Update README.md
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README.md
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@@ -1,3 +1,1211 @@
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1 |
---
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2 |
+
pipeline_tag: sentence-similarity
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tags:
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- sentence-transformers
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- feature-extraction
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- sentence-similarity
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- mteb
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model-index:
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- name: stella-large-zh-v3-1792d
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results:
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- task:
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type: STS
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dataset:
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type: C-MTEB/AFQMC
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name: MTEB AFQMC
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config: default
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split: validation
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revision: None
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metrics:
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- type: cos_sim_pearson
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value: 54.48093298255762
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- type: cos_sim_spearman
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value: 59.105354109068685
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- type: euclidean_pearson
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value: 57.761189988643444
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- type: euclidean_spearman
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value: 59.10537421115596
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- type: manhattan_pearson
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value: 56.94359297051431
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- type: manhattan_spearman
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value: 58.37611109821567
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- task:
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type: STS
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dataset:
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type: C-MTEB/ATEC
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name: MTEB ATEC
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config: default
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split: test
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revision: None
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metrics:
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- type: cos_sim_pearson
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value: 54.39711127600595
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- type: cos_sim_spearman
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value: 58.190191920824454
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- type: euclidean_pearson
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value: 61.80082379352729
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+
- type: euclidean_spearman
|
48 |
+
value: 58.19018966860797
|
49 |
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- type: manhattan_pearson
|
50 |
+
value: 60.927601060396206
|
51 |
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- type: manhattan_spearman
|
52 |
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value: 57.78832902694192
|
53 |
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- task:
|
54 |
+
type: Classification
|
55 |
+
dataset:
|
56 |
+
type: mteb/amazon_reviews_multi
|
57 |
+
name: MTEB AmazonReviewsClassification (zh)
|
58 |
+
config: zh
|
59 |
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split: test
|
60 |
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revision: 1399c76144fd37290681b995c656ef9b2e06e26d
|
61 |
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metrics:
|
62 |
+
- type: accuracy
|
63 |
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value: 46.31600000000001
|
64 |
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- type: f1
|
65 |
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value: 44.45281663598873
|
66 |
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- task:
