diff --git a/models/dummy_model.py b/models/dummy_model.py
index 00243d73adf0abb050ac6a2c713596ccddae53c5..a3f15b29e4af3522e6eb318c49b14aff8c56fb08 100644
--- a/models/dummy_model.py
+++ b/models/dummy_model.py
@@ -76,19 +76,19 @@ class llama3_8b_FewShot(ShopBenchBaseModel):
# self.tokenizer.convert_tokens_to_ids("\\n"),
]
- def average_pool(self,last_hidden_states: Tensor,
- attention_mask: Tensor) -> Tensor:
- last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
- return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
-
+ def get_detailed_instruct(self, task_description: str, query: str) -> str:
+ return f'Instruct: {task_description}\nQuery: {query}'
+
def build_vector_database(self, ):
+ self.embed_model = SentenceTransformer("./models/multilingual-e5-large-instruct")
+
# few shot preprocess
dim = 1024 # Embedding dimension for intfloat/multilingual-e5-large
nlist = 1024 # Number of cluster centroids
quantizer = faiss.IndexFlatIP(dim)
self.index = faiss.IndexIVFFlat(quantizer, dim, nlist, faiss.METRIC_INNER_PRODUCT)
self.index.nprobe = 3
- self.embed_model = SentenceTransformer("./models/intfloat-multilingual-e5-large")
+
self.few_shot_example_text = []
self.fewshot_embeddings = []
with open('./models/sample_example1.jsonl','r',encoding='utf8') as f:
@@ -100,36 +100,55 @@ class llama3_8b_FewShot(ShopBenchBaseModel):
else:
passage = t_data['instruction'] + str(t_data['output']) + '\n'
passage = passage.replace('\\n','\n')
- self.few_shot_example_text.append('passage: ' + passage)
+ self.few_shot_example_text.append(passage)
+
+
+
+ # preprocess retriev index and save trained index
+ # self.fewshot_embeddings = self.embed_model.encode(self.few_shot_example_text, batch_size=128, show_progress_bar=True)
+ # print(f'process few shot example embedding done! {len(self.few_shot_example_text)}')
+ # self.index.train(self.fewshot_embeddings.astype(np.float32))
+ # self.index.add(self.fewshot_embeddings.astype(np.float32))
+ # faiss.write_index(self.index, "./models/index.ivf")
+
self.index = faiss.read_index("./models/index.ivf")
self.metadata = [{"fewshot_examaple": fewshot_examaple} for fewshot_examaple in self.few_shot_example_text]
def predict(self, prompt: str, is_multiple_choice: bool) -> str:
- query_text = 'query: ' + prompt
- query_embed = self.embed_model.encode([query_text])[0]
+ task_description = "Given a online shopping user query, retrieve relevant Question-Answer that similar to the query."
+ query_text = ' ' + prompt
+ query_embed = self.embed_model.encode([self.get_detailed_instruct(task_description, query_text)])[0]
topk = 3
scores, indices = self.index.search(np.array([query_embed]).astype(np.float32), topk)
# Retrieve and process results
-
- if not is_multiple_choice:
- exmaple_prompt = []
- for score, idx in zip(scores[0], indices[0]):
- if score>=0.85:
- fewshot_examaple = self.metadata[idx]["fewshot_examaple"]
- exmaple_prompt.append(fewshot_examaple[9:])
- if len(exmaple_prompt) > 0:
- prompt_example = self.system_prompt + 'Here are some similar questions and answers you can refer to:\n'
- for i in exmaple_prompt:
- prompt_example += i+'\n'
- prompt_example += '\nQuestion:' + prompt
- else:
- prompt_example = self.system_prompt + '\n' + prompt
- print(prompt_example)
+ exmaple_prompt = []
+ for score, idx in zip(scores[0], indices[0]):
+ print(f'score:{score} meta data:{self.metadata[idx]["fewshot_examaple"]}')
+ if score>=0.895:
+ fewshot_examaple = self.metadata[idx]["fewshot_examaple"]
+ exmaple_prompt.append(fewshot_examaple)
+ if len(exmaple_prompt) > 0:
+ prompt_example = self.system_prompt + 'Here are some similar questions and answers you can refer to:\n'
+ for i in exmaple_prompt:
+ prompt_example += i+'\n'
+ prompt_example += 'Now answer the Question:' + prompt
+ else:
+ prompt_example = self.system_prompt + '\nNow answer the Question:' + prompt
+ print(prompt_example)
+ if is_multiple_choice:
+ # todo add retrive few shot example
+ inputs = self.tokenizer.encode(prompt_example, add_special_tokens=False, return_tensors="pt")
+ inputs = inputs.cuda()
+ if is_multiple_choice:
+ generate_ids = self.model.generate(inputs, max_new_tokens=1, temperature=0.1, eos_token_id=self.terminators)
+ result = self.tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+ generation = result[len(prompt_example):]
+ else:
messages = [
{"role": "system", "content": prompt_example[:len(self.system_prompt)]},
{"role": "user", "content": prompt_example[len(self.system_prompt):]},
@@ -141,22 +160,12 @@ class llama3_8b_FewShot(ShopBenchBaseModel):
).to(self.model.device)
outputs = self.model.generate(
input_ids,
- max_new_tokens=138,
+ max_new_tokens=256,
eos_token_id=self.terminators,
do_sample=False,
)
- response = outputs[0][input_ids.shape[-1]:]
- response = self.tokenizer.decode(response, skip_special_tokens=True)
- print(response)
- return response
- else:
- prompt_example = self.system_prompt + '\n' + prompt
- print(prompt_example)
- inputs = self.tokenizer.encode(prompt_example, add_special_tokens=False, return_tensors="pt")
- inputs = inputs.cuda()
- if is_multiple_choice:
- generate_ids = self.model.generate(inputs, max_new_tokens=1, temperature=0.1, eos_token_id=self.terminators)
- result = self.tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
- generation = result[len(prompt_example):]
- print(f'model generate answer : {generation}')
- return generation
\ No newline at end of file
+ generation = outputs[0][input_ids.shape[-1]:]
+ generation = self.tokenizer.decode(generation, skip_special_tokens=True)
+
+ print(f'model generate answer : {generation}')
+ return generation
\ No newline at end of file
diff --git a/models/index.ivf b/models/index.ivf
index dda02e515cc203b56a243e0c710d5aad9bf07c93..14ee93d003fc410fcd6ff77ba6006b6a5cb82ffa 100644
--- a/models/index.ivf
+++ b/models/index.ivf
@@ -1,3 +1,3 @@
version https://git-lfs.github.com/spec/v1
-oid sha256:84468e3f1364e8902cfa4426a11eea174eec7b86a4f33cd0ff0bad233be7c5ee
+oid sha256:c4638cb8d13dc8c9cd52a06f2a3c4ede4e18521d451a3dd7bfe16ff577d997ed
size 130950571
diff --git a/models/intfloat-multilingual-e5-large/config_sentence_transformers.json b/models/intfloat-multilingual-e5-large/config_sentence_transformers.json
deleted file mode 100644
index 27406537d1829fe5924fdd38d8b47b7c4403745e..0000000000000000000000000000000000000000
--- a/models/intfloat-multilingual-e5-large/config_sentence_transformers.json
+++ /dev/null
@@ -1,9 +0,0 @@
-{
- "__version__": {
- "sentence_transformers": "2.7.0",
- "transformers": "4.40.1",
- "pytorch": "2.1.1+cu118"
- },
- "prompts": {},
- "default_prompt_name": null
-}
\ No newline at end of file
diff --git a/models/intfloat-multilingual-e5-large/model.safetensors b/models/intfloat-multilingual-e5-large/model.safetensors
deleted file mode 100644
index 38b5448bc6ad24bd404621eda844115fab9fa0ca..0000000000000000000000000000000000000000
--- a/models/intfloat-multilingual-e5-large/model.safetensors
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:b97ef8b3fe70ff51008184da99b70a0df37ed377bb538a224f3951f2ed388234
-size 2239607176
diff --git a/models/intfloat-multilingual-e5-large/1_Pooling/config.json b/models/multilingual-e5-large-instruct/1_Pooling/config.json
similarity index 100%
rename from models/intfloat-multilingual-e5-large/1_Pooling/config.json
rename to models/multilingual-e5-large-instruct/1_Pooling/config.json
diff --git a/models/intfloat-multilingual-e5-large/README.md b/models/multilingual-e5-large-instruct/README.md
similarity index 63%
rename from models/intfloat-multilingual-e5-large/README.md
rename to models/multilingual-e5-large-instruct/README.md
index bef87884efd3d12dc1790c10d7f4c2ee68ba4f9c..5738062f350d1d88a231a8f5596e827e585e08dd 100644
--- a/models/intfloat-multilingual-e5-large/README.md
+++ b/models/multilingual-e5-large-instruct/README.md
@@ -1,12 +1,10 @@
---
tags:
- mteb
-- Sentence Transformers
-- sentence-similarity
-- feature-extraction
- sentence-transformers
+- transformers
model-index:
-- name: multilingual-e5-large
+- name: multilingual-e5-large-instruct
results:
- task:
type: Classification
@@ -18,11 +16,11 @@ model-index:
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
- value: 79.05970149253731
+ value: 76.23880597014924
- type: ap
- value: 43.486574390835635
+ value: 39.07351965022687
- type: f1
- value: 73.32700092140148
+ value: 70.04836733862683
- task:
type: Classification
dataset:
@@ -33,11 +31,11 @@ model-index:
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
- value: 71.22055674518201
+ value: 66.71306209850107
- type: ap
- value: 81.55756710830498
+ value: 79.01499914759529
- type: f1
- value: 69.28271787752661
+ value: 64.81951817560703
- task:
type: Classification
dataset:
@@ -48,11 +46,11 @@ model-index:
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
- value: 80.41979010494754
+ value: 73.85307346326837
- type: ap
- value: 29.34879922376344
+ value: 22.447519885878737
- type: f1
- value: 67.62475449011278
+ value: 61.0162730745633
- task:
type: Classification
dataset:
@@ -63,11 +61,11 @@ model-index:
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
- value: 77.8372591006424
+ value: 76.04925053533191
- type: ap
- value: 26.557560591210738
+ value: 23.44983217128922
- type: f1
- value: 64.96619417368707
+ value: 62.5723230907759
- task:
type: Classification
dataset:
@@ -78,11 +76,11 @@ model-index:
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
- value: 93.489875
+ value: 96.28742500000001
- type: ap
- value: 90.98758636917603
+ value: 94.8449918887462
- type: f1
- value: 93.48554819717332
+ value: 96.28680923610432
- task:
type: Classification
dataset:
@@ -93,9 +91,9 @@ model-index:
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
- value: 47.564
+ value: 56.716
- type: f1
- value: 46.75122173518047
+ value: 55.76510398266401
- task:
type: Classification
dataset:
@@ -106,9 +104,9 @@ model-index:
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
- value: 45.400000000000006
+ value: 52.99999999999999
- type: f1
- value: 44.17195682400632
+ value: 52.00829994765178
- task:
type: Classification
dataset:
@@ -119,9 +117,9 @@ model-index:
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
- value: 43.068
+ value: 48.806000000000004
- type: f1
- value: 42.38155696855596
+ value: 48.082345914983634
- task:
type: Classification
dataset:
@@ -132,9 +130,9 @@ model-index:
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
- value: 41.89
+ value: 48.507999999999996
- type: f1
- value: 40.84407321682663
+ value: 47.68752844642045
- task:
type: Classification
dataset:
@@ -145,9 +143,9 @@ model-index:
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
- value: 40.120000000000005
+ value: 47.709999999999994
- type: f1
- value: 39.522976223819114
+ value: 47.05870376637181
- task:
type: Classification
dataset:
@@ -158,9 +156,9 @@ model-index:
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
- value: 38.832
+ value: 44.662000000000006
- type: f1
- value: 38.0392533394713
+ value: 43.42371965372771
- task:
type: Retrieval
dataset:
@@ -171,65 +169,65 @@ model-index:
revision: None
metrics:
- type: map_at_1
- value: 30.725
+ value: 31.721
- type: map_at_10
- value: 46.055
+ value: 49.221
- type: map_at_100
- value: 46.900999999999996
+ value: 49.884
- type: map_at_1000
- value: 46.911
+ value: 49.888
- type: map_at_3
- value: 41.548
+ value: 44.31
- type: map_at_5
- value: 44.297
+ value: 47.276
- type: mrr_at_1
- value: 31.152
+ value: 32.432
- type: mrr_at_10
- value: 46.231
+ value: 49.5
- type: mrr_at_100
- value: 47.07
+ value: 50.163000000000004
- type: mrr_at_1000
- value: 47.08
+ value: 50.166
- type: mrr_at_3
- value: 41.738
+ value: 44.618
- type: mrr_at_5
- value: 44.468999999999994
+ value: 47.541
- type: ndcg_at_1
- value: 30.725
+ value: 31.721
- type: ndcg_at_10
- value: 54.379999999999995
+ value: 58.384
- type: ndcg_at_100
- value: 58.138
+ value: 61.111000000000004
- type: ndcg_at_1000
- value: 58.389
+ value: 61.187999999999995
- type: ndcg_at_3
- value: 45.156
+ value: 48.386
- type: ndcg_at_5
- value: 50.123
+ value: 53.708999999999996
- type: precision_at_1
- value: 30.725
+ value: 31.721
- type: precision_at_10
- value: 8.087
+ value: 8.741
- type: precision_at_100
- value: 0.9769999999999999
+ value: 0.991
- type: precision_at_1000
value: 0.1
- type: precision_at_3
- value: 18.54
+ value: 20.057
- type: precision_at_5
- value: 13.542000000000002
+ value: 14.609
- type: recall_at_1
- value: 30.725
+ value: 31.721
- type: recall_at_10
- value: 80.868
+ value: 87.411
- type: recall_at_100
- value: 97.653
+ value: 99.075
- type: recall_at_1000
- value: 99.57300000000001
+ value: 99.644
- type: recall_at_3
- value: 55.619
+ value: 60.171
- type: recall_at_5
- value: 67.71000000000001
+ value: 73.044
- task:
