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"""Chatbots using API-based services."""
from __future__ import annotations
import os
from dataclasses import dataclass
import config
@dataclass(frozen=True)
class GptMtInstance:
"""An instance from the GPT-MT dataset.
Attributes:
data: The input sentence.
label: The output sentence.
doc_id: The document ID.
lang_pair: The language pair.
"""
data: str
label: str
doc_id: str
lang_pair: str
def process_data(
input_dir: str,
lang_pairs: list[str],
) -> list[GptMtInstance]:
"""Load data."""
# Load the data
data: list[GptMtInstance] = []
eval_dir = os.path.join(input_dir, "evaluation", "testset")
for lang_pair in lang_pairs:
src_lang, trg_lang = lang_pair[:2], lang_pair[2:]
src_file = os.path.join(
eval_dir, "wmt-testset", lang_pair, f"test.{src_lang}-{trg_lang}.{src_lang}"
)
trg_file = os.path.join(
eval_dir, "wmt-testset", lang_pair, f"test.{src_lang}-{trg_lang}.{trg_lang}"
)
doc_file = os.path.join(
eval_dir,
"wmt-testset-docids",
lang_pair,
f"test.{src_lang}-{trg_lang}.docids",
)
with open(src_file, "r") as src_in, open(trg_file, "r") as trg_in, open(
doc_file, "r"
) as doc_in:
for src_line, trg_line, doc_line in zip(src_in, trg_in, doc_in):
data.append(
GptMtInstance(
src_line.strip(), trg_line.strip(), doc_line.strip(), lang_pair
)
)
return data
def process_output(
input_dir: str,
lang_pairs: list[str],
model_preset: str,
) -> list[str]:
"""Load model outputs."""
# Load the data
data: list[str] = []
model_path = config.model_configs[model_preset].path
system_dir = os.path.join(input_dir, "evaluation", "system-outputs", model_path)
for lang_pair in lang_pairs:
src_lang, trg_lang = lang_pair[:2], lang_pair[2:]
sys_file = os.path.join(
system_dir, lang_pair, f"test.{src_lang}-{trg_lang}.{trg_lang}"
)
with open(sys_file, "r") as sys_in:
for sys_line in sys_in:
data.append(sys_line.strip())
return data
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