|
67 |
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type: STS
|
68 |
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dataset:
|
69 |
+
type: C-MTEB/BQ
|
70 |
+
name: MTEB BQ
|
71 |
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config: default
|
72 |
+
split: test
|
73 |
+
revision: None
|
74 |
+
metrics:
|
75 |
+
- type: cos_sim_pearson
|
76 |
+
value: 69.12211326097868
|
77 |
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- type: cos_sim_spearman
|
78 |
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value: 71.0741302039443
|
79 |
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- type: euclidean_pearson
|
80 |
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value: 69.89070483887852
|
81 |
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- type: euclidean_spearman
|
82 |
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value: 71.07413020351787
|
83 |
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- type: manhattan_pearson
|
84 |
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value: 69.62345441260962
|
85 |
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- type: manhattan_spearman
|
86 |
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value: 70.8517591280618
|
87 |
+
- task:
|
88 |
+
type: Clustering
|
89 |
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dataset:
|
90 |
+
type: C-MTEB/CLSClusteringP2P
|
91 |
+
name: MTEB CLSClusteringP2P
|
92 |
+
config: default
|
93 |
+
split: test
|
94 |
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revision: None
|
95 |
+
metrics:
|
96 |
+
- type: v_measure
|
97 |
+
value: 41.937723608805314
|
98 |
+
- task:
|
99 |
+
type: Clustering
|
100 |
+
dataset:
|
101 |
+
type: C-MTEB/CLSClusteringS2S
|
102 |
+
name: MTEB CLSClusteringS2S
|
103 |
+
config: default
|
104 |
+
split: test
|
105 |
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revision: None
|
106 |
+
metrics:
|
107 |
+
- type: v_measure
|
108 |
+
value: 40.34373057675427
|
109 |
+
- task:
|
110 |
+
type: Reranking
|
111 |
+
dataset:
|
112 |
+
type: C-MTEB/CMedQAv1-reranking
|
113 |
+
name: MTEB CMedQAv1
|
114 |
+
config: default
|
115 |
+
split: test
|
116 |
+
revision: None
|
117 |
+
metrics:
|
118 |
+
- type: map
|
119 |
+
value: 88.98896401788376
|
120 |
+
- type: mrr
|
121 |
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value: 90.97119047619047
|
122 |
+
- task:
|
123 |
+
type: Reranking
|
124 |
+
dataset:
|
125 |
+
type: C-MTEB/CMedQAv2-reranking
|
126 |
+
name: MTEB CMedQAv2
|
127 |
+
config: default
|
128 |
+
split: test
|
129 |
+
revision: None
|
130 |
+
metrics:
|
131 |
+
- type: map
|
132 |
+
value: 89.59718540244556
|
133 |
+
- type: mrr
|
134 |
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value: 91.41246031746032
|
135 |
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- task:
|
136 |
+
type: Retrieval
|
137 |
+
dataset:
|
138 |
+
type: C-MTEB/CmedqaRetrieval
|
139 |
+
name: MTEB CmedqaRetrieval
|
140 |
+
config: default
|
141 |
+
split: dev
|
142 |
+
revision: None
|
143 |
+
metrics:
|
144 |
+
- type: map_at_1
|
145 |
+
value: 26.954
|
146 |
+
- type: map_at_10
|
147 |
+
value: 40.144999999999996
|
148 |
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- type: map_at_100
|
149 |
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value: 42.083999999999996
|
150 |
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- type: map_at_1000
|
151 |
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value: 42.181000000000004
|
152 |
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- type: map_at_3
|
153 |
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value: 35.709
|
154 |
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- type: map_at_5
|
155 |
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value: 38.141000000000005
|
156 |
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- type: mrr_at_1
|
157 |
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value: 40.71
|
158 |
+
- type: mrr_at_10
|
159 |
+
value: 48.93
|
160 |
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- type: mrr_at_100
|
161 |
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value: 49.921
|
162 |
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- type: mrr_at_1000
|
163 |
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value: 49.958999999999996
|
164 |
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- type: mrr_at_3
|
165 |
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value: 46.32
|
166 |
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- type: mrr_at_5
|
167 |
+
value: 47.769
|
168 |
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- type: ndcg_at_1
