Update README.md

README.md

@@ -7,6 +7,24 @@ language:
 - ja
 ---
 
+
+
+|                     |     |           | 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     |
+
 ## Evaluation on [MIRACL japanese](https://huggingface.co/datasets/miracl/miracl)
 These models don't train on the MIRACL training data.
 
@@ -22,6 +40,8 @@ These models don't train on the MIRACL training data.
 
 *'splade-japanese-v2-doc' model does not require query encoder during inference.
 
+
+
 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.
@@ -41,7 +61,7 @@ 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):
+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)