type: Clustering
dataset:
@@ -240,7 +238,7 @@ model-index:
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
- value: 44.30960650674069
+ value: 46.40419580759799
- task:
type: Clustering
dataset:
@@ -251,7 +249,7 @@ model-index:
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
- value: 38.427074197498996
+ value: 40.48593255007969
- task:
type: Reranking
dataset:
@@ -262,9 +260,9 @@ model-index:
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
- value: 60.28270056031872
+ value: 63.889179122289995
- type: mrr
- value: 74.38332673789738
+ value: 77.61146286769556
- task:
type: STS
dataset:
@@ -275,17 +273,17 @@ model-index:
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
- value: 84.05942144105269
+ value: 88.15075203727929
- type: cos_sim_spearman
- value: 82.51212105850809
+ value: 86.9622224570873
- type: euclidean_pearson
- value: 81.95639829909122
+ value: 86.70473853624121
- type: euclidean_spearman
- value: 82.3717564144213
+ value: 86.9622224570873
- type: manhattan_pearson
- value: 81.79273425468256
+ value: 86.21089380980065
- type: manhattan_spearman
- value: 82.20066817871039
+ value: 86.75318154937008
- task:
type: BitextMining
dataset:
@@ -296,13 +294,13 @@ model-index:
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
- value: 99.46764091858039
+ value: 99.65553235908142
- type: f1
- value: 99.37717466945023
+ value: 99.60681976339595
- type: precision
- value: 99.33194154488518
+ value: 99.58246346555325
- type: recall
- value: 99.46764091858039
+ value: 99.65553235908142
- task:
type: BitextMining
dataset:
@@ -313,13 +311,13 @@ model-index:
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
- value: 98.29407880255337
+ value: 99.26260180497468
- type: f1
- value: 98.11248073959938
+ value: 99.14520507740848
- type: precision
- value: 98.02443319392472
+ value: 99.08650671362535
- type: recall
- value: 98.29407880255337
+ value: 99.26260180497468
- task:
type: BitextMining
dataset:
@@ -330,13 +328,13 @@ model-index:
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
- value: 97.79009352268791
+ value: 98.07412538967787
- type: f1
- value: 97.5176076665512
+ value: 97.86629719431936
- type: precision
- value: 97.38136473848286
+ value: 97.76238309664012
- type: recall
- value: 97.79009352268791
+ value: 98.07412538967787
- task:
type: BitextMining
dataset:
@@ -347,13 +345,13 @@ model-index:
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
- value: 99.26276987888363
+ value: 99.42074776197998
- type: f1
- value: 99.20133403545726
+ value: 99.38564156573635
- type: precision
- value: 99.17500438827453
+ value: 99.36808846761454
- type: recall
- value: 99.26276987888363
+ value: 99.42074776197998
- task:
type: Classification
dataset:
@@ -364,9 +362,9 @@ model-index:
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
- value: 84.72727272727273
+ value: 85.73376623376623
- type: f1
- value: 84.67672206031433
+ value: 85.68480707214599
- task:
type: Clustering
dataset:
@@ -377,7 +375,7 @@ model-index:
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
- value: 35.34220182511161
+ value: 40.935218072113855
- task:
type: Clustering
dataset:
@@ -388,7 +386,7 @@ model-index:
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
- value: 33.4987096128766
+ value: 36.276389017675264
- task:
type: Retrieval
dataset:
@@ -399,65 +397,65 @@ model-index:
revision: None
metrics:
- type: map_at_1
- value: 25.558249999999997
+ value: 27.764166666666668
- type: map_at_10
- value: 34.44425000000001
+ value: 37.298166666666674
- type: map_at_100
- value: 35.59833333333333
+ value: 38.530166666666666
- type: map_at_1000
- value: 35.706916666666665
+ value: 38.64416666666667
- type: map_at_3
- value: 31.691749999999995
+ value: 34.484833333333334
- type: map_at_5
- value: 33.252916666666664
+ value: 36.0385
- type: mrr_at_1
- value: 30.252666666666666
+ value: 32.93558333333333
- type: mrr_at_10
- value: 38.60675
+ value: 41.589749999999995
- type: mrr_at_100
- value: 39.42666666666666
+ value: 42.425333333333334
- type: mrr_at_1000
- value: 39.48408333333334
+ value: 42.476333333333336
- type: mrr_at_3
- value: 36.17441666666665
+ value: 39.26825
- type: mrr_at_5
- value: 37.56275
+ value: 40.567083333333336
- type: ndcg_at_1
- value: 30.252666666666666
+ value: 32.93558333333333
- type: ndcg_at_10
- value: 39.683
+ value: 42.706583333333334
- type: ndcg_at_100
- value: 44.68541666666667
+ value: 47.82483333333333
- type: ndcg_at_1000
- value: 46.94316666666668
+ value: 49.95733333333334
- type: ndcg_at_3
- value: 34.961749999999995
+ value: 38.064750000000004
- type: ndcg_at_5
- value: 37.215666666666664
+ value: 40.18158333333333
- type: precision_at_1
- value: 30.252666666666666
+ value: 32.93558333333333
- type: precision_at_10
- value: 6.904166666666667
+ value: 7.459833333333334
- type: precision_at_100
- value: 1.0989999999999995
+ value: 1.1830833333333335
- type: precision_at_1000
- value: 0.14733333333333334
+ value: 0.15608333333333332
- type: precision_at_3
- value: 16.037666666666667
+ value: 17.5235
- type: precision_at_5
- value: 11.413583333333333
+ value: 12.349833333333333
- type: recall_at_1
- value: 25.558249999999997
+ value: 27.764166666666668
- type: recall_at_10
- value: 51.13341666666666
+ value: 54.31775
- type: recall_at_100
- value: 73.08366666666667
+ value: 76.74350000000001
- type: recall_at_1000
- value: 88.79483333333334
+ value: 91.45208333333332
- type: recall_at_3
- value: 37.989083333333326
+ value: 41.23425
- type: recall_at_5
- value: 43.787833333333325
+ value: 46.73983333333334
- task:
type: Retrieval
dataset:
@@ -468,65 +466,65 @@ model-index:
revision: None
metrics:
- type: map_at_1
- value: 10.338
+ value: 12.969
- type: map_at_10
- value: 18.360000000000003
+ value: 21.584999999999997
- type: map_at_100
- value: 19.942
+ value: 23.3
- type: map_at_1000
- value: 20.134
+ value: 23.5
- type: map_at_3
- value: 15.174000000000001
+ value: 18.218999999999998
- type: map_at_5
- value: 16.830000000000002
+ value: 19.983
- type: mrr_at_1
- value: 23.257
+ value: 29.316
- type: mrr_at_10
- value: 33.768
+ value: 40.033
- type: mrr_at_100
- value: 34.707
+ value: 40.96
- type: mrr_at_1000
- value: 34.766000000000005
+ value: 41.001
- type: mrr_at_3
- value: 30.977
+ value: 37.123
- type: mrr_at_5
- value: 32.528
+ value: 38.757999999999996
- type: ndcg_at_1
- value: 23.257
+ value: 29.316
- type: ndcg_at_10
- value: 25.733
+ value: 29.858
- type: ndcg_at_100
- value: 32.288
+ value: 36.756
- type: ndcg_at_1000
- value: 35.992000000000004
+ value: 40.245999999999995
- type: ndcg_at_3
- value: 20.866
+ value: 24.822
- type: ndcg_at_5
- value: 22.612
+ value: 26.565
- type: precision_at_1
- value: 23.257
+ value: 29.316
- type: precision_at_10
- value: 8.124
+ value: 9.186
- type: precision_at_100
- value: 1.518
+ value: 1.6549999999999998
- type: precision_at_1000
- value: 0.219
+ value: 0.22999999999999998
- type: precision_at_3
- value: 15.679000000000002
+ value: 18.436
- type: precision_at_5
- value: 12.117
+ value: 13.876
- type: recall_at_1
- value: 10.338
+ value: 12.969
- type: recall_at_10
- value: 31.154
+ value: 35.142
- type: recall_at_100
- value: 54.161
+ value: 59.143
- type: recall_at_1000
- value: 75.21900000000001
+ value: 78.594
- type: recall_at_3
- value: 19.427
+ value: 22.604
- type: recall_at_5
- value: 24.214
+ value: 27.883000000000003
- task:
type: Retrieval
dataset:
@@ -537,65 +535,65 @@ model-index:
revision: None
metrics:
- type: map_at_1
- value: 8.498
+ value: 8.527999999999999
- type: map_at_10
- value: 19.103
+ value: 17.974999999999998
- type: map_at_100
- value: 27.375
+ value: 25.665
- type: map_at_1000
- value: 28.981
+ value: 27.406000000000002
- type: map_at_3
- value: 13.764999999999999
+ value: 13.017999999999999
- type: map_at_5
- value: 15.950000000000001
+ value: 15.137
- type: mrr_at_1
- value: 65.5
+ value: 62.5
- type: mrr_at_10
- value: 74.53800000000001
+ value: 71.891
- type: mrr_at_100
- value: 74.71799999999999
+ value: 72.294
- type: mrr_at_1000
- value: 74.725
+ value: 72.296
- type: mrr_at_3
- value: 72.792
+ value: 69.958
- type: mrr_at_5
- value: 73.554
+ value: 71.121
- type: ndcg_at_1
- value: 53.37499999999999
+ value: 50.875
- type: ndcg_at_10
- value: 41.286
+ value: 38.36
- type: ndcg_at_100
- value: 45.972
+ value: 44.235
- type: ndcg_at_1000
- value: 53.123
+ value: 52.154
- type: ndcg_at_3
- value: 46.172999999999995
+ value: 43.008
- type: ndcg_at_5
- value: 43.033
+ value: 40.083999999999996
- type: precision_at_1
- value: 65.5
+ value: 62.5
- type: precision_at_10
- value: 32.725
+ value: 30.0
- type: precision_at_100
- value: 10.683
+ value: 10.038
- type: precision_at_1000
- value: 1.978
+ value: 2.0869999999999997
- type: precision_at_3
- value: 50
+ value: 46.833000000000006
- type: precision_at_5
- value: 41.349999999999994
+ value: 38.800000000000004
- type: recall_at_1
- value: 8.498
+ value: 8.527999999999999
- type: recall_at_10
- value: 25.070999999999998
+ value: 23.828
- type: recall_at_100
- value: 52.383
+ value: 52.322
- type: recall_at_1000
- value: 74.91499999999999
+ value: 77.143
- type: recall_at_3
- value: 15.207999999999998
+ value: 14.136000000000001
- type: recall_at_5
- value: 18.563
+ value: 17.761
- task:
type: Classification
dataset:
@@ -606,9 +604,9 @@ model-index:
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
- value: 46.5
+ value: 51.51
- type: f1
- value: 41.93833713984145
+ value: 47.632159862049896
- task:
type: Retrieval
dataset:
@@ -619,65 +617,65 @@ model-index:
revision: None
metrics:
- type: map_at_1
- value: 67.914
+ value: 60.734
- type: map_at_10
- value: 78.10000000000001
+ value: 72.442
- type: map_at_100
- value: 78.333
+ value: 72.735
- type: map_at_1000
- value: 78.346
+ value: 72.75
- type: map_at_3
- value: 76.626
+ value: 70.41199999999999
- type: map_at_5
- value: 77.627
+ value: 71.80499999999999
- type: mrr_at_1
- value: 72.74199999999999
+ value: 65.212
- type: mrr_at_10
- value: 82.414
+ value: 76.613
- type: mrr_at_100
- value: 82.511
+ value: 76.79899999999999
- type: mrr_at_1000
- value: 82.513
+ value: 76.801
- type: mrr_at_3
- value: 81.231
+ value: 74.8
- type: mrr_at_5
- value: 82.065
+ value: 76.12400000000001
- type: ndcg_at_1
- value: 72.74199999999999
+ value: 65.212
- type: ndcg_at_10
- value: 82.806
+ value: 77.988
- type: ndcg_at_100
- value: 83.677
+ value: 79.167
- type: ndcg_at_1000
- value: 83.917
+ value: 79.452
- type: ndcg_at_3
- value: 80.305
+ value: 74.362
- type: ndcg_at_5
- value: 81.843
+ value: 76.666
- type: precision_at_1
- value: 72.74199999999999
+ value: 65.212
- type: precision_at_10
- value: 10.24
+ value: 10.003
- type: precision_at_100
- value: 1.089
+ value: 1.077
- type: precision_at_1000
- value: 0.11299999999999999
+ value: 0.11199999999999999
- type: precision_at_3
- value: 31.268
+ value: 29.518
- type: precision_at_5
- value: 19.706000000000003
+ value: 19.016
- type: recall_at_1
- value: 67.914
+ value: 60.734
- type: recall_at_10
- value: 92.889
+ value: 90.824
- type: recall_at_100
- value: 96.42699999999999
+ value: 95.71600000000001
- type: recall_at_1000
- value: 97.92
+ value: 97.577
- type: recall_at_3
- value: 86.21
+ value: 81.243
- type: recall_at_5
- value: 90.036
+ value: 86.90299999999999
- task:
type: Retrieval
dataset:
@@ -688,65 +686,65 @@ model-index:
revision: None
metrics:
- type: map_at_1
- value: 22.166
+ value: 23.845
- type: map_at_10
- value: 35.57
+ value: 39.281
- type: map_at_100
- value: 37.405
+ value: 41.422
- type: map_at_1000
- value: 37.564
+ value: 41.593
- type: map_at_3
- value: 30.379
+ value: 34.467
- type: map_at_5
- value: 33.324
+ value: 37.017
- type: mrr_at_1
- value: 43.519000000000005
+ value: 47.531
- type: mrr_at_10
- value: 51.556000000000004
+ value: 56.204
- type: mrr_at_100
- value: 52.344
+ value: 56.928999999999995
- type: mrr_at_1000
- value: 52.373999999999995
+ value: 56.962999999999994
- type: mrr_at_3
- value: 48.868
+ value: 54.115
- type: mrr_at_5
- value: 50.319
+ value: 55.373000000000005
- type: ndcg_at_1
- value: 43.519000000000005
+ value: 47.531
- type: ndcg_at_10
- value: 43.803
+ value: 47.711999999999996
- type: ndcg_at_100
- value: 50.468999999999994
+ value: 54.510999999999996
- type: ndcg_at_1000
- value: 53.111
+ value: 57.103
- type: ndcg_at_3
- value: 38.893
+ value: 44.145
- type: ndcg_at_5
- value: 40.653
+ value: 45.032
- type: precision_at_1
- value: 43.519000000000005
+ value: 47.531
- type: precision_at_10
- value: 12.253
+ value: 13.194
- type: precision_at_100
- value: 1.931
+ value: 2.045
- type: precision_at_1000
- value: 0.242
+ value: 0.249
- type: precision_at_3
- value: 25.617
+ value: 29.424
- type: precision_at_5
- value: 19.383
+ value: 21.451
- type: recall_at_1
- value: 22.166