|
169 |
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value: 40.71
|
170 |
+
- type: ndcg_at_10
|
171 |
+
value: 46.869
|
172 |
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- type: ndcg_at_100
|
173 |
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value: 54.234
|
174 |
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- type: ndcg_at_1000
|
175 |
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value: 55.854000000000006
|
176 |
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- type: ndcg_at_3
|
177 |
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value: 41.339
|
178 |
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- type: ndcg_at_5
|
179 |
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value: 43.594
|
180 |
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- type: precision_at_1
|
181 |
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value: 40.71
|
182 |
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- type: precision_at_10
|
183 |
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value: 10.408000000000001
|
184 |
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- type: precision_at_100
|
185 |
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value: 1.635
|
186 |
+
- type: precision_at_1000
|
187 |
+
value: 0.184
|
188 |
+
- type: precision_at_3
|
189 |
+
value: 23.348
|
190 |
+
- type: precision_at_5
|
191 |
+
value: 16.929
|
192 |
+
- type: recall_at_1
|
193 |
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value: 26.954
|
194 |
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- type: recall_at_10
|
195 |
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value: 57.821999999999996
|
196 |
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- type: recall_at_100
|
197 |
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value: 88.08200000000001
|
198 |
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- type: recall_at_1000
|
199 |
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value: 98.83800000000001
|
200 |
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- type: recall_at_3
|
201 |
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value: 41.221999999999994
|
202 |
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- type: recall_at_5
|
203 |
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value: 48.241
|
204 |
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- task:
|
205 |
+
type: PairClassification
|
206 |
+
dataset:
|
207 |
+
type: C-MTEB/CMNLI
|
208 |
+
name: MTEB Cmnli
|
209 |
+
config: default
|
210 |
+
split: validation
|
211 |
+
revision: None
|
212 |
+
metrics:
|
213 |
+
- type: cos_sim_accuracy
|
214 |
+
value: 83.6680697534576
|
215 |
+
- type: cos_sim_ap
|
216 |
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value: 90.77401562455269
|
217 |
+
- type: cos_sim_f1
|
218 |
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value: 84.68266427450101
|
219 |
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- type: cos_sim_precision
|
220 |
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value: 81.36177547942253
|
221 |
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- type: cos_sim_recall
|
222 |
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value: 88.28618190320317
|
223 |
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- type: dot_accuracy
|
224 |
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value: 83.6680697534576
|
225 |
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- type: dot_ap
|
226 |
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value: 90.76429465198817
|
227 |
+
- type: dot_f1
|
228 |
+
value: 84.68266427450101
|
229 |
+
- type: dot_precision
|
230 |
+
value: 81.36177547942253
|
231 |
+
- type: dot_recall
|
232 |
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value: 88.28618190320317
|
233 |
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- type: euclidean_accuracy
|
234 |
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value: 83.6680697534576
|
235 |
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- type: euclidean_ap
|
236 |
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value: 90.77401909305344
|
237 |
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- type: euclidean_f1
|
238 |
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value: 84.68266427450101
|
239 |
+
- type: euclidean_precision
|
240 |
+
value: 81.36177547942253
|
241 |
+
- type: euclidean_recall
|
242 |
+
value: 88.28618190320317
|
243 |
+
- type: manhattan_accuracy
|
244 |
+
value: 83.40348767288035
|
245 |
+
- type: manhattan_ap
|
246 |
+
value: 90.57002020310819
|
247 |
