc(doc["sentence1"]).encode("latin-1").decode("utf-8")
)
doc["sentence2"] = (
process_doc(doc["sentence2"]).encode("latin-1").decode("utf-8")
)
return doc
return dataset.map(_helper)
def coref_doc_to_text(x):
def _span_in_context(span_index, span_text):
span_start = span_index
span_end = span_start + len(span_text.split(" ")) - 1
tokens[span_start] = f"*{tokens[span_start]}"
tokens[span_end] = f"{tokens[span_end]}*"
tokens = x["text"].split(" ")
_span_in_context(x["span1_index"], x["span1_text"])
_span_in_context(
x["span2_index"] - 1, x["span2_text"]
) # span1_index is 0-based but span2_index is 1-based ??
context = process_doc(" ".join(tokens))
span_1 = process_doc(x["span1_text"])
span_2 = process_doc(x["span2_text"])
text = (
f"Testua: {context}\n"
+ f'Galdera: Aurreko testuan, "*{span_1}*" eta "*{span_2}*" gauza bera dira?\n'
+ "Erantzuna:"
)
return text
# Measure F1 as in the benchmark repo: https://github.com/orai-nlp/BasqueGLUE/blob/main/eval_basqueglue.py
def micro_f1_score(items):
f1_metric = load_metric("f1")
golds, preds = list(zip(*items))
f1_score = f1_metric.compute(references=golds, predictions=preds, average="micro")[
"f1"
]
return f1_score
def vaxx_f1_score(items):
f1_metric = load_metric("f1")
golds, preds = list(zip(*items))
f1_class = f1_metric.compute(
references=golds, predictions=preds, labels=[0, 2], average=None
)["f1"]
f1_score = sum(f1_class) / len(f1_class)
return f1_score
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