+ value: 23.845
- type: recall_at_10
- value: 51.6
+ value: 54.967
- type: recall_at_100
- value: 76.574
+ value: 79.11399999999999
- type: recall_at_1000
- value: 92.192
+ value: 94.56700000000001
- type: recall_at_3
- value: 34.477999999999994
+ value: 40.256
- type: recall_at_5
- value: 41.835
+ value: 46.215
- task:
type: Retrieval
dataset:
@@ -757,65 +755,65 @@ model-index:
revision: None
metrics:
- type: map_at_1
- value: 39.041
+ value: 37.819
- type: map_at_10
- value: 62.961999999999996
+ value: 60.889
- type: map_at_100
- value: 63.79899999999999
+ value: 61.717999999999996
- type: map_at_1000
- value: 63.854
+ value: 61.778
- type: map_at_3
- value: 59.399
+ value: 57.254000000000005
- type: map_at_5
- value: 61.669
+ value: 59.541
- type: mrr_at_1
- value: 78.082
+ value: 75.638
- type: mrr_at_10
- value: 84.321
+ value: 82.173
- type: mrr_at_100
- value: 84.49600000000001
+ value: 82.362
- type: mrr_at_1000
- value: 84.502
+ value: 82.37
- type: mrr_at_3
- value: 83.421
+ value: 81.089
- type: mrr_at_5
- value: 83.977
+ value: 81.827
- type: ndcg_at_1
- value: 78.082
+ value: 75.638
- type: ndcg_at_10
- value: 71.229
+ value: 69.317
- type: ndcg_at_100
- value: 74.10900000000001
+ value: 72.221
- type: ndcg_at_1000
- value: 75.169
+ value: 73.382
- type: ndcg_at_3
- value: 66.28699999999999
+ value: 64.14
- type: ndcg_at_5
- value: 69.084
+ value: 67.07600000000001
- type: precision_at_1
- value: 78.082
+ value: 75.638
- type: precision_at_10
- value: 14.993
+ value: 14.704999999999998
- type: precision_at_100
- value: 1.7239999999999998
+ value: 1.698
- type: precision_at_1000
- value: 0.186
+ value: 0.185
- type: precision_at_3
- value: 42.737
+ value: 41.394999999999996
- type: precision_at_5
- value: 27.843
+ value: 27.162999999999997
- type: recall_at_1
- value: 39.041
+ value: 37.819
- type: recall_at_10
- value: 74.96300000000001
+ value: 73.52499999999999
- type: recall_at_100
- value: 86.199
+ value: 84.875
- type: recall_at_1000
- value: 93.228
+ value: 92.559
- type: recall_at_3
- value: 64.105
+ value: 62.092999999999996
- type: recall_at_5
- value: 69.608
+ value: 67.907
- task:
type: Classification
dataset:
@@ -826,11 +824,11 @@ model-index:
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
- value: 90.23160000000001
+ value: 94.60079999999999
- type: ap
- value: 85.5674856808308
+ value: 92.67396345347356
- type: f1
- value: 90.18033354786317
+ value: 94.5988098167121
- task:
type: Retrieval
dataset:
@@ -841,65 +839,65 @@ model-index:
revision: None
metrics:
- type: map_at_1
- value: 24.091
+ value: 21.285
- type: map_at_10
- value: 36.753
+ value: 33.436
- type: map_at_100
- value: 37.913000000000004
+ value: 34.63
- type: map_at_1000
- value: 37.958999999999996
+ value: 34.681
- type: map_at_3
- value: 32.818999999999996
+ value: 29.412
- type: map_at_5
- value: 35.171
+ value: 31.715
- type: mrr_at_1
- value: 24.742
+ value: 21.848
- type: mrr_at_10
- value: 37.285000000000004
+ value: 33.979
- type: mrr_at_100
- value: 38.391999999999996
+ value: 35.118
- type: mrr_at_1000
- value: 38.431
+ value: 35.162
- type: mrr_at_3
- value: 33.440999999999995
+ value: 30.036
- type: mrr_at_5
- value: 35.75
+ value: 32.298
- type: ndcg_at_1
- value: 24.742
+ value: 21.862000000000002
- type: ndcg_at_10
- value: 43.698
+ value: 40.43
- type: ndcg_at_100
- value: 49.145
+ value: 46.17
- type: ndcg_at_1000
- value: 50.23800000000001
+ value: 47.412
- type: ndcg_at_3
- value: 35.769
+ value: 32.221
- type: ndcg_at_5
- value: 39.961999999999996
+ value: 36.332
- type: precision_at_1
- value: 24.742
+ value: 21.862000000000002
- type: precision_at_10
- value: 6.7989999999999995
+ value: 6.491
- type: precision_at_100
- value: 0.95
+ value: 0.935
- type: precision_at_1000
value: 0.104
- type: precision_at_3
- value: 15.096000000000002
+ value: 13.744
- type: precision_at_5
- value: 11.183
+ value: 10.331999999999999
- type: recall_at_1
- value: 24.091
+ value: 21.285
- type: recall_at_10
- value: 65.068
+ value: 62.083
- type: recall_at_100
- value: 89.899
+ value: 88.576
- type: recall_at_1000
- value: 98.16
+ value: 98.006
- type: recall_at_3
- value: 43.68
+ value: 39.729
- type: recall_at_5
- value: 53.754999999999995
+ value: 49.608000000000004
- task:
type: Classification
dataset:
@@ -910,9 +908,9 @@ model-index:
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
- value: 93.66621067031465
+ value: 93.92612859097127
- type: f1
- value: 93.49622853272142
+ value: 93.82370333372853
- task:
type: Classification
dataset:
@@ -923,9 +921,9 @@ model-index:
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
- value: 91.94702733164272
+ value: 92.67681036911807
- type: f1
- value: 91.17043441745282
+ value: 92.14191382411472
- task:
type: Classification
dataset:
@@ -936,9 +934,9 @@ model-index:
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
- value: 92.20146764509674
+ value: 92.26817878585723
- type: f1
- value: 91.98359080555608
+ value: 91.92824250337878
- task:
type: Classification
dataset:
@@ -949,9 +947,9 @@ model-index:
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
- value: 88.99780770435328
+ value: 89.96554963983714
- type: f1
- value: 89.19746342724068
+ value: 90.02859329630792
- task:
type: Classification
dataset:
@@ -962,9 +960,9 @@ model-index:
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
- value: 89.78486912871998
+ value: 90.02509860164935
- type: f1
- value: 89.24578823628642
+ value: 89.30665159182062
- task:
type: Classification
dataset:
@@ -975,9 +973,9 @@ model-index:
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
- value: 88.74502712477394
+ value: 87.55515370705244
- type: f1
- value: 89.00297573881542
+ value: 87.94449232331907
- task:
type: Classification
dataset:
@@ -988,9 +986,9 @@ model-index:
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
- value: 77.9046967624259
+ value: 82.4623803009576
- type: f1
- value: 59.36787125785957
+ value: 66.06738378772725
- task:
type: Classification
dataset:
@@ -1001,9 +999,9 @@ model-index:
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
- value: 74.5280360664976
+ value: 79.3716539870386
- type: f1
- value: 57.17723440888718
+ value: 60.37614033396853
- task:
type: Classification
dataset:
@@ -1014,9 +1012,9 @@ model-index:
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
- value: 75.44029352901934
+ value: 80.34022681787857
- type: f1
- value: 54.052855531072964
+ value: 58.302008026952
- task:
type: Classification
dataset:
@@ -1027,9 +1025,9 @@ model-index:
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
- value: 70.5606013153774
+ value: 76.72095208268087
- type: f1
- value: 52.62215934386531
+ value: 59.64524724009049
- task:
type: Classification
dataset:
@@ -1040,9 +1038,9 @@ model-index:
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
- value: 73.11581211903908
+ value: 77.87020437432773
- type: f1
- value: 52.341291845645465
+ value: 57.80202694670567
- task:
type: Classification
dataset:
@@ -1053,9 +1051,9 @@ model-index:
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
- value: 74.28933092224233
+ value: 77.73598553345387
- type: f1
- value: 57.07918745504911
+ value: 58.19628250675031
- task:
type: Classification
dataset:
@@ -1066,9 +1064,9 @@ model-index:
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
- value: 62.38063214525892
+ value: 67.6630800268998
- type: f1
- value: 59.46463723443009
+ value: 65.00996668051691
- task:
type: Classification
dataset:
@@ -1079,9 +1077,9 @@ model-index:
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
- value: 56.06926698049766
+ value: 60.7128446536651
- type: f1
- value: 52.49084283283562
+ value: 57.95860594874963
- task:
type: Classification
dataset:
@@ -1092,9 +1090,9 @@ model-index:
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
- value: 60.74983187626093
+ value: 63.61129791526563
- type: f1
- value: 56.960640620165904
+ value: 59.75328290206483
- task:
type: Classification
dataset:
@@ -1105,9 +1103,9 @@ model-index:
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
- value: 64.86550100874243
+ value: 69.00134498991257
- type: f1
- value: 62.47370548140688
+ value: 67.0230483991802
- task:
type: Classification
dataset:
@@ -1118,9 +1116,9 @@ model-index:
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
- value: 63.971082716879636
+ value: 68.54068594485541
- type: f1
- value: 61.03812421957381
+ value: 65.54604628946976
- task:
type: Classification
dataset:
@@ -1131,9 +1129,9 @@ model-index:
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
- value: 54.98318762609282
+ value: 63.032952252858095
- type: f1
- value: 51.51207916008392
+ value: 58.715741857057104
- task:
type: Classification
dataset:
@@ -1144,9 +1142,9 @@ model-index:
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
- value: 69.45527908540686
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
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type: Classification
dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
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type: Classification
dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
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type: Classification
dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
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type: Classification
dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
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type: Classification
dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
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type: Classification
dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
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type: Clustering
dataset:
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revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
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type: Clustering
dataset:
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revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
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type: Reranking
dataset:
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revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
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- task:
type: Retrieval
dataset:
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revision: None
metrics:
- type: map_at_1
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- task:
type: Retrieval
dataset:
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revision: None
metrics:
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- type: recall_at_5
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- task:
type: Retrieval
dataset:
@@ -2565,65 +2563,65 @@ model-index:
revision: None
metrics:
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value: 0.157
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- type: precision_at_5
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- type: recall_at_1
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- type: recall_at_10
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- type: recall_at_100
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- type: recall_at_1000
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- type: recall_at_3
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- type: recall_at_5
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- task:
type: Clustering
dataset:
@@ -2634,7 +2632,7 @@ model-index:
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
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- task:
type: Clustering
dataset:
@@ -2645,7 +2643,7 @@ model-index:
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
- value: 63.216287629895994
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- task:
type: Retrieval
dataset:
@@ -2656,65 +2654,65 @@ model-index:
revision: None
metrics:
- type: map_at_1
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- type: recall_at_1
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- type: recall_at_100
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+ value: 42.17
- type: recall_at_1000
- value: 68.812
+ value: 70.12
- type: recall_at_3
- value: 9.508
+ value: 10.288
- type: recall_at_5
- value: 12.983
+ value: 14.072000000000001
- task:
type: STS
dataset:
@@ -2725,17 +2723,17 @@ model-index:
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
- value: 83.967008785134
+ value: 85.84209174935282
- type: cos_sim_spearman
- value: 80.23142141101837
+ value: 81.73248048438833
- type: euclidean_pearson
- value: 81.20166064704539
+ value: 83.02810070308149
- type: euclidean_spearman
- value: 80.18961335654585
+ value: 81.73248295679514
- type: manhattan_pearson
- value: 81.13925443187625
+ value: 82.95368060376002
