File size: 4,163 Bytes
d155e36 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 | import os
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
os.environ["HF_HOME"] = "../../cache/hgCache"
os.environ["TRANSFORMERS_CACHE"] = "../../cache/transformersCache/"
import gzip
import logging
import sys
from collections import defaultdict
import numpy as np
import pytrec_eval
import tqdm
import pandas as pd
from pylate import models, rank
from FlagEmbedding import BGEM3FlagModel
datasetnames = [
"fiqa2018",
"climatefever",
"dbpedia",
"fever",
"fiqa2018",
"hotpotqa",
# "msmarco",
"nfcorpus",
"nq",
"quoraretrieval",
"scidocs",
"arguana",
"scifact",
"touche2020",
]
model = BGEM3FlagModel("BAAI/bge-m3", use_fp16=True)
for datasetname in datasetnames:
evalResultsDf = None
dfDocs = pd.read_parquet(
f"datasets/{datasetname}/corpus/train-00000-of-00001.parquet"
).dropna()
dfQueries = pd.read_parquet(
f"datasets/{datasetname}/queries/train-00000-of-00001.parquet"
).dropna()
# Read test queries
queries = []
documents = []
passage_cand = {}
relevant_qid = []
relevant_docs = defaultdict(lambda: defaultdict(int))
# read corpus
newId2oldId_Docs = {}
for i, row in enumerate(dfDocs.values):
documents.append(row[2])
newId2oldId_Docs[i] = str(row[0])
relevant_qid.append(str(row[0]))
# read queries
newId2oldId_Queries = {}
for i, row in enumerate(dfQueries.values):
queries.append(row[2])
newId2oldId_Queries[i] = str(row[0])
for j, rowDoc in enumerate(dfDocs.values):
relevant_docs[str(row[0])][str(rowDoc[0])] = 0
# read qrels
dfQrels = pd.read_parquet(
f"datasets/{datasetname}/qrels/train-00000-of-00001.parquet"
)
for i, row in enumerate(dfQrels.values):
relevant_docs[str(row[0])][str(row[1])] = 1
candidateIds = [[i for i in range(len(documents))]]
queries_result_list = []
run = {}
document_embeddings = model.encode(
documents,
batch_size=4,
max_length=512,
return_dense=True,
return_sparse=True,
return_colbert_vecs=True,
)
for i, query in enumerate(tqdm.tqdm(queries)):
queries_embeddings = model.encode(
[query],
max_length=32,
return_dense=True,
return_sparse=True,
return_colbert_vecs=True,
)
similarities = []
for j in range(len(documents)):
similarities.append(
model.colbert_score(
queries_embeddings["colbert_vecs"][0],
document_embeddings["colbert_vecs"][j],
)
)
run[newId2oldId_Queries[i]] = {}
for j, score in enumerate(similarities):
run[newId2oldId_Queries[i]][newId2oldId_Docs[j]] = float(score)
evaluator = pytrec_eval.RelevanceEvaluator(
relevant_docs, pytrec_eval.supported_measures
)
scores = evaluator.evaluate(run)
def print_line(measure, scope, value):
print("{:25s}{:8s}{:.4f}".format(measure, scope, value))
for query_id, query_measures in sorted(scores.items()):
break
for measure, value in sorted(query_measures.items()):
print_line(measure, query_id, value)
# Scope hack: use query_measures of last item in previous loop to
# figure out all unique measure names.
resultsColumns = ["model name"]
resultsRow = ["bgem3"]
for measure in sorted(query_measures.keys()):
resultsColumns.append(measure)
resultsRow.append(
pytrec_eval.compute_aggregated_measure(
measure, [query_measures[measure] for query_measures in scores.values()]
)
)
if evalResultsDf is None:
evalResultsDf = pd.DataFrame(columns=resultsColumns)
evalResultsDf.loc[-1] = resultsRow
evalResultsDf.index = evalResultsDf.index + 1
evalResultsDf.to_csv(f"results/{datasetname}_bgem3.csv", encoding="utf-8")
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