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"""
This scripts runs the evaluation (dev & test) for the AskUbuntu dataset
Usage:
python eval_askubuntu.py [sbert_model_name_or_path]
"""
from sentence_transformers import SentenceTransformer, LoggingHandler
from sentence_transformers import util, evaluation
import logging
import os
import gzip
import sys
#### Just some code to print debug information to stdout
logging.basicConfig(format='%(asctime)s - %(message)s',
datefmt='%Y-%m-%d %H:%M:%S',
level=logging.INFO,
handlers=[LoggingHandler()])
#### /print debug information to stdout
model = SentenceTransformer(sys.argv[1])
################# Download AskUbuntu and extract training corpus #################
askubuntu_folder = 'askubuntu'
training_corpus = os.path.join(askubuntu_folder, 'train.unsupervised.txt')
## Download the AskUbuntu dataset from https://github.com/taolei87/askubuntu
for filename in ['text_tokenized.txt.gz', 'dev.txt', 'test.txt', 'train_random.txt']:
filepath = os.path.join(askubuntu_folder, filename)
if not os.path.exists(filepath):
util.http_get('https://github.com/taolei87/askubuntu/raw/master/'+filename, filepath)
# Read the corpus
corpus = {}
dev_test_ids = set()
with gzip.open(os.path.join(askubuntu_folder, 'text_tokenized.txt.gz'), 'rt', encoding='utf8') as fIn:
for line in fIn:
splits = line.strip().split("\t")
id = splits[0]
title = splits[1]
corpus[id] = title
# Read dev & test dataset
def read_eval_dataset(filepath):
dataset = []
with open(filepath) as fIn:
for line in fIn:
query_id, relevant_id, candidate_ids, bm25_scores = line.strip().split("\t")
if len(relevant_id) == 0: #Skip examples without relevant entries
continue
relevant_id = relevant_id.split(" ")
candidate_ids = candidate_ids.split(" ")
negative_ids = set(candidate_ids) - set(relevant_id)
dataset.append({
'query': corpus[query_id],
'positive': [corpus[pid] for pid in relevant_id],
'negative': [corpus[pid] for pid in negative_ids]
})
dev_test_ids.add(query_id)
dev_test_ids.update(candidate_ids)
return dataset
dev_dataset = read_eval_dataset(os.path.join(askubuntu_folder, 'dev.txt'))
test_dataset = read_eval_dataset(os.path.join(askubuntu_folder, 'test.txt'))
# Create a dev evaluator
dev_evaluator = evaluation.RerankingEvaluator(dev_dataset, name="AskUbuntu dev")
logging.info("Dev performance before training")
dev_evaluator(model)
test_evaluator = evaluation.RerankingEvaluator(test_dataset, name="AskUbuntu test")
logging.info("Test performance before training")
test_evaluator(model)
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