- type: manhattan_spearman
- value: 80.07948723044424
+ value: 81.60277910998718
- task:
type: STS
dataset:
@@ -2746,17 +2744,17 @@ model-index:
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
- value: 86.94262461316023
+ value: 88.52628804556943
- type: cos_sim_spearman
- value: 80.01596278563865
+ value: 82.5713913555672
- type: euclidean_pearson
- value: 83.80799622922581
+ value: 85.8796774746988
- type: euclidean_spearman
- value: 79.94984954947103
+ value: 82.57137506803424
- type: manhattan_pearson
- value: 83.68473841756281
+ value: 85.79671002960058
- type: manhattan_spearman
- value: 79.84990707951822
+ value: 82.49445981618027
- task:
type: STS
dataset:
@@ -2767,17 +2765,17 @@ model-index:
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
- value: 80.57346443146068
+ value: 86.23682503505542
- type: cos_sim_spearman
- value: 81.54689837570866
+ value: 87.15008956711806
- type: euclidean_pearson
- value: 81.10909881516007
+ value: 86.79805401524959
- type: euclidean_spearman
- value: 81.56746243261762
+ value: 87.15008956711806
- type: manhattan_pearson
- value: 80.87076036186582
+ value: 86.65298502699244
- type: manhattan_spearman
- value: 81.33074987964402
+ value: 86.97677821948562
- task:
type: STS
dataset:
@@ -2788,17 +2786,17 @@ model-index:
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
- value: 79.54733787179849
+ value: 85.63370304677802
- type: cos_sim_spearman
- value: 77.72202105610411
+ value: 84.97105553540318
- type: euclidean_pearson
- value: 78.9043595478849
+ value: 85.28896108687721
- type: euclidean_spearman
- value: 77.93422804309435
+ value: 84.97105553540318
- type: manhattan_pearson
- value: 78.58115121621368
+ value: 85.09663190337331
- type: manhattan_spearman
- value: 77.62508135122033
+ value: 84.79126831644619
- task:
type: STS
dataset:
@@ -2809,17 +2807,17 @@ model-index:
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
- value: 88.59880017237558
+ value: 90.2614838800733
- type: cos_sim_spearman
- value: 89.31088630824758
+ value: 91.0509162991835
- type: euclidean_pearson
- value: 88.47069261564656
+ value: 90.33098317533373
- type: euclidean_spearman
- value: 89.33581971465233
+ value: 91.05091625871644
- type: manhattan_pearson
- value: 88.40774264100956
+ value: 90.26250435151107
- type: manhattan_spearman
- value: 89.28657485627835
+ value: 90.97999594417519
- task:
type: STS
dataset:
@@ -2830,939 +2828,372 @@ model-index:
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
- value: 84.08055117917084
+ value: 85.80480973335091
- type: cos_sim_spearman
- value: 85.78491813080304
+ value: 87.313695492969
- type: euclidean_pearson
- value: 84.99329155500392
+ value: 86.49267251576939
- type: euclidean_spearman
- value: 85.76728064677287
+ value: 87.313695492969
- type: manhattan_pearson
- value: 84.87947428989587
+ value: 86.44019901831935
- type: manhattan_spearman
- value: 85.62429454917464
+ value: 87.24205395460392
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
- name: MTEB STS17 (ko-ko)
- config: ko-ko
+ name: MTEB STS17 (en-en)
+ config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
- value: 82.14190939287384
+ value: 90.05662789380672
- type: cos_sim_spearman
- value: 82.27331573306041
+ value: 90.02759424426651
- type: euclidean_pearson
- value: 81.891896953716
+ value: 90.4042483422981
- type: euclidean_spearman
- value: 82.37695542955998
+ value: 90.02759424426651
- type: manhattan_pearson
- value: 81.73123869460504
+ value: 90.51446975000226
- type: manhattan_spearman
- value: 82.19989168441421
+ value: 90.08832889933616
- task:
type: STS
dataset:
- type: mteb/sts17-crosslingual-sts
- name: MTEB STS17 (ar-ar)
- config: ar-ar
+ type: mteb/sts22-crosslingual-sts
+ name: MTEB STS22 (en)
+ config: en
split: test
- revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
+ revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
- value: 76.84695301843362
+ value: 67.5975528273532
- type: cos_sim_spearman
- value: 77.87790986014461
+ value: 67.62969861411354
- type: euclidean_pearson
- value: 76.91981583106315
+ value: 69.224275734323
- type: euclidean_spearman
- value: 77.88154772749589
+ value: 67.62969861411354
- type: manhattan_pearson
- value: 76.94953277451093
+ value: 69.3761447059927
- type: manhattan_spearman
- value: 77.80499230728604
+ value: 67.90921005611467
- task:
type: STS
dataset:
- type: mteb/sts17-crosslingual-sts
- name: MTEB STS17 (en-ar)
- config: en-ar
+ type: mteb/stsbenchmark-sts
+ name: MTEB STSBenchmark
+ config: default
split: test
- revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
+ revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
- value: 75.44657840482016
+ value: 87.11244327231684
- type: cos_sim_spearman
- value: 75.05531095119674
+ value: 88.37902438979035
- type: euclidean_pearson
- value: 75.88161755829299
+ value: 87.86054279847336
- type: euclidean_spearman
- value: 74.73176238219332
+ value: 88.37902438979035
- type: manhattan_pearson
- value: 75.63984765635362
+ value: 87.77257757320378
- type: manhattan_spearman
- value: 74.86476440770737
+ value: 88.25208966098123
- task:
- type: STS
+ type: Reranking
dataset:
- type: mteb/sts17-crosslingual-sts
- name: MTEB STS17 (en-de)
- config: en-de
+ type: mteb/scidocs-reranking
+ name: MTEB SciDocsRR
+ config: default
split: test
- revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
+ revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- - type: cos_sim_pearson
- value: 85.64700140524133
- - type: cos_sim_spearman
- value: 86.16014210425672
- - type: euclidean_pearson
- value: 86.49086860843221
- - type: euclidean_spearman
- value: 86.09729326815614
- - type: manhattan_pearson
- value: 86.43406265125513
- - type: manhattan_spearman
- value: 86.17740150939994
+ - type: map
+ value: 85.87174608143563
+ - type: mrr
+ value: 96.12836872640794
- task:
- type: STS
+ type: Retrieval
dataset:
- type: mteb/sts17-crosslingual-sts
- name: MTEB STS17 (en-en)
- config: en-en
+ type: scifact
+ name: MTEB SciFact
+ config: default
split: test
- revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
+ revision: None
metrics:
- - type: cos_sim_pearson
- value: 87.91170098764921
- - type: cos_sim_spearman
- value: 88.12437004058931
- - type: euclidean_pearson
- value: 88.81828254494437
- - type: euclidean_spearman
- value: 88.14831794572122
- - type: manhattan_pearson
- value: 88.93442183448961
- - type: manhattan_spearman
- value: 88.15254630778304
+ - type: map_at_1
+ value: 57.760999999999996
+ - type: map_at_10
+ value: 67.258
+ - type: map_at_100
+ value: 67.757
+ - type: map_at_1000
+ value: 67.78800000000001
+ - type: map_at_3
+ value: 64.602
+ - type: map_at_5
+ value: 65.64
+ - type: mrr_at_1
+ value: 60.667
+ - type: mrr_at_10
+ value: 68.441
+ - type: mrr_at_100
+ value: 68.825
+ - type: mrr_at_1000
+ value: 68.853
+ - type: mrr_at_3
+ value: 66.444
+ - type: mrr_at_5
+ value: 67.26100000000001
+ - type: ndcg_at_1
+ value: 60.667
+ - type: ndcg_at_10
+ value: 71.852
+ - type: ndcg_at_100
+ value: 73.9
+ - type: ndcg_at_1000
+ value: 74.628
+ - type: ndcg_at_3
+ value: 67.093
+ - type: ndcg_at_5
+ value: 68.58
+ - type: precision_at_1
+ value: 60.667
+ - type: precision_at_10
+ value: 9.6
+ - type: precision_at_100
+ value: 1.0670000000000002
+ - type: precision_at_1000
+ value: 0.11199999999999999
+ - type: precision_at_3
+ value: 26.111
+ - type: precision_at_5
+ value: 16.733
+ - type: recall_at_1
+ value: 57.760999999999996
+ - type: recall_at_10
+ value: 84.967
+ - type: recall_at_100
+ value: 93.833
+ - type: recall_at_1000
+ value: 99.333
+ - type: recall_at_3
+ value: 71.589
+ - type: recall_at_5
+ value: 75.483
- task:
- type: STS
+ type: PairClassification
dataset:
- type: mteb/sts17-crosslingual-sts
- name: MTEB STS17 (en-tr)
- config: en-tr
+ type: mteb/sprintduplicatequestions-pairclassification
+ name: MTEB SprintDuplicateQuestions
+ config: default
split: test
- revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
+ revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- - type: cos_sim_pearson
- value: 72.91390577997292
- - type: cos_sim_spearman
- value: 71.22979457536074
- - type: euclidean_pearson
- value: 74.40314008106749
- - type: euclidean_spearman
- value: 72.54972136083246
- - type: manhattan_pearson
- value: 73.85687539530218
- - type: manhattan_spearman
- value: 72.09500771742637
+ - type: cos_sim_accuracy
+ value: 99.66633663366336
+ - type: cos_sim_ap
+ value: 91.17685358899108
+ - type: cos_sim_f1
+ value: 82.16818642350559
+ - type: cos_sim_precision
+ value: 83.26488706365504
+ - type: cos_sim_recall
+ value: 81.10000000000001
+ - type: dot_accuracy
+ value: 99.66633663366336
+ - type: dot_ap
+ value: 91.17663411119032
+ - type: dot_f1
+ value: 82.16818642350559
+ - type: dot_precision
+ value: 83.26488706365504
+ - type: dot_recall
+ value: 81.10000000000001
+ - type: euclidean_accuracy
+ value: 99.66633663366336
+ - type: euclidean_ap
+ value: 91.17685189882275
+ - type: euclidean_f1
+ value: 82.16818642350559
+ - type: euclidean_precision
+ value: 83.26488706365504
+ - type: euclidean_recall
+ value: 81.10000000000001
+ - type: manhattan_accuracy
+ value: 99.66633663366336
+ - type: manhattan_ap
+ value: 91.2241619496737
+ - type: manhattan_f1
+ value: 82.20472440944883
+ - type: manhattan_precision
+ value: 86.51933701657458
+ - type: manhattan_recall
+ value: 78.3
+ - type: max_accuracy
+ value: 99.66633663366336
+ - type: max_ap
+ value: 91.2241619496737
+ - type: max_f1
+ value: 82.20472440944883
- task:
- type: STS
+ type: Clustering
dataset:
- type: mteb/sts17-crosslingual-sts
- name: MTEB STS17 (es-en)
- config: es-en
+ type: mteb/stackexchange-clustering
+ name: MTEB StackExchangeClustering
+ config: default
split: test
- revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
+ revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- - type: cos_sim_pearson
- value: 80.9301067983089
- - type: cos_sim_spearman
- value: 80.74989828346473
- - type: euclidean_pearson
- value: 81.36781301814257
- - type: euclidean_spearman
- value: 80.9448819964426
- - type: manhattan_pearson
- value: 81.0351322685609
- - type: manhattan_spearman
- value: 80.70192121844177
+ - type: v_measure
+ value: 66.85101268897951
- task:
- type: STS
+ type: Clustering
dataset:
- type: mteb/sts17-crosslingual-sts
- name: MTEB STS17 (es-es)
- config: es-es
+ type: mteb/stackexchange-clustering-p2p
+ name: MTEB StackExchangeClusteringP2P
+ config: default
split: test
- revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
+ revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- - type: cos_sim_pearson
- value: 87.13820465980005
- - type: cos_sim_spearman
- value: 86.73532498758757
- - type: euclidean_pearson
- value: 87.21329451846637
- - type: euclidean_spearman
- value: 86.57863198601002
- - type: manhattan_pearson
- value: 87.06973713818554
- - type: manhattan_spearman
- value: 86.47534918791499
+ - type: v_measure
+ value: 42.461184054706905
- task:
- type: STS
+ type: Reranking
dataset:
- type: mteb/sts17-crosslingual-sts
- name: MTEB STS17 (fr-en)
- config: fr-en
+ type: mteb/stackoverflowdupquestions-reranking
+ name: MTEB StackOverflowDupQuestions
+ config: default
split: test
- revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
+ revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- - type: cos_sim_pearson
- value: 85.48720108904415
- - type: cos_sim_spearman
- value: 85.62221757068387
- - type: euclidean_pearson
- value: 86.1010129512749
- - type: euclidean_spearman
- value: 85.86580966509942
- - type: manhattan_pearson
- value: 86.26800938808971
- - type: manhattan_spearman
- value: 85.88902721678429
+ - type: map
+ value: 51.44542568873886
+ - type: mrr
+ value: 52.33656151854681
- task:
- type: STS
+ type: Summarization
dataset:
- type: mteb/sts17-crosslingual-sts
- name: MTEB STS17 (it-en)
- config: it-en
+ type: mteb/summeval
+ name: MTEB SummEval
+ config: default
split: test
- revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
+ revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
- value: 83.98021347333516
+ value: 30.75982974997539
- type: cos_sim_spearman
- value: 84.53806553803501
- - type: euclidean_pearson
- value: 84.61483347248364
- - type: euclidean_spearman
- value: 85.14191408011702
- - type: manhattan_pearson
- value: 84.75297588825967
- - type: manhattan_spearman
- value: 85.33176753669242
+ value: 30.385405026539914
+ - type: dot_pearson
+ value: 30.75982433546523
+ - type: dot_spearman
+ value: 30.385405026539914
- task:
- type: STS
+ type: Retrieval
dataset:
- type: mteb/sts17-crosslingual-sts
- name: MTEB STS17 (nl-en)
- config: nl-en
+ type: trec-covid
+ name: MTEB TRECCOVID
+ config: default
split: test
- revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
