aken12/splade-japanese-v3

Evaluation on MIRACL japanese

These models don't train on the MIRACL training data.

Model	nDCG@10	Recall@1000	Recall@5	Recall@30
BM25	0.369	0.931	-	-
splade-japanese	0.405	0.931	0.406	0.663
splade-japanese-efficient	0.408	0.954	0.419	0.718
splade-japanese-v2	0.580	0.967	0.629	0.844
splade-japanese-v2-doc	0.478	0.930	0.514	0.759
splade-japanese-v3	0.604	0.979	0.647	0.877

*'splade-japanese-v2-doc' model does not require query encoder during inference.

Evaluation on hotchpotch/JQaRA

			JQaRa
		NDCG@10	MRR@10	NDCG@100	MRR@100
splade-japanese-v3		0.505	0.772	0.7	0.775
JaColBERTv2		0.585	0.836	0.753	0.838
JaColBERT		0.549	0.811	0.730	0.814
bge-m3+all		0.576	0.818	0.745	0.820
bg3-m3+dense		0.539	0.785	0.721	0.788
m-e5-large		0.554	0.799	0.731	0.801
m-e5-base		0.471	0.727	0.673	0.731
m-e5-small		0.492	0.729	0.689	0.733
GLuCoSE		0.308	0.518	0.564	0.527
sup-simcse-ja-base		0.324	0.541	0.572	0.550
sup-simcse-ja-large		0.356	0.575	0.596	0.583
fio-base-v0.1		0.372	0.616	0.608	0.622

下のコードを実行すれば，単語拡張や重み付けの確認ができます．

If you'd like to try it out, you can see the expansion of queries or documents by running the code below.

you need to install

!pip install fugashi ipadic unidic-lite

from transformers import AutoModelForMaskedLM,AutoTokenizer
import torch
import numpy as np

model = AutoModelForMaskedLM.from_pretrained("aken12/splade-japanese-v3") 
tokenizer = AutoTokenizer.from_pretrained("aken12/splade-japanese-v3")
vocab_dict = {v: k for k, v in tokenizer.get_vocab().items()}

def encode_query(query): ##query passsage maxlen: 32,180
    query = tokenizer(query, return_tensors="pt")
    output = model(**query, return_dict=True).logits
    output, _ = torch.max(torch.log(1 + torch.relu(output)) * query['attention_mask'].unsqueeze(-1), dim=1)
    return output

with torch.no_grad():
    model_output = encode_query(query="筑波大学では何の研究が行われているか？")

reps = model_output
idx = torch.nonzero(reps[0], as_tuple=False)

dict_splade = {}
for i in idx:
    token_value = reps[0][i[0]].item()
    if token_value > 0:
        token = vocab_dict[int(i[0])]
        dict_splade[token] = float(token_value)

sorted_dict_splade = sorted(dict_splade.items(), key=lambda item: item[1], reverse=True)
for token, value in sorted_dict_splade:
    print(token, value)