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- type: manhattan_f1
|
248 |
+
value: 84.51526032315978
|
249 |
+
- type: manhattan_precision
|
250 |
+
value: 81.25134843581445
|
251 |
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- type: manhattan_recall
|
252 |
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value: 88.05237315875614
|
253 |
+
- type: max_accuracy
|
254 |
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value: 83.6680697534576
|
255 |
+
- type: max_ap
|
256 |
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value: 90.77401909305344
|
257 |
+
- type: max_f1
|
258 |
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value: 84.68266427450101
|
259 |
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- task:
|
260 |
+
type: Retrieval
|
261 |
+
dataset:
|
262 |
+
type: C-MTEB/CovidRetrieval
|
263 |
+
name: MTEB CovidRetrieval
|
264 |
+
config: default
|
265 |
+
split: dev
|
266 |
+
revision: None
|
267 |
+
metrics:
|
268 |
+
- type: map_at_1
|
269 |
+
value: 69.705
|
270 |
+
- type: map_at_10
|
271 |
+
value: 78.648
|
272 |
+
- type: map_at_100
|
273 |
+
value: 78.888
|
274 |
+
- type: map_at_1000
|
275 |
+
value: 78.89399999999999
|
276 |
+
- type: map_at_3
|
277 |
+
value: 77.151
|
278 |
+
- type: map_at_5
|
279 |
+
value: 77.98
|
280 |
+
- type: mrr_at_1
|
281 |
+
value: 69.863
|
282 |
+
- type: mrr_at_10
|
283 |
+
value: 78.62599999999999
|
284 |
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- type: mrr_at_100
|
285 |
+
value: 78.861
|
286 |
+
- type: mrr_at_1000
|
287 |
+
value: 78.867
|
288 |
+
- type: mrr_at_3
|
289 |
+
value: 77.204
|
290 |
+
- type: mrr_at_5
|
291 |
+
value: 78.005
|
292 |
+
- type: ndcg_at_1
|
293 |
+
value: 69.968
|
294 |
+
- type: ndcg_at_10
|
295 |
+
value: 82.44399999999999
|
296 |
+
- type: ndcg_at_100
|
297 |
+
value: 83.499
|
298 |
+
- type: ndcg_at_1000
|
299 |
+
value: 83.647
|
300 |
+
- type: ndcg_at_3
|
301 |
+
value: 79.393
|
302 |
+
- type: ndcg_at_5
|
303 |
+
value: 80.855
|
304 |
+
- type: precision_at_1
|
305 |
+
value: 69.968
|
306 |
+
- type: precision_at_10
|
307 |
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value: 9.515
|
308 |
+
- type: precision_at_100
|
309 |
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value: 0.9990000000000001
|
310 |
+
- type: precision_at_1000
|
311 |
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value: 0.101
|
312 |
+
- type: precision_at_3
|
313 |
+
value: 28.802
|
314 |
+
- type: precision_at_5
|
315 |
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value: 18.019
|
316 |
+
- type: recall_at_1
|
317 |
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value: 69.705
|
318 |
+
- type: recall_at_10
|
319 |
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value: 94.152
|
320 |
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- type: recall_at_100
|
321 |
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value: 98.84100000000001
|
322 |
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- type: recall_at_1000
|
323 |
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value: 100.0
|
324 |
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- type: recall_at_3
|
325 |
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value: 85.774
|
326 |
+
- type: recall_at_5
|
327 |
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value: 89.252
|
328 |
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- task:
|
329 |
+
type: Retrieval
|
330 |
+
dataset:
|
331 |
+
type: C-MTEB/DuRetrieval
|
332 |
+
name: MTEB DuRetrieval
|
333 |
+
config: default
|
334 |
+
split: dev
|
335 |
+
revision: None
|
336 |
+
metrics:
|
337 |
+
- type: map_at_1
|
338 |
+
value: 25.88
|
339 |
+
- type: map_at_10
|
340 |
+
value: 79.857
|
341 |
+
- type: map_at_100
|
342 |
+
value: 82.636
|
343 |
+
- type: map_at_1000
|
344 |
+
value: 82.672
|
345 |
+
- type: map_at_3
|
346 |
+
value: 55.184
|
347 |
+
- type: map_at_5
|
348 |
+
value: 70.009
|
349 |
+
- type: mrr_at_1
|
350 |
+
value: 89.64999999999999
|
351 |
+
- type: mrr_at_10
|
352 |
+
value: 92.967
|
353 |
+
- type: mrr_at_100
|
354 |
+
value: 93.039
|
355 |
+
- type: mrr_at_1000
|
356 |
+
value: 93.041
|
357 |
+
- type: mrr_at_3
|
358 |
+
value: 92.65
|
359 |
+
- type: mrr_at_5
|
360 |
+
value: 92.86
|
361 |
+
- type: ndcg_at_1
|
362 |
+
value: 89.64999999999999
|
363 |
+
- type: ndcg_at_10
|
364 |
+
value: 87.126
|
365 |
+
- type: ndcg_at_100
|
366 |
+
value: 89.898
|
367 |
+
- type: ndcg_at_1000
|
368 |
+
value: 90.253
|
369 |
+
- type: ndcg_at_3
|
370 |
+
value: 86.012
|
371 |
+
- type: ndcg_at_5
|
372 |
+
value: 85.124
|
373 |
+
- type: precision_at_1
|
374 |
+
value: 89.64999999999999
|
375 |
+
- type: precision_at_10