+ revision: None
metrics:
- - type: cos_sim_pearson
- value: 84.51856644893233
- - type: cos_sim_spearman
- value: 85.27510748506413
- - type: euclidean_pearson
- value: 85.09886861540977
- - type: euclidean_spearman
- value: 85.62579245860887
- - type: manhattan_pearson
- value: 84.93017860464607
- - type: manhattan_spearman
- value: 85.5063988898453
+ - type: map_at_1
+ value: 0.22799999999999998
+ - type: map_at_10
+ value: 2.064
+ - type: map_at_100
+ value: 13.056000000000001
+ - type: map_at_1000
+ value: 31.747999999999998
+ - type: map_at_3
+ value: 0.67
+ - type: map_at_5
+ value: 1.097
+ - type: mrr_at_1
+ value: 90.0
+ - type: mrr_at_10
+ value: 94.667
+ - type: mrr_at_100
+ value: 94.667
+ - type: mrr_at_1000
+ value: 94.667
+ - type: mrr_at_3
+ value: 94.667
+ - type: mrr_at_5
+ value: 94.667
+ - type: ndcg_at_1
+ value: 86.0
+ - type: ndcg_at_10
+ value: 82.0
+ - type: ndcg_at_100
+ value: 64.307
+ - type: ndcg_at_1000
+ value: 57.023999999999994
+ - type: ndcg_at_3
+ value: 85.816
+ - type: ndcg_at_5
+ value: 84.904
+ - type: precision_at_1
+ value: 90.0
+ - type: precision_at_10
+ value: 85.8
+ - type: precision_at_100
+ value: 66.46
+ - type: precision_at_1000
+ value: 25.202
+ - type: precision_at_3
+ value: 90.0
+ - type: precision_at_5
+ value: 89.2
+ - type: recall_at_1
+ value: 0.22799999999999998
+ - type: recall_at_10
+ value: 2.235
+ - type: recall_at_100
+ value: 16.185
+ - type: recall_at_1000
+ value: 53.620999999999995
+ - type: recall_at_3
+ value: 0.7040000000000001
+ - type: recall_at_5
+ value: 1.172
- task:
- type: STS
+ type: BitextMining
dataset:
- type: mteb/sts22-crosslingual-sts
- name: MTEB STS22 (en)
- config: en
+ type: mteb/tatoeba-bitext-mining
+ name: MTEB Tatoeba (sqi-eng)
+ config: sqi-eng
split: test
- revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
+ revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- - type: cos_sim_pearson
- value: 62.581573200584195
- - type: cos_sim_spearman
- value: 63.05503590247928
- - type: euclidean_pearson
- value: 63.652564812602094
- - type: euclidean_spearman
- value: 62.64811520876156
- - type: manhattan_pearson
- value: 63.506842893061076
- - type: manhattan_spearman
- value: 62.51289573046917
+ - type: accuracy
+ value: 97.39999999999999
+ - type: f1
+ value: 96.75
+ - type: precision
+ value: 96.45
+ - type: recall
+ value: 97.39999999999999
- task:
- type: STS
+ type: BitextMining
dataset:
- type: mteb/sts22-crosslingual-sts
- name: MTEB STS22 (de)
- config: de
+ type: mteb/tatoeba-bitext-mining
+ name: MTEB Tatoeba (fry-eng)
+ config: fry-eng
split: test
- revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
- metrics:
- - type: cos_sim_pearson
- value: 48.2248801729127
- - type: cos_sim_spearman
- value: 56.5936604678561
- - type: euclidean_pearson
- value: 43.98149464089
- - type: euclidean_spearman
- value: 56.108561882423615
- - type: manhattan_pearson
- value: 43.86880305903564
- - type: manhattan_spearman
- value: 56.04671150510166
- - task:
- type: STS
- dataset:
- type: mteb/sts22-crosslingual-sts
- name: MTEB STS22 (es)
- config: es
- split: test
- revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
- metrics:
- - type: cos_sim_pearson
- value: 55.17564527009831
- - type: cos_sim_spearman
- value: 64.57978560979488
- - type: euclidean_pearson
- value: 58.8818330154583
- - type: euclidean_spearman
- value: 64.99214839071281
- - type: manhattan_pearson
- value: 58.72671436121381
- - type: manhattan_spearman
- value: 65.10713416616109
- - task:
- type: STS
- dataset:
- type: mteb/sts22-crosslingual-sts
- name: MTEB STS22 (pl)
- config: pl
- split: test
- revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
- metrics:
- - type: cos_sim_pearson
- value: 26.772131864023297
- - type: cos_sim_spearman
- value: 34.68200792408681
- - type: euclidean_pearson
- value: 16.68082419005441
- - type: euclidean_spearman
- value: 34.83099932652166
- - type: manhattan_pearson
- value: 16.52605949659529
- - type: manhattan_spearman
- value: 34.82075801399475
- - task:
- type: STS
- dataset:
- type: mteb/sts22-crosslingual-sts
- name: MTEB STS22 (tr)
- config: tr
- split: test
- revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
- metrics:
- - type: cos_sim_pearson
- value: 54.42415189043831
- - type: cos_sim_spearman
- value: 63.54594264576758
- - type: euclidean_pearson
- value: 57.36577498297745
- - type: euclidean_spearman
- value: 63.111466379158074
- - type: manhattan_pearson
- value: 57.584543715873885
- - type: manhattan_spearman
- value: 63.22361054139183
- - task:
- type: STS
- dataset:
- type: mteb/sts22-crosslingual-sts
- name: MTEB STS22 (ar)
- config: ar
- split: test
- revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
- metrics:
- - type: cos_sim_pearson
- value: 47.55216762405518
- - type: cos_sim_spearman
- value: 56.98670142896412
- - type: euclidean_pearson
- value: 50.15318757562699
- - type: euclidean_spearman
- value: 56.524941926541906
- - type: manhattan_pearson
- value: 49.955618528674904
- - type: manhattan_spearman
- value: 56.37102209240117
- - task:
- type: STS
- dataset:
- type: mteb/sts22-crosslingual-sts
- name: MTEB STS22 (ru)
- config: ru
- split: test
- revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
- metrics:
- - type: cos_sim_pearson
- value: 49.20540980338571
- - type: cos_sim_spearman
- value: 59.9009453504406
- - type: euclidean_pearson
- value: 49.557749853620535
- - type: euclidean_spearman
- value: 59.76631621172456
- - type: manhattan_pearson
- value: 49.62340591181147
- - type: manhattan_spearman
- value: 59.94224880322436
- - task:
- type: STS
- dataset:
- type: mteb/sts22-crosslingual-sts
- name: MTEB STS22 (zh)
- config: zh
- split: test
- revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
- metrics:
- - type: cos_sim_pearson
- value: 51.508169956576985
- - type: cos_sim_spearman
- value: 66.82461565306046
- - type: euclidean_pearson
- value: 56.2274426480083
- - type: euclidean_spearman
- value: 66.6775323848333
- - type: manhattan_pearson
- value: 55.98277796300661
- - type: manhattan_spearman
- value: 66.63669848497175
- - task:
- type: STS
- dataset:
- type: mteb/sts22-crosslingual-sts
- name: MTEB STS22 (fr)
- config: fr
- split: test
- revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
- metrics:
- - type: cos_sim_pearson
- value: 72.86478788045507
- - type: cos_sim_spearman
- value: 76.7946552053193
- - type: euclidean_pearson
- value: 75.01598530490269
- - type: euclidean_spearman
- value: 76.83618917858281
- - type: manhattan_pearson
- value: 74.68337628304332
- - type: manhattan_spearman
- value: 76.57480204017773
- - task:
- type: STS
- dataset:
- type: mteb/sts22-crosslingual-sts
- name: MTEB STS22 (de-en)
- config: de-en
- split: test
- revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
- metrics:
- - type: cos_sim_pearson
- value: 55.922619099401984
- - type: cos_sim_spearman
- value: 56.599362477240774
- - type: euclidean_pearson
- value: 56.68307052369783
- - type: euclidean_spearman
- value: 54.28760436777401
- - type: manhattan_pearson
- value: 56.67763566500681
- - type: manhattan_spearman
- value: 53.94619541711359
- - task:
- type: STS
- dataset:
- type: mteb/sts22-crosslingual-sts
- name: MTEB STS22 (es-en)
- config: es-en
- split: test
- revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
- metrics:
- - type: cos_sim_pearson
- value: 66.74357206710913
- - type: cos_sim_spearman
- value: 72.5208244925311
- - type: euclidean_pearson
- value: 67.49254562186032
- - type: euclidean_spearman
- value: 72.02469076238683
- - type: manhattan_pearson
- value: 67.45251772238085
- - type: manhattan_spearman
- value: 72.05538819984538
- - task:
- type: STS
- dataset:
- type: mteb/sts22-crosslingual-sts
- name: MTEB STS22 (it)
- config: it
- split: test
- revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
- metrics:
- - type: cos_sim_pearson
- value: 71.25734330033191
- - type: cos_sim_spearman
- value: 76.98349083946823
- - type: euclidean_pearson
- value: 73.71642838667736
- - type: euclidean_spearman
- value: 77.01715504651384
- - type: manhattan_pearson
- value: 73.61712711868105
- - type: manhattan_spearman
- value: 77.01392571153896
- - task:
- type: STS
- dataset:
- type: mteb/sts22-crosslingual-sts
- name: MTEB STS22 (pl-en)
- config: pl-en
- split: test
- revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
- metrics:
- - type: cos_sim_pearson
- value: 63.18215462781212
- - type: cos_sim_spearman
- value: 65.54373266117607
- - type: euclidean_pearson
- value: 64.54126095439005
- - type: euclidean_spearman
- value: 65.30410369102711
- - type: manhattan_pearson
- value: 63.50332221148234
- - type: manhattan_spearman
- value: 64.3455878104313
- - task:
- type: STS
- dataset:
- type: mteb/sts22-crosslingual-sts
- name: MTEB STS22 (zh-en)
- config: zh-en
- split: test
- revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
- metrics:
- - type: cos_sim_pearson
- value: 62.30509221440029
- - type: cos_sim_spearman
- value: 65.99582704642478
- - type: euclidean_pearson
- value: 63.43818859884195
- - type: euclidean_spearman
- value: 66.83172582815764
- - type: manhattan_pearson
- value: 63.055779168508764
- - type: manhattan_spearman
- value: 65.49585020501449
- - task:
- type: STS
- dataset:
- type: mteb/sts22-crosslingual-sts
- name: MTEB STS22 (es-it)
- config: es-it
- split: test
- revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
- metrics:
- - type: cos_sim_pearson
- value: 59.587830825340404
- - type: cos_sim_spearman
- value: 68.93467614588089
- - type: euclidean_pearson
- value: 62.3073527367404
- - type: euclidean_spearman
- value: 69.69758171553175
- - type: manhattan_pearson
- value: 61.9074580815789
- - type: manhattan_spearman
- value: 69.57696375597865
- - task:
- type: STS
- dataset:
- type: mteb/sts22-crosslingual-sts
- name: MTEB STS22 (de-fr)
- config: de-fr
- split: test
- revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
- metrics:
- - type: cos_sim_pearson
- value: 57.143220125577066
- - type: cos_sim_spearman
- value: 67.78857859159226
- - type: euclidean_pearson
- value: 55.58225107923733
- - type: euclidean_spearman
- value: 67.80662907184563
- - type: manhattan_pearson
- value: 56.24953502726514
- - type: manhattan_spearman
- value: 67.98262125431616
- - task:
- type: STS
- dataset:
- type: mteb/sts22-crosslingual-sts
- name: MTEB STS22 (de-pl)
- config: de-pl
- split: test
- revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
- metrics:
- - type: cos_sim_pearson
- value: 21.826928900322066
- - type: cos_sim_spearman
- value: 49.578506634400405
- - type: euclidean_pearson
- value: 27.939890138843214
- - type: euclidean_spearman
- value: 52.71950519136242
- - type: manhattan_pearson
- value: 26.39878683847546
- - type: manhattan_spearman
- value: 47.54609580342499
- - task:
- type: STS
- dataset:
- type: mteb/sts22-crosslingual-sts
- name: MTEB STS22 (fr-pl)
- config: fr-pl
- split: test
- revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
- metrics:
- - type: cos_sim_pearson
- value: 57.27603854632001
- - type: cos_sim_spearman
- value: 50.709255283710995
- - type: euclidean_pearson
- value: 59.5419024445929
- - type: euclidean_spearman
- value: 50.709255283710995
- - type: manhattan_pearson
- value: 59.03256832438492
- - type: manhattan_spearman
- value: 61.97797868009122
- - task:
- type: STS
- dataset:
- type: mteb/stsbenchmark-sts
- name: MTEB STSBenchmark
- config: default
- split: test
- revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
- metrics:
- - type: cos_sim_pearson
- value: 85.00757054859712
- - type: cos_sim_spearman
- value: 87.29283629622222
- - type: euclidean_pearson
- value: 86.54824171775536
- - type: euclidean_spearman
- value: 87.24364730491402
- - type: manhattan_pearson
- value: 86.5062156915074
- - type: manhattan_spearman
- value: 87.15052170378574
- - task:
- type: Reranking
- dataset:
- type: mteb/scidocs-reranking
- name: MTEB SciDocsRR
- config: default
- split: test
- revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
- metrics:
- - type: map
- value: 82.03549357197389
- - type: mrr
- value: 95.05437645143527
- - task:
- type: Retrieval
- dataset:
- type: scifact
- name: MTEB SciFact
- config: default
- split: test
- revision: None
- metrics:
- - type: map_at_1
- value: 57.260999999999996
- - type: map_at_10
- value: 66.259
- - type: map_at_100
- value: 66.884
- - type: map_at_1000
- value: 66.912
- - type: map_at_3
- value: 63.685
- - type: map_at_5
- value: 65.35499999999999
- - type: mrr_at_1
- value: 60.333000000000006
- - type: mrr_at_10
- value: 67.5
- - type: mrr_at_100
- value: 68.013
- - type: mrr_at_1000
- value: 68.038
- - type: mrr_at_3
- value: 65.61099999999999
- - type: mrr_at_5
