diff --git a/docs/results/jinaai/jina-embeddings-v3/summary.json b/docs/results/jinaai/jina-embeddings-v3/summary.json
new file mode 100644
index 0000000..8524862
--- /dev/null
+++ b/docs/results/jinaai/jina-embeddings-v3/summary.json
@@ -0,0 +1,62 @@
+{
+ "Classification": {
+ "amazon_counterfactual_classification": {
+ "macro_f1": 0.7882733929438857
+ },
+ "amazon_review_classification": {
+ "macro_f1": 0.5933239824757218
+ },
+ "massive_intent_classification": {
+ "macro_f1": 0.7765343277120157
+ },
+ "massive_scenario_classification": {
+ "macro_f1": 0.8974174944345525
+ }
+ },
+ "Reranking": {
+ "esci": {
+ "ndcg@10": 0.9271464336251287
+ }
+ },
+ "Retrieval": {
+ "jagovfaqs_22k": {
+ "ndcg@10": 0.7149884473155108
+ },
+ "jaqket": {
+ "ndcg@10": 0.46484206025698144
+ },
+ "mrtydi": {
+ "ndcg@10": 0.4544765083850943
+ },
+ "nlp_journal_abs_intro": {
+ "ndcg@10": 0.9843205562446103
+ },
+ "nlp_journal_title_abs": {
+ "ndcg@10": 0.9561509620323349
+ },
+ "nlp_journal_title_intro": {
+ "ndcg@10": 0.9385000684351988
+ }
+ },
+ "STS": {
+ "jsick": {
+ "spearman": 0.781637470000662
+ },
+ "jsts": {
+ "spearman": 0.8193234425217734
+ }
+ },
+ "Clustering": {
+ "livedoor_news": {
+ "v_measure_score": 0.5472248713636514
+ },
+ "mewsc16": {
+ "v_measure_score": 0.4818974386694296
+ }
+ },
+ "PairClassification": {
+ "paws_x_ja": {
+ "binary_f1": 0.623716814159292
+ }
+ }
+}
diff --git a/leaderboard.md b/leaderboard.md
index 4b05e46..ccd9236 100644
--- a/leaderboard.md
+++ b/leaderboard.md
@@ -7,7 +7,8 @@ The summary shows the average scores within each task. The average score is the
| Model | Avg. | Retrieval | STS | Classification | Reranking | Clustering | PairClassification |
|:----------------------------------------------|:----------|:------------|:----------|:-----------------|:------------|:-------------|:---------------------|
-| OpenAI/text-embedding-3-large | **74.05** | **74.48** | 82.52 | **77.58** | **93.58** | 53.32 | 62.35 |
+| OpenAI/text-embedding-3-large | **74.05** | 74.48 | 82.52 | **77.58** | **93.58** | 53.32 | 62.35 |
+| jinaai/jina-embeddings-v3 | 73.44 | **75.22** | 80.05 | 76.39 | 92.71 | 51.46 | 62.37 |
| cl-nagoya/ruri-large | 73.31 | 73.02 | 83.13 | 77.43 | 92.99 | 51.82 | 62.29 |
| pkshatech/GLuCoSE-base-ja-v2 | 72.23 | 73.36 | 82.96 | 74.21 | 93.01 | 48.65 | 62.37 |
| pkshatech/RoSEtta-base-ja | 72.04 | 73.21 | 81.39 | 72.41 | 92.69 | 53.23 | 61.74 |
@@ -38,11 +39,12 @@ The summary shows the average scores within each task. The average score is the
## Retrieval
| Model | Avg. | jagovfaqs_22k
(ndcg@10) | jaqket
(ndcg@10) | mrtydi
(ndcg@10) | nlp_journal_abs_intro
(ndcg@10) | nlp_journal_title_abs
(ndcg@10) | nlp_journal_title_intro
(ndcg@10) |
|:----------------------------------------------|:----------|:-----------------------------|:----------------------|:----------------------|:-------------------------------------|:-------------------------------------|:---------------------------------------|
-| OpenAI/text-embedding-3-large | **74.48** | 72.41 | 48.21 | 34.88 | **99.33** | 96.55 | **95.47** |
+| jinaai/jina-embeddings-v3 | **75.22** | 71.50 | 46.48 | **45.45** | 98.43 | 95.62 | 93.85 |
+| OpenAI/text-embedding-3-large | 74.48 | 72.41 | 48.21 | 34.88 | **99.33** | 96.55 | **95.47** |