|
376 |
+
value: 41.735
|
377 |
+
- type: precision_at_100
|
378 |
+
value: 4.797
|
379 |
+
- type: precision_at_1000
|
380 |
+
value: 0.488
|
381 |
+
- type: precision_at_3
|
382 |
+
value: 77.267
|
383 |
+
- type: precision_at_5
|
384 |
+
value: 65.48
|
385 |
+
- type: recall_at_1
|
386 |
+
value: 25.88
|
387 |
+
- type: recall_at_10
|
388 |
+
value: 88.28399999999999
|
389 |
+
- type: recall_at_100
|
390 |
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value: 97.407
|
391 |
+
- type: recall_at_1000
|
392 |
+
value: 99.29299999999999
|
393 |
+
- type: recall_at_3
|
394 |
+
value: 57.38799999999999
|
395 |
+
- type: recall_at_5
|
396 |
+
value: 74.736
|
397 |
+
- task:
|
398 |
+
type: Retrieval
|
399 |
+
dataset:
|
400 |
+
type: C-MTEB/EcomRetrieval
|
401 |
+
name: MTEB EcomRetrieval
|
402 |
+
config: default
|
403 |
+
split: dev
|
404 |
+
revision: None
|
405 |
+
metrics:
|
406 |
+
- type: map_at_1
|
407 |
+
value: 53.2
|
408 |
+
- type: map_at_10
|
409 |
+
value: 63.556000000000004
|
410 |
+
- type: map_at_100
|
411 |
+
value: 64.033
|
412 |
+
- type: map_at_1000
|
413 |
+
value: 64.044
|
414 |
+
- type: map_at_3
|
415 |
+
value: 60.983
|
416 |
+
- type: map_at_5
|
417 |
+
value: 62.588
|
418 |
+
- type: mrr_at_1
|
419 |
+
value: 53.2
|
420 |
+
- type: mrr_at_10
|
421 |
+
value: 63.556000000000004
|
422 |
+
- type: mrr_at_100
|
423 |
+
value: 64.033
|
424 |
+
- type: mrr_at_1000
|
425 |
+
value: 64.044
|
426 |
+
- type: mrr_at_3
|
427 |
+
value: 60.983
|
428 |
+
- type: mrr_at_5
|
429 |
+
value: 62.588
|
430 |
+
- type: ndcg_at_1
|
431 |
+
value: 53.2
|
432 |
+
- type: ndcg_at_10
|
433 |
+
value: 68.61699999999999
|
434 |
+
- type: ndcg_at_100
|
435 |
+
value: 70.88499999999999
|
436 |
+
- type: ndcg_at_1000
|
437 |
+
value: 71.15899999999999
|
438 |
+
- type: ndcg_at_3
|
439 |
+
value: 63.434000000000005
|
440 |
+
- type: ndcg_at_5
|
441 |
+
value: 66.301
|
442 |
+
- type: precision_at_1
|
443 |
+
value: 53.2
|
444 |
+
- type: precision_at_10
|
445 |
+
value: 8.450000000000001
|
446 |
+
- type: precision_at_100
|
447 |
+
value: 0.95
|
448 |
+
- type: precision_at_1000
|
449 |
+
value: 0.097
|
450 |
+
- type: precision_at_3
|
451 |
+
value: 23.5
|
452 |
+
- type: precision_at_5
|
453 |
+
value: 15.479999999999999
|
454 |
+
- type: recall_at_1
|
455 |
+
value: 53.2
|
456 |
+
- type: recall_at_10
|
457 |
+
value: 84.5
|
458 |
+
- type: recall_at_100
|
459 |
+
value: 95.0
|
460 |
+
- type: recall_at_1000
|
461 |
+
value: 97.1
|
462 |
+
- type: recall_at_3
|
463 |
+
value: 70.5
|
464 |
+
- type: recall_at_5
|
465 |
+
value: 77.4
|
466 |
+
- task:
|
467 |
+
type: Classification
|
468 |
+
dataset:
|
469 |
+
type: C-MTEB/IFlyTek-classification
|
470 |
+
name: MTEB IFlyTek
|
471 |
+
config: default
|
472 |
+
split: validation
|
473 |
+
revision: None
|
474 |
+
metrics:
|
475 |
+
- type: accuracy
|
476 |
+
value: 50.63485956136976
|
477 |
+
- type: f1
|
478 |
+
value: 38.286307407751266
|
479 |
+
- task:
|
480 |
+
type: Classification
|
481 |
+
dataset:
|
482 |
+
type: C-MTEB/JDReview-classification
|
483 |
+
name: MTEB JDReview
|
484 |
+
config: default
|
485 |
+
split: test
|
486 |
+
revision: None
|
487 |
+
metrics:
|
488 |
+
- type: accuracy
|
489 |
+
value: 86.11632270168855
|
490 |
+
- type: ap
|
491 |
+
value: 54.43932599806482
|
492 |
+
- type: f1
|
493 |
+
value: 80.85485110996076
|
494 |
+
- task:
|
495 |
+
type: STS
|
496 |
+
dataset:
|
497 |
+
type: C-MTEB/LCQMC
|
498 |
+
name: MTEB LCQMC
|
499 |
+
config: default
|
500 |
+
split: test
|
501 |
+
revision: None
|
502 |
+
metrics:
|
503 |
+
- type: cos_sim_pearson
|
504 |
+
value: 72.47315152994804
|
505 |
+
- type: cos_sim_spearman
|
506 |
+
value: 78.26531600908152
|
507 |
+
- type: euclidean_pearson
|
508 |
+
value: 77.8560788714531
|
509 |
+
- type: euclidean_spearman
|
510 |
+
value: 78.26531157334841
|
511 |
+
- type: manhattan_pearson
|
512 |
+
value: 77.70593783974188
|
513 |
+
- type: manhattan_spearman
|
514 |
+
value: 78.13880812439999
|
515 |
+
- task:
|
516 |
+
type: Reranking
|
517 |
+
dataset:
|
518 |
+
type: C-MTEB/Mmarco-reranking
|
519 |
+
name: MTEB MMarcoReranking
|
520 |
+
config: default
|
521 |
+
split: dev
|
522 |
+
revision: None
|
523 |
+
metrics:
|
524 |
+
- type: map
|
525 |
+
value: 28.088177976572222
|
526 |
+
- type: mrr
|
527 |
+
value: 27.125
|
528 |
+
- task:
|
529 |
+
type: Retrieval
|
530 |
+
dataset:
|
531 |
+
type: C-MTEB/MMarcoRetrieval
|
532 |
+
name: MTEB MMarcoRetrieval
|
533 |
+
config: default
|
534 |
+
split: dev
|
535 |
+
revision: None
|
536 |
+
metrics:
|
537 |
+
- type: map_at_1
|
538 |
+
value: 66.428
|
539 |
+
- type: map_at_10
|
540 |
+
value: 75.5
|
541 |
+
- type: map_at_