- value: 66.861
- - type: ndcg_at_1
- value: 60.333000000000006
- - type: ndcg_at_10
- value: 70.41
- - type: ndcg_at_100
- value: 73.10600000000001
- - type: ndcg_at_1000
- value: 73.846
- - type: ndcg_at_3
- value: 66.133
- - type: ndcg_at_5
- value: 68.499
- - type: precision_at_1
- value: 60.333000000000006
- - type: precision_at_10
- value: 9.232999999999999
- - type: precision_at_100
- value: 1.0630000000000002
- - type: precision_at_1000
- value: 0.11299999999999999
- - type: precision_at_3
- value: 25.667
- - type: precision_at_5
- value: 17.067
- - type: recall_at_1
- value: 57.260999999999996
- - type: recall_at_10
- value: 81.94399999999999
- - type: recall_at_100
- value: 93.867
- - type: recall_at_1000
- value: 99.667
- - type: recall_at_3
- value: 70.339
- - type: recall_at_5
- value: 76.25
- - task:
- type: PairClassification
- dataset:
- type: mteb/sprintduplicatequestions-pairclassification
- name: MTEB SprintDuplicateQuestions
- config: default
- split: test
- revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
- metrics:
- - type: cos_sim_accuracy
- value: 99.74356435643564
- - type: cos_sim_ap
- value: 93.13411948212683
- - type: cos_sim_f1
- value: 86.80521991300147
- - type: cos_sim_precision
- value: 84.00374181478017
- - type: cos_sim_recall
- value: 89.8
- - type: dot_accuracy
- value: 99.67920792079208
- - type: dot_ap
- value: 89.27277565444479
- - type: dot_f1
- value: 83.9276990718124
- - type: dot_precision
- value: 82.04393505253104
- - type: dot_recall
- value: 85.9
- - type: euclidean_accuracy
- value: 99.74257425742574
- - type: euclidean_ap
- value: 93.17993008259062
- - type: euclidean_f1
- value: 86.69396110542476
- - type: euclidean_precision
- value: 88.78406708595388
- - type: euclidean_recall
- value: 84.7
- - type: manhattan_accuracy
- value: 99.74257425742574
- - type: manhattan_ap
- value: 93.14413755550099
- - type: manhattan_f1
- value: 86.82483594144371
- - type: manhattan_precision
- value: 87.66564729867483
- - type: manhattan_recall
- value: 86
- - type: max_accuracy
- value: 99.74356435643564
- - type: max_ap
- value: 93.17993008259062
- - type: max_f1
- value: 86.82483594144371
- - task:
- type: Clustering
- dataset:
- type: mteb/stackexchange-clustering
- name: MTEB StackExchangeClustering
- config: default
- split: test
- revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
- metrics:
- - type: v_measure
- value: 57.525863806168566
- - task:
- type: Clustering
- dataset:
- type: mteb/stackexchange-clustering-p2p
- name: MTEB StackExchangeClusteringP2P
- config: default
- split: test
- revision: 815ca46b2622cec33ccafc3735d572c266efdb44
- metrics:
- - type: v_measure
- value: 32.68850574423839
- - task:
- type: Reranking
- dataset:
- type: mteb/stackoverflowdupquestions-reranking
- name: MTEB StackOverflowDupQuestions
- config: default
- split: test
- revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
- metrics:
- - type: map
- value: 49.71580650644033
- - type: mrr
- value: 50.50971903913081
- - task:
- type: Summarization
- dataset:
- type: mteb/summeval
- name: MTEB SummEval
- config: default
- split: test
- revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
- metrics:
- - type: cos_sim_pearson
- value: 29.152190498799484
- - type: cos_sim_spearman
- value: 29.686180371952727
- - type: dot_pearson
- value: 27.248664793816342
- - type: dot_spearman
- value: 28.37748983721745
- - task:
- type: Retrieval
- dataset:
- type: trec-covid
- name: MTEB TRECCOVID
- config: default
- split: test
- revision: None
- metrics:
- - type: map_at_1
- value: 0.20400000000000001
- - type: map_at_10
- value: 1.6209999999999998
- - type: map_at_100
- value: 9.690999999999999
- - type: map_at_1000
- value: 23.733
- - type: map_at_3
- value: 0.575
- - type: map_at_5
- value: 0.885
- - type: mrr_at_1
- value: 78
- - type: mrr_at_10
- value: 86.56700000000001
- - type: mrr_at_100
- value: 86.56700000000001
- - type: mrr_at_1000
- value: 86.56700000000001
- - type: mrr_at_3
- value: 85.667
- - type: mrr_at_5
- value: 86.56700000000001
- - type: ndcg_at_1
- value: 76
- - type: ndcg_at_10
- value: 71.326
- - type: ndcg_at_100
- value: 54.208999999999996
- - type: ndcg_at_1000
- value: 49.252
- - type: ndcg_at_3
- value: 74.235
- - type: ndcg_at_5
- value: 73.833
- - type: precision_at_1
- value: 78
- - type: precision_at_10
- value: 74.8
- - type: precision_at_100
- value: 55.50000000000001
- - type: precision_at_1000
- value: 21.836
- - type: precision_at_3
- value: 78
- - type: precision_at_5
- value: 78
- - type: recall_at_1
- value: 0.20400000000000001
- - type: recall_at_10
- value: 1.894
- - type: recall_at_100
- value: 13.245999999999999
- - type: recall_at_1000
- value: 46.373
- - type: recall_at_3
- value: 0.613
- - type: recall_at_5
- value: 0.991
- - task:
- type: BitextMining
- dataset:
- type: mteb/tatoeba-bitext-mining
- name: MTEB Tatoeba (sqi-eng)
- config: sqi-eng
- split: test
- revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
- metrics:
- - type: accuracy
- value: 95.89999999999999
- - type: f1
- value: 94.69999999999999
- - type: precision
- value: 94.11666666666667
- - type: recall
- value: 95.89999999999999
- - task:
- type: BitextMining
- dataset:
- type: mteb/tatoeba-bitext-mining
- name: MTEB Tatoeba (fry-eng)
- config: fry-eng
- split: test
- revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
+ revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
- value: 68.20809248554913
+ value: 85.54913294797689
- type: f1
- value: 63.431048720066066
+ value: 82.46628131021194
- type: precision
- value: 61.69143958161298
+ value: 81.1175337186898
- type: recall
- value: 68.20809248554913
+ value: 85.54913294797689
- task:
type: BitextMining
dataset:
@@ -3773,13 +3204,13 @@ model-index:
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
- value: 71.21951219512195
+ value: 81.21951219512195
- type: f1
- value: 66.82926829268293
+ value: 77.33333333333334
- type: precision
- value: 65.1260162601626
+ value: 75.54878048780488
- type: recall
- value: 71.21951219512195
+ value: 81.21951219512195
- task:
type: BitextMining
dataset:
@@ -3790,13 +3221,13 @@ model-index:
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
- value: 97.2
+ value: 98.6
- type: f1
- value: 96.26666666666667
+ value: 98.26666666666665
- type: precision
- value: 95.8
+ value: 98.1
- type: recall
- value: 97.2
+ value: 98.6
- task:
type: BitextMining
dataset:
@@ -3807,13 +3238,13 @@ model-index:
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
- value: 99.3
+ value: 99.5
- type: f1
- value: 99.06666666666666
+ value: 99.33333333333333
- type: precision
- value: 98.95
+ value: 99.25
- type: recall
- value: 99.3
+ value: 99.5
- task:
type: BitextMining
dataset:
@@ -3824,13 +3255,13 @@ model-index:
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
- value: 97.39999999999999
+ value: 97.8
- type: f1
- value: 96.63333333333333
+ value: 97.2
- type: precision
- value: 96.26666666666668
+ value: 96.89999999999999
- type: recall
- value: 97.39999999999999
+ value: 97.8
- task:
type: BitextMining
dataset:
@@ -3841,13 +3272,13 @@ model-index:
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
- value: 96
+ value: 97.8
- type: f1
- value: 94.86666666666666
+ value: 97.18333333333334
- type: precision
- value: 94.31666666666668
+ value: 96.88333333333333
- type: recall
- value: 96
+ value: 97.8
- task:
type: BitextMining
dataset:
@@ -3858,13 +3289,13 @@ model-index:
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
- value: 47.01492537313433
+ value: 77.61194029850746
- type: f1
- value: 40.178867566927266
+ value: 72.81094527363183
- type: precision
- value: 38.179295828549556
+ value: 70.83333333333333
- type: recall
- value: 47.01492537313433
+ value: 77.61194029850746
- task:
type: BitextMining
dataset:
@@ -3875,13 +3306,13 @@ model-index:
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
- value: 86.5
+ value: 93.7
- type: f1
- value: 83.62537480063796
+ value: 91.91666666666667
- type: precision
- value: 82.44555555555554
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
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type: BitextMining
dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
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type: BitextMining
dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
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- type: recall
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type: BitextMining
dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
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type: BitextMining
dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
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- type: recall
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type: BitextMining
dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
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- type: recall
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type: BitextMining
dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
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type: BitextMining
dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
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type: BitextMining
dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
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type: BitextMining
dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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- type: recall
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type: BitextMining
dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
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type: BitextMining
dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
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- type: recall
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type: BitextMining
dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
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- type: recall
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type: BitextMining
dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
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type: BitextMining
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
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- type: f1
- value: 81.75853018372703
+ value: 89.76377952755905
- type: precision
- value: 80.34120734908137
+ value: 88.71391076115485
- type: recall
- value: 85.03937007874016
+ value: 92.1259842519685
- task:
type: BitextMining
dataset:
@@ -5456,13 +4887,13 @@ model-index:
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
- value: 88.3
+ value: 94.1
- type: f1
- value: 85.5
+ value: 92.49
- type: precision
- value: 84.25833333333334
+ value: 91.725
- type: recall
- value: 88.3
+ value: 94.1
- task:
type: BitextMining
dataset:
@@ -5473,13 +4904,13 @@ model-index:
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
- value: 65.51246537396122
+ value: 77.5623268698061
- type: f1
- value: 60.02297410192148
+ value: 73.27364463791058
- type: precision
- value: 58.133467727289236
+ value: 71.51947852086357
- type: recall
- value: 65.51246537396122
+ value: 77.5623268698061
- task:
type: BitextMining
dataset:
@@ -5490,13 +4921,13 @@ model-index:
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
- value: 96
+ value: 97.39999999999999
- type: f1
- value: 94.89
+ value: 96.56666666666666
- type: precision
- value: 94.39166666666667
+ value: 96.16666666666667
- type: recall
- value: 96
+ value: 97.39999999999999
- task:
type: BitextMining
dataset:
@@ -5507,13 +4938,13 @@ model-index:
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
- value: 57.692307692307686
+ value: 66.34615384615384
- type: f1
- value: 53.162393162393165
+ value: 61.092032967032964
- type: precision
- value: 51.70673076923077
+ value: 59.27197802197802
- type: recall
- value: 57.692307692307686
+ value: 66.34615384615384
- task:
type: BitextMining
dataset:
@@ -5524,13 +4955,13 @@ model-index:
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
- value: 91.60000000000001
+ value: 94.89999999999999
- type: f1
- value: 89.21190476190475
+ value: 93.41190476190476
- type: precision
- value: 88.08666666666667
+ value: 92.7
- type: recall
- value: 91.60000000000001
+ value: 94.89999999999999
- task:
type: BitextMining
dataset:
@@ -5541,13 +4972,13 @@ model-index:
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
- value: 88
+ value: 93.10000000000001
- type: f1
- value: 85.47
+ value: 91.10000000000001
- type: precision
- value: 84.43266233766234
+ value: 90.13333333333333
- type: recall
- value: 88
+ value: 93.10000000000001
- task:
type: BitextMining
dataset:
@@ -5558,13 +4989,13 @@ model-index:
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
- value: 92.7
+ value: 93.7
- type: f1
- value: 90.64999999999999
+ value: 91.97333333333334
- type: precision
- value: 89.68333333333332
+ value: 91.14166666666667
- type: recall
- value: 92.7
+ value: 93.7
- task:
type: BitextMining
dataset:
@@ -5575,13 +5006,13 @@ model-index:
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
- value: 80.30660377358491
+ value: 92.21698113207547
- type: f1
- value: 76.33044137466307
+ value: 90.3796046720575
- type: precision
- value: 74.78970125786164
+ value: 89.56367924528303
- type: recall
- value: 80.30660377358491
+ value: 92.21698113207547
- task:
type: BitextMining
dataset:
@@ -5592,13 +5023,13 @@ model-index:
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
- value: 96.39999999999999
+ value: 97.6
- type: f1
- value: 95.44
+ value: 96.91666666666667
- type: precision
- value: 94.99166666666666
+ value: 96.6
- type: recall
- value: 96.39999999999999
+ value: 97.6
- task:
type: BitextMining
dataset:
@@ -5609,13 +5040,13 @@ model-index:
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
- value: 96.53284671532847
+ value: 97.44525547445255
- type: f1
- value: 95.37712895377129
+ value: 96.71532846715328
- type: precision
- value: 94.7992700729927
+ value: 96.35036496350365
- type: recall
- value: 96.53284671532847
+ value: 97.44525547445255
- task:
type: BitextMining
dataset:
@@ -5626,13 +5057,13 @@ model-index:
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
- value: 89
+ value: 94.1
- type: f1
- value: 86.23190476190476
+ value: 92.34000000000002
- type: precision
- value: 85.035
+ value: 91.49166666666667
- type: recall
- value: 89
+ value: 94.1
- task:
type: Retrieval
dataset:
@@ -5643,65 +5074,65 @@ model-index:
revision: None
metrics:
- type: map_at_1
- value: 2.585
+ value: 3.2910000000000004
- type: map_at_10
- value: 9.012
+ value: 10.373000000000001
- type: map_at_100
- value: 14.027000000000001
+ value: 15.612
- type: map_at_1000
- value: 15.565000000000001
+ value: 17.06
- type: map_at_3
- value: 5.032
+ value: 6.119
- type: map_at_5
- value: 6.657
+ value: 7.917000000000001
- type: mrr_at_1
- value: 28.571
+ value: 44.897999999999996
- type: mrr_at_10
- value: 45.377
+ value: 56.054
- type: mrr_at_100
- value: 46.119
+ value: 56.82000000000001
- type: mrr_at_1000
- value: 46.127
+ value: 56.82000000000001
- type: mrr_at_3
- value: 41.156
+ value: 52.381
- type: mrr_at_5
- value: 42.585
+ value: 53.81
- type: ndcg_at_1
- value: 27.551
+ value: 42.857
- type: ndcg_at_10
- value: 23.395
+ value: 27.249000000000002
- type: ndcg_at_100
- value: 33.342
+ value: 36.529
- type: ndcg_at_1000
- value: 45.523
+ value: 48.136
- type: ndcg_at_3
- value: 25.158
+ value: 33.938
- type: ndcg_at_5
- value: 23.427
+ value: 29.951
- type: precision_at_1
- value: 28.571
+ value: 44.897999999999996
- type: precision_at_10
- value: 21.429000000000002
+ value: 22.653000000000002
- type: precision_at_100
- value: 6.714
+ value: 7.000000000000001
- type: precision_at_1000
- value: 1.473
+ value: 1.48
- type: precision_at_3
- value: 27.211000000000002
+ value: 32.653
- type: precision_at_5
- value: 24.490000000000002
+ value: 27.755000000000003
- type: recall_at_1
- value: 2.585
+ value: 3.2910000000000004
- type: recall_at_10
- value: 15.418999999999999
+ value: 16.16
- type: recall_at_100
- value: 42.485
+ value: 43.908
- type: recall_at_1000
- value: 79.536
+ value: 79.823
- type: recall_at_3
- value: 6.239999999999999
+ value: 7.156
- type: recall_at_5
- value: 8.996
+ value: 10.204
- task:
type: Classification
dataset:
@@ -5712,11 +5143,11 @@ model-index:
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
- value: 71.3234
+ value: 71.05879999999999
- type: ap
- value: 14.361688653847423
+ value: 14.609748142799111
- type: f1
- value: 54.819068624319044
+ value: 54.878956295843096
- task:
type: Classification
dataset:
@@ -5727,9 +5158,9 @@ model-index:
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
- value: 61.97792869269949
+ value: 64.61799660441426
- type: f1
- value: 62.28965628513728
+ value: 64.8698191961434
- task:
type: Clustering
dataset:
@@ -5740,7 +5171,7 @@ model-index:
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
- value: 38.90540145385218
+ value: 51.32860036611885
- task:
type: PairClassification
dataset:
@@ -5751,51 +5182,51 @@ model-index:
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
- value: 86.53513739047506
+ value: 88.34714192048638
- type: cos_sim_ap
- value: 75.27741586677557
+ value: 80.26732975975634
- type: cos_sim_f1
- value: 69.18792902473774
+ value: 73.53415148134374
- type: cos_sim_precision
- value: 67.94708725515136
+ value: 69.34767360299276
- type: cos_sim_recall
- value: 70.47493403693932
+ value: 78.25857519788919
- type: dot_accuracy
- value: 84.7052512368123
+ value: 88.34714192048638
- type: dot_ap
- value: 69.36075482849378
+ value: 80.26733698491206
- type: dot_f1
- value: 64.44688376631296
+ value: 73.53415148134374
- type: dot_precision
- value: 59.92288500793831
+ value: 69.34767360299276
- type: dot_recall
- value: 69.70976253298153
+ value: 78.25857519788919
- type: euclidean_accuracy
- value: 86.60666388508076
+ value: 88.34714192048638
- type: euclidean_ap
- value: 75.47512772621097
+ value: 80.26734337771738
- type: euclidean_f1
- value: 69.413872536473
+ value: 73.53415148134374
- type: euclidean_precision
- value: 67.39562624254472
+ value: 69.34767360299276
- type: euclidean_recall
- value: 71.55672823218997
+ value: 78.25857519788919
- type: manhattan_accuracy
- value: 86.52917684925792
+ value: 88.30541813196639
- type: manhattan_ap
- value: 75.34000110496703
+ value: 80.19415808104145
- type: manhattan_f1
- value: 69.28489190226429
+ value: 73.55143870713441
- type: manhattan_precision
- value: 67.24608889992551
+ value: 73.25307511122743
- type: manhattan_recall
- value: 71.45118733509234
+ value: 73.85224274406332
- type: max_accuracy
- value: 86.60666388508076
+ value: 88.34714192048638
- type: max_ap
- value: 75.47512772621097
+ value: 80.26734337771738
- type: max_f1
- value: 69.413872536473
+ value: 73.55143870713441
- task:
type: PairClassification
dataset:
@@ -5806,51 +5237,51 @@ model-index:
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
- value: 89.01695967710637
+ value: 89.81061047075717
- type: cos_sim_ap
- value: 85.8298270742901
+ value: 87.11747055081017
- type: cos_sim_f1
- value: 78.46988128389272
+ value: 80.04355498817256
- type: cos_sim_precision
- value: 74.86017897091722
+ value: 78.1165262000733
- type: cos_sim_recall
- value: 82.44533415460425
+ value: 82.06806282722513
- type: dot_accuracy
- value: 88.19420188613343
+ value: 89.81061047075717
- type: dot_ap
- value: 83.82679165901324
+ value: 87.11746902745236
- type: dot_f1
- value: 76.55833777304208
+ value: 80.04355498817256
- type: dot_precision
- value: 75.6884875846501
+ value: 78.1165262000733
- type: dot_recall
- value: 77.44841392054204
+ value: 82.06806282722513
- type: euclidean_accuracy
- value: 89.03054294252338
+ value: 89.81061047075717
- type: euclidean_ap
- value: 85.89089555185325
+ value: 87.11746919324248
- type: euclidean_f1
- value: 78.62997658079624
+ value: 80.04355498817256
- type: euclidean_precision
- value: 74.92329149232914
+ value: 78.1165262000733
- type: euclidean_recall
- value: 82.72251308900523
+ value: 82.06806282722513
- type: manhattan_accuracy
- value: 89.0266620095471
+ value: 89.79508673885202
- type: manhattan_ap
- value: 85.86458997929147
+ value: 87.11074390832218
- type: manhattan_f1
- value: 78.50685331000291
+ value: 80.13002540726349
- type: manhattan_precision
- value: 74.5499861534201
+ value: 77.83826945412311
- type: manhattan_recall
- value: 82.90729904527257
+ value: 82.56082537727133
- type: max_accuracy
- value: 89.03054294252338
+ value: 89.81061047075717
- type: max_ap
- value: 85.89089555185325
+ value: 87.11747055081017
- type: max_f1
- value: 78.62997658079624
+ value: 80.13002540726349
language:
- multilingual
- af
@@ -5949,7 +5380,7 @@ language:
license: mit
---
-## Multilingual-E5-large
+## Multilingual-E5-large-instruct
[Multilingual E5 Text Embeddings: A Technical Report](https://arxiv.org/pdf/2402.05672).
Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024
@@ -5958,7 +5389,9 @@ This model has 24 layers and the embedding size is 1024.
## Usage
-Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset.
+Below are examples to encode queries and passages from the MS-MARCO passage ranking dataset.
+
+### Transformers
```python
import torch.nn.functional as F
@@ -5972,16 +5405,24 @@ def average_pool(last_hidden_states: Tensor,
last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
+def get_detailed_instruct(task_description: str, query: str) -> str:
+ return f'Instruct: {task_description}\nQuery: {query}'
-# Each input text should start with "query: " or "passage: ", even for non-English texts.
-# For tasks other than retrieval, you can simply use the "query: " prefix.
-input_texts = ['query: how much protein should a female eat',
- 'query: å—瓜的家常åšæ³•',
- "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
- "passage: 1.清炒å—ç“œä¸ åŽŸæ–™:å«©å—ç“œåŠä¸ª 调料:葱ã€ç›ã€ç™½ç³–ã€é¸¡ç²¾ åšæ³•: 1ã€å—瓜用刀薄薄的削去表é¢ä¸€å±‚çš®,用勺å刮去瓤 2ã€æ“¦æˆç»†ä¸(没有擦èœæ¿å°±ç”¨åˆ€æ…¢æ…¢åˆ‡æˆç»†ä¸) 3ã€é”…烧çƒæ”¾æ²¹,入葱花煸出香味 4ã€å…¥å—ç“œä¸å¿«é€Ÿç¿»ç‚’一分钟左å³,放ç›ã€ä¸€ç‚¹ç™½ç³–和鸡精调味出锅 2.香葱炒å—ç“œ 原料:å—ç“œ1åª è°ƒæ–™:香葱ã€è’œæœ«ã€æ©„榄油ã€ç› åšæ³•: 1ã€å°†å—瓜去皮,切æˆç‰‡ 2ã€æ²¹é”…8æˆçƒåŽ,将蒜末放入爆香 3ã€çˆ†é¦™åŽ,å°†å—瓜片放入,翻炒 4ã€åœ¨ç¿»ç‚’çš„åŒæ—¶,å¯ä»¥ä¸æ—¶åœ°å¾€é”…é‡ŒåŠ æ°´,但ä¸è¦å¤ªå¤š 5ã€æ”¾å…¥ç›,炒匀 6ã€å—瓜差ä¸å¤šè½¯å’Œç»µäº†ä¹‹åŽ,å°±å¯ä»¥å…³ç« 7ã€æ’’入香葱,å³å¯å‡ºé”…"]
+# Each query must come with a one-sentence instruction that describes the task
+task = 'Given a web search query, retrieve relevant passages that answer the query'
+queries = [
+ get_detailed_instruct(task, 'how much protein should a female eat'),
+ get_detailed_instruct(task, 'å—瓜的家常åšæ³•')
+]
+# No need to add instruction for retrieval documents
+documents = [
+ "As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
+ "1.清炒å—ç“œä¸ åŽŸæ–™:å«©å—ç“œåŠä¸ª 调料:葱ã€ç›ã€ç™½ç³–ã€é¸¡ç²¾ åšæ³•: 1ã€å—瓜用刀薄薄的削去表é¢ä¸€å±‚çš®,用勺å刮去瓤 2ã€æ“¦æˆç»†ä¸(没有擦èœæ¿å°±ç”¨åˆ€æ…¢æ…¢åˆ‡æˆç»†ä¸) 3ã€é”…烧çƒæ”¾æ²¹,入葱花煸出香味 4ã€å…¥å—ç“œä¸å¿«é€Ÿç¿»ç‚’一分钟左å³,放ç›ã€ä¸€ç‚¹ç™½ç³–和鸡精调味出锅 2.香葱炒å—ç“œ 原料:å—ç“œ1åª è°ƒæ–™:香葱ã€è’œæœ«ã€æ©„榄油ã€ç› åšæ³•: 1ã€å°†å—瓜去皮,切æˆç‰‡ 2ã€æ²¹é”…8æˆçƒåŽ,将蒜末放入爆香 3ã€çˆ†é¦™åŽ,å°†å—瓜片放入,翻炒 4ã€åœ¨ç¿»ç‚’çš„åŒæ—¶,å¯ä»¥ä¸æ—¶åœ°å¾€é”…é‡ŒåŠ æ°´,但ä¸è¦å¤ªå¤š 5ã€æ”¾å…¥ç›,炒匀 6ã€å—瓜差ä¸å¤šè½¯å’Œç»µäº†ä¹‹åŽ,å°±å¯ä»¥å…³ç« 7ã€æ’’入香葱,å³å¯å‡ºé”…"
+]
+input_texts = queries + documents
-tokenizer = AutoTokenizer.from_pretrained('intfloat/multilingual-e5-large')
-model = AutoModel.from_pretrained('intfloat/multilingual-e5-large')
+tokenizer = AutoTokenizer.from_pretrained('intfloat/multilingual-e5-large-instruct')
+model = AutoModel.from_pretrained('intfloat/multilingual-e5-large-instruct')
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')
@@ -5993,6 +5434,36 @@ embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())
+# => [[91.92852783203125, 67.580322265625], [70.3814468383789, 92.1330795288086]]
+```
+
+### Sentence Transformers
+
+```python
+from sentence_transformers import SentenceTransformer
+
+def get_detailed_instruct(task_description: str, query: str) -> str:
+ return f'Instruct: {task_description}\nQuery: {query}'
+
+# Each query must come with a one-sentence instruction that describes the task
+task = 'Given a web search query, retrieve relevant passages that answer the query'
+queries = [
+ get_detailed_instruct(task, 'how much protein should a female eat'),
+ get_detailed_instruct(task, 'å—瓜的家常åšæ³•')
+]
+# No need to add instruction for retrieval documents
+documents = [
+ "As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
+ "1.清炒å—ç“œä¸ åŽŸæ–™:å«©å—ç“œåŠä¸ª 调料:葱ã€ç›ã€ç™½ç³–ã€é¸¡ç²¾ åšæ³•: 1ã€å—瓜用刀薄薄的削去表é¢ä¸€å±‚çš®,用勺å刮去瓤 2ã€æ“¦æˆç»†ä¸(没有擦èœæ¿å°±ç”¨åˆ€æ…¢æ…¢åˆ‡æˆç»†ä¸) 3ã€é”…烧çƒæ”¾æ²¹,入葱花煸出香味 4ã€å…¥å—ç“œä¸å¿«é€Ÿç¿»ç‚’一分钟左å³,放ç›ã€ä¸€ç‚¹ç™½ç³–和鸡精调味出锅 2.香葱炒å—ç“œ 原料:å—ç“œ1åª è°ƒæ–™:香葱ã€è’œæœ«ã€æ©„榄油ã€ç› åšæ³•: 1ã€å°†å—瓜去皮,切æˆç‰‡ 2ã€æ²¹é”…8æˆçƒåŽ,将蒜末放入爆香 3ã€çˆ†é¦™åŽ,å°†å—瓜片放入,翻炒 4ã€åœ¨ç¿»ç‚’çš„åŒæ—¶,å¯ä»¥ä¸æ—¶åœ°å¾€é”…é‡ŒåŠ æ°´,但ä¸è¦å¤ªå¤š 5ã€æ”¾å…¥ç›,炒匀 6ã€å—瓜差ä¸å¤šè½¯å’Œç»µäº†ä¹‹åŽ,å°±å¯ä»¥å…³ç« 7ã€æ’’入香葱,å³å¯å‡ºé”…"
+]
+input_texts = queries + documents
+
+model = SentenceTransformer('intfloat/multilingual-e5-large-instruct')
+
+embeddings = model.encode(input_texts, convert_to_tensor=True, normalize_embeddings=True)
+scores = (embeddings[:2] @ embeddings[2:].T) * 100
+print(scores.tolist())
+# [[91.92853546142578, 67.5802993774414], [70.38143157958984, 92.13307189941406]]
```
## Supported Languages
@@ -6006,91 +5477,26 @@ but low-resource languages may see performance degradation.