| pkshatech/GLuCoSE-base-ja-v2 | 73.36 | 69.79 | **67.29** | 41.86 | 90.29 | 95.11 | 75.80 |
| pkshatech/RoSEtta-base-ja | 73.21 | 65.96 | 65.33 | 36.73 | 95.54 | 94.08 | 81.63 |
| cl-nagoya/ruri-large | 73.02 | **76.68** | 61.74 | 38.03 | 87.12 | 96.58 | 77.97 |
-| intfloat/multilingual-e5-large | 70.98 | 70.30 | 58.78 | **43.63** | 86.00 | 94.70 | 72.48 |
+| intfloat/multilingual-e5-large | 70.98 | 70.30 | 58.78 | 43.63 | 86.00 | 94.70 | 72.48 |
| cl-nagoya/ruri-base | 69.82 | 74.56 | 50.12 | 35.45 | 86.89 | 96.57 | 75.31 |
| cl-nagoya/ruri-small | 69.41 | 73.65 | 48.44 | 33.43 | 87.69 | **97.17** | 76.09 |
| intfloat/multilingual-e5-base | 68.21 | 65.34 | 50.67 | 38.38 | 87.10 | 94.73 | 73.05 |
@@ -79,6 +81,7 @@ The summary shows the average scores within each task. The average score is the
| pkshatech/RoSEtta-base-ja | 81.39 | 83.83 | 78.95 |
| cl-nagoya/unsup-simcse-ja-large | 80.56 | 80.15 | 80.98 |
| intfloat/multilingual-e5-small | 80.07 | 81.50 | 78.65 |
+| jinaai/jina-embeddings-v3 | 80.05 | 78.16 | 81.93 |
| intfloat/multilingual-e5-base | 79.84 | 81.28 | 78.39 |
| intfloat/multilingual-e5-large | 79.70 | 78.40 | 80.99 |
| OpenAI/text-embedding-3-small | 79.46 | 80.83 | 78.08 |
@@ -104,6 +107,7 @@ The summary shows the average scores within each task. The average score is the
| cl-nagoya/ruri-large | 77.43 | 80.81 | 56.80 | **82.56** | 89.56 |
| pkshatech/GLuCoSE-base-ja | 76.82 | **82.44** | 58.07 | 78.85 | 87.94 |
| oshizo/sbert-jsnli-luke-japanese-base-lite | 76.61 | 79.95 | 57.48 | 80.26 | 88.75 |
+| jinaai/jina-embeddings-v3 | 76.39 | 78.83 | 59.33 | 77.65 | 89.74 |
| cl-nagoya/ruri-small | 76.22 | 79.92 | 55.61 | 81.49 | 87.88 |
| cl-nagoya/ruri-base | 75.58 | 76.66 | 55.76 | 81.41 | 88.49 |
| cl-nagoya/unsup-simcse-ja-large | 74.66 | 76.79 | 55.37 | 79.13 | 87.36 |
@@ -141,6 +145,7 @@ The summary shows the average scores within each task. The average score is the
| OpenAI/text-embedding-3-small | 92.92 | 92.92 |
| cl-nagoya/ruri-base | 92.91 | 92.91 |
| intfloat/multilingual-e5-base | 92.85 | 92.85 |
+| jinaai/jina-embeddings-v3 | 92.71 | 92.71 |
| pkshatech/RoSEtta-base-ja | 92.69 | 92.69 |
| pkshatech/GLuCoSE-base-ja | 91.90 | 91.90 |
| cl-nagoya/sup-simcse-ja-base | 91.83 | 91.83 |
@@ -167,6 +172,7 @@ The summary shows the average scores within each task. The average score is the
| pkshatech/RoSEtta-base-ja | 53.23 | **58.62** | 47.85 |
| cl-nagoya/ruri-large | 51.82 | 51.39 | 52.25 |
| cl-nagoya/sup-simcse-ja-base | 51.79 | 52.67 | 50.91 |
+| jinaai/jina-embeddings-v3 | 51.46 | 54.72 | 48.19 |
| intfloat/multilingual-e5-large | 51.24 | 57.13 | 45.34 |
| cl-nagoya/ruri-small | 51.19 | 50.96 | 51.41 |
| OpenAI/text-embedding-3-small | 51.06 | 54.57 | 47.55 |
@@ -204,6 +210,7 @@ The summary shows the average scores within each task. The average score is the
| cl-nagoya/ruri-base | 62.38 | 62.38 |
| oshizo/sbert-jsnli-luke-japanese-base-lite | 62.38 | 62.38 |
| MU-Kindai/Japanese-DiffCSE-BERT-base | 62.38 | 62.38 |
+| jinaai/jina-embeddings-v3 | 62.37 | 62.37 |
| pkshatech/GLuCoSE-base-ja-v2 | 62.37 | 62.37 |
| MU-Kindai/Japanese-SimCSE-BERT-base-sup | 62.37 | 62.37 |
| MU-Kindai/Japanese-SimCSE-BERT-large-sup | 62.35 | 62.35 |