/
run_evaluation.py
131 lines (112 loc) · 4.71 KB
/
run_evaluation.py
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THE_INDEX = {
'dl19': 'msmarco-v1-passage',
'dl20': 'msmarco-v1-passage',
'covid': 'beir-v1.0.0-trec-covid.flat',
'arguana': 'beir-v1.0.0-arguana.flat',
'touche': 'beir-v1.0.0-webis-touche2020.flat',
'news': 'beir-v1.0.0-trec-news.flat',
'scifact': 'beir-v1.0.0-scifact.flat',
'fiqa': 'beir-v1.0.0-fiqa.flat',
'scidocs': 'beir-v1.0.0-scidocs.flat',
'nfc': 'beir-v1.0.0-nfcorpus.flat',
'quora': 'beir-v1.0.0-quora.flat',
'dbpedia': 'beir-v1.0.0-dbpedia-entity.flat',
'fever': 'beir-v1.0.0-fever-flat',
'robust04': 'beir-v1.0.0-robust04.flat',
'signal': 'beir-v1.0.0-signal1m.flat',
'mrtydi-ar': 'mrtydi-v1.1-arabic',
'mrtydi-bn': 'mrtydi-v1.1-bengali',
'mrtydi-fi': 'mrtydi-v1.1-finnish',
'mrtydi-id': 'mrtydi-v1.1-indonesian',
'mrtydi-ja': 'mrtydi-v1.1-japanese',
'mrtydi-ko': 'mrtydi-v1.1-korean',
'mrtydi-ru': 'mrtydi-v1.1-russian',
'mrtydi-sw': 'mrtydi-v1.1-swahili',
'mrtydi-te': 'mrtydi-v1.1-telugu',
'mrtydi-th': 'mrtydi-v1.1-thai',
}
THE_TOPICS = {
'dl19': 'dl19-passage',
'dl20': 'dl20-passage',
'covid': 'beir-v1.0.0-trec-covid-test',
'arguana': 'beir-v1.0.0-arguana-test',
'touche': 'beir-v1.0.0-webis-touche2020-test',
'news': 'beir-v1.0.0-trec-news-test',
'scifact': 'beir-v1.0.0-scifact-test',
'fiqa': 'beir-v1.0.0-fiqa-test',
'scidocs': 'beir-v1.0.0-scidocs-test',
'nfc': 'beir-v1.0.0-nfcorpus-test',
'quora': 'beir-v1.0.0-quora-test',
'dbpedia': 'beir-v1.0.0-dbpedia-entity-test',
'fever': 'beir-v1.0.0-fever-test',
'robust04': 'beir-v1.0.0-robust04-test',
'signal': 'beir-v1.0.0-signal1m-test',
'mrtydi-ar': 'mrtydi-v1.1-arabic-test',
'mrtydi-bn': 'mrtydi-v1.1-bengali-test',
'mrtydi-fi': 'mrtydi-v1.1-finnish-test',
'mrtydi-id': 'mrtydi-v1.1-indonesian-test',
'mrtydi-ja': 'mrtydi-v1.1-japanese-test',
'mrtydi-ko': 'mrtydi-v1.1-korean-test',
'mrtydi-ru': 'mrtydi-v1.1-russian-test',
'mrtydi-sw': 'mrtydi-v1.1-swahili-test',
'mrtydi-te': 'mrtydi-v1.1-telugu-test',
'mrtydi-th': 'mrtydi-v1.1-thai-test',
}
from rank_gpt import run_retriever, sliding_windows, write_eval_file
from pyserini.search import LuceneSearcher, get_topics, get_qrels
from tqdm import tqdm
import tempfile
import os
import json
import shutil
openai_key = os.environ.get("OPENAI_API_KEY", None)
for data in ['dl19', 'dl20', 'covid', 'nfc', 'touche', 'dbpedia', 'scifact', 'signal', 'news', 'robust04']:
print('#' * 20)
print(f'Evaluation on {data}')
print('#' * 20)
# Retrieve passages using pyserini BM25.
try:
searcher = LuceneSearcher.from_prebuilt_index(THE_INDEX[data])
topics = get_topics(THE_TOPICS[data] if data != 'dl20' else 'dl20')
qrels = get_qrels(THE_TOPICS[data])
rank_results = run_retriever(topics, searcher, qrels, k=100)
except:
print(f'Failed to retrieve passages for {data}')
continue
# Run sliding window permutation generation
new_results = []
for item in tqdm(rank_results):
new_item = sliding_windows(item, rank_start=0, rank_end=100, window_size=20, step=10,
model_name='gpt-3.5-turbo', api_key=openai_key)
new_results.append(new_item)
# Evaluate nDCG@10
from trec_eval import EvalFunction
# Create an empty text file to write results, and pass the name to eval
temp_file = tempfile.NamedTemporaryFile(delete=False).name
EvalFunction.write_file(new_results, temp_file)
EvalFunction.main(THE_TOPICS[data], temp_file)
for data in ['mrtydi-ar', 'mrtydi-bn', 'mrtydi-fi', 'mrtydi-id', 'mrtydi-ja', 'mrtydi-ko', 'mrtydi-ru', 'mrtydi-sw', 'mrtydi-te', 'mrtydi-th']:
print('#' * 20)
print(f'Evaluation on {data}')
print('#' * 20)
# Retrieve passages using pyserini BM25.
try:
searcher = LuceneSearcher.from_prebuilt_index(THE_INDEX[data])
topics = get_topics(THE_TOPICS[data] if data != 'dl20' else 'dl20')
qrels = get_qrels(THE_TOPICS[data])
rank_results = run_retriever(topics, searcher, qrels, k=100)
rank_results = rank_results[:100]
except:
print(f'Failed to retrieve passages for {data}')
continue
# Run sliding window permutation generation
new_results = []
for item in tqdm(rank_results):
new_item = sliding_windows(item, rank_start=0, rank_end=100, window_size=20, step=10,
model_name='gpt-3.5-turbo', api_key=openai_key)
new_results.append(new_item)
# Evaluate nDCG@10
from trec_eval import EvalFunction
temp_file = tempfile.NamedTemporaryFile(delete=False).name
EvalFunction.write_file(new_results, temp_file)
EvalFunction.main(THE_TOPICS[data], temp_file)