**Initialization**: [xlm-roberta-large](https://huggingface.co/xlm-roberta-large)
-**First stage**: contrastive pre-training with weak supervision
+**First stage**: contrastive pre-training with 1 billion weakly supervised text pairs.
-| Dataset | Weak supervision | # of text pairs |
-|--------------------------------------------------------------------------------------------------------|---------------------------------------|-----------------|
-| Filtered [mC4](https://huggingface.co/datasets/mc4) | (title, page content) | 1B |
-| [CC News](https://huggingface.co/datasets/intfloat/multilingual_cc_news) | (title, news content) | 400M |
-| [NLLB](https://huggingface.co/datasets/allenai/nllb) | translation pairs | 2.4B |
-| [Wikipedia](https://huggingface.co/datasets/intfloat/wikipedia) | (hierarchical section title, passage) | 150M |
-| Filtered [Reddit](https://www.reddit.com/) | (comment, response) | 800M |
-| [S2ORC](https://github.com/allenai/s2orc) | (title, abstract) and citation pairs | 100M |
-| [Stackexchange](https://stackexchange.com/) | (question, answer) | 50M |
-| [xP3](https://huggingface.co/datasets/bigscience/xP3) | (input prompt, response) | 80M |
-| [Miscellaneous unsupervised SBERT data](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | - | 10M |
-
-**Second stage**: supervised fine-tuning
-
-| Dataset | Language | # of text pairs |
-|----------------------------------------------------------------------------------------|--------------|-----------------|
-| [MS MARCO](https://microsoft.github.io/msmarco/) | English | 500k |
-| [NQ](https://github.com/facebookresearch/DPR) | English | 70k |
-| [Trivia QA](https://github.com/facebookresearch/DPR) | English | 60k |
-| [NLI from SimCSE](https://github.com/princeton-nlp/SimCSE) | English | <300k |
-| [ELI5](https://huggingface.co/datasets/eli5) | English | 500k |
-| [DuReader Retrieval](https://github.com/baidu/DuReader/tree/master/DuReader-Retrieval) | Chinese | 86k |
-| [KILT Fever](https://huggingface.co/datasets/kilt_tasks) | English | 70k |
-| [KILT HotpotQA](https://huggingface.co/datasets/kilt_tasks) | English | 70k |
-| [SQuAD](https://huggingface.co/datasets/squad) | English | 87k |
-| [Quora](https://huggingface.co/datasets/quora) | English | 150k |
-| [Mr. TyDi](https://huggingface.co/datasets/castorini/mr-tydi) | 11 languages | 50k |
-| [MIRACL](https://huggingface.co/datasets/miracl/miracl) | 16 languages | 40k |
-
-For all labeled datasets, we only use its training set for fine-tuning.
-
-For other training details, please refer to our paper at [https://arxiv.org/pdf/2402.05672](https://arxiv.org/pdf/2402.05672).
-
-## Benchmark Results on [Mr. TyDi](https://arxiv.org/abs/2108.08787)
-
-| Model | Avg MRR@10 | | ar | bn | en | fi | id | ja | ko | ru | sw | te | th |
-|-----------------------|------------|-------|------| --- | --- | --- | --- | --- | --- | --- |------| --- | --- |
-| BM25 | 33.3 | | 36.7 | 41.3 | 15.1 | 28.8 | 38.2 | 21.7 | 28.1 | 32.9 | 39.6 | 42.4 | 41.7 |
-| mDPR | 16.7 | | 26.0 | 25.8 | 16.2 | 11.3 | 14.6 | 18.1 | 21.9 | 18.5 | 7.3 | 10.6 | 13.5 |
-| BM25 + mDPR | 41.7 | | 49.1 | 53.5 | 28.4 | 36.5 | 45.5 | 35.5 | 36.2 | 42.7 | 40.5 | 42.0 | 49.2 |
-| | |
-| multilingual-e5-small | 64.4 | | 71.5 | 66.3 | 54.5 | 57.7 | 63.2 | 55.4 | 54.3 | 60.8 | 65.4 | 89.1 | 70.1 |
-| multilingual-e5-base | 65.9 | | 72.3 | 65.0 | 58.5 | 60.8 | 64.9 | 56.6 | 55.8 | 62.7 | 69.0 | 86.6 | 72.7 |
-| multilingual-e5-large | **70.5** | | 77.5 | 73.2 | 60.8 | 66.8 | 68.5 | 62.5 | 61.6 | 65.8 | 72.7 | 90.2 | 76.2 |
+**Second stage**: fine-tuning on datasets from the [E5-mistral](https://arxiv.org/abs/2401.00368) paper.
## MTEB Benchmark Evaluation
Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results
on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316).
-## Support for Sentence Transformers
-
-Below is an example for usage with sentence_transformers.
-```python
-from sentence_transformers import SentenceTransformer
-model = SentenceTransformer('intfloat/multilingual-e5-large')
-input_texts = [
- 'query: how much protein should a female eat',
- 'query: å—瓜的家常åšæ³•',
- "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 i s 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or traini ng for a marathon. Check out the chart below to see how much protein you should be eating each day.",
- "passage: 1.清炒å—ç“œä¸ åŽŸæ–™:å«©å—ç“œåŠä¸ª 调料:葱ã€ç›ã€ç™½ç³–ã€é¸¡ç²¾ åšæ³•: 1ã€å—瓜用刀薄薄的削去表é¢ä¸€å±‚çš® ,用勺å刮去瓤 2ã€æ“¦æˆç»†ä¸(没有擦èœæ¿å°±ç”¨åˆ€æ…¢æ…¢åˆ‡æˆç»†ä¸) 3ã€é”…烧çƒæ”¾æ²¹,入葱花煸出香味 4ã€å…¥å—ç“œä¸å¿«é€Ÿç¿»ç‚’一分钟左å³, 放ç›ã€ä¸€ç‚¹ç™½ç³–和鸡精调味出锅 2.香葱炒å—ç“œ 原料:å—ç“œ1åª è°ƒæ–™:香葱ã€è’œæœ«ã€æ©„榄油ã€ç› åšæ³•: 1ã€å°†å—瓜去皮,切æˆç‰‡ 2ã€æ²¹ é”…8æˆçƒåŽ,将蒜末放入爆香 3ã€çˆ†é¦™åŽ,å°†å—瓜片放入,翻炒 4ã€åœ¨ç¿»ç‚’çš„åŒæ—¶,å¯ä»¥ä¸æ—¶åœ°å¾€é”…é‡ŒåŠ æ°´,但ä¸è¦å¤ªå¤š 5ã€æ”¾å…¥ç›,炒匀 6ã€å—瓜差ä¸å¤šè½¯å’Œç»µäº†ä¹‹åŽ,å°±å¯ä»¥å…³ç« 7ã€æ’’入香葱,å³å¯å‡ºé”…"
-]
-embeddings = model.encode(input_texts, normalize_embeddings=True)
-```
-
-Package requirements
-
-`pip install sentence_transformers~=2.2.2`
-
-Contributors: [michaelfeil](https://huggingface.co/michaelfeil)
-
## FAQ
-**1. Do I need to add the prefix "query: " and "passage: " to input texts?**
+**1. Do I need to add instructions to the query?**
Yes, this is how the model is trained, otherwise you will see a performance degradation.
+The task definition should be a one-sentence instruction that describes the task.
+This is a way to customize text embeddings for different scenarios through natural language instructions.
-Here are some rules of thumb:
-- Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval.
-
-- Use "query: " prefix for symmetric tasks such as semantic similarity, bitext mining, paraphrase retrieval.
+Please check out [unilm/e5/utils.py](https://github.com/microsoft/unilm/blob/9c0f1ff7ca53431fe47d2637dfe253643d94185b/e5/utils.py#L106) for instructions we used for evaluation.
-- Use "query: " prefix if you want to use embeddings as features, such as linear probing classification, clustering.
+On the other hand, there is no need to add instructions to the document side.
**2. Why are my reproduced results slightly different from reported in the model card?**
diff --git a/models/intfloat-multilingual-e5-large/config.json b/models/multilingual-e5-large-instruct/config.json
similarity index 91%
rename from models/intfloat-multilingual-e5-large/config.json
rename to models/multilingual-e5-large-instruct/config.json
index 2f783e7b1a6ea86b2da15611439f978255abad68..ca4605dd459aa90d6d3322809c6438e10ef5345d 100644
--- a/models/intfloat-multilingual-e5-large/config.json
+++ b/models/multilingual-e5-large-instruct/config.json
@@ -1,5 +1,5 @@
{
- "_name_or_path": "intfloat/multilingual-e5-large",
+ "_name_or_path": "intfloat/multilingual-e5-large-instruct",
"architectures": [
"XLMRobertaModel"
],
diff --git a/models/multilingual-e5-large-instruct/config_sentence_transformers.json b/models/multilingual-e5-large-instruct/config_sentence_transformers.json
new file mode 100644
index 0000000000000000000000000000000000000000..8df5a4d3a2ecae9b64def8242fe852a8cb8b4898
--- /dev/null
+++ b/models/multilingual-e5-large-instruct/config_sentence_transformers.json
@@ -0,0 +1,9 @@
+{
+ "__version__": {
+ "sentence_transformers": "2.4.0.dev0",
+ "transformers": "4.37.0",
+ "pytorch": "2.1.0+cu121"
+ },
+ "prompts": {},
+ "default_prompt_name": null
+}
\ No newline at end of file
diff --git a/models/multilingual-e5-large-instruct/model.safetensors b/models/multilingual-e5-large-instruct/model.safetensors
new file mode 100644
index 0000000000000000000000000000000000000000..40cb980238e620aa0e1d748d32632888061ff99f
--- /dev/null
+++ b/models/multilingual-e5-large-instruct/model.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d0d642a915473631cdf27ac25ca63d7fdd75a3bc3ef6cac2202cbcd63043cf20
+size 2239607176
diff --git a/models/intfloat-multilingual-e5-large/modules.json b/models/multilingual-e5-large-instruct/modules.json
similarity index 100%
rename from models/intfloat-multilingual-e5-large/modules.json
rename to models/multilingual-e5-large-instruct/modules.json
diff --git a/models/intfloat-multilingual-e5-large/sentence_bert_config.json b/models/multilingual-e5-large-instruct/sentence_bert_config.json
similarity index 100%
rename from models/intfloat-multilingual-e5-large/sentence_bert_config.json
rename to models/multilingual-e5-large-instruct/sentence_bert_config.json
diff --git a/models/intfloat-multilingual-e5-large/sentencepiece.bpe.model b/models/multilingual-e5-large-instruct/sentencepiece.bpe.model
similarity index 100%
rename from models/intfloat-multilingual-e5-large/sentencepiece.bpe.model
rename to models/multilingual-e5-large-instruct/sentencepiece.bpe.model
diff --git a/models/intfloat-multilingual-e5-large/special_tokens_map.json b/models/multilingual-e5-large-instruct/special_tokens_map.json
similarity index 100%
rename from models/intfloat-multilingual-e5-large/special_tokens_map.json
rename to models/multilingual-e5-large-instruct/special_tokens_map.json
diff --git a/models/intfloat-multilingual-e5-large/tokenizer.json b/models/multilingual-e5-large-instruct/tokenizer.json
similarity index 100%
rename from models/intfloat-multilingual-e5-large/tokenizer.json
rename to models/multilingual-e5-large-instruct/tokenizer.json
diff --git a/models/intfloat-multilingual-e5-large/tokenizer_config.json b/models/multilingual-e5-large-instruct/tokenizer_config.json
similarity index 97%
rename from models/intfloat-multilingual-e5-large/tokenizer_config.json
rename to models/multilingual-e5-large-instruct/tokenizer_config.json
index 31cf25a6c1a3e2f4183a314ad8252cd2919f6968..bc9cb006ea6982868d4e32f58506b1d17873b21f 100644
--- a/models/intfloat-multilingual-e5-large/tokenizer_config.json
+++ b/models/multilingual-e5-large-instruct/tokenizer_config.json
@@ -41,6 +41,7 @@
"special": true
}
},
+ "additional_special_tokens": [],
"bos_token": "<s>",
"clean_up_tokenization_spaces": true,
"cls_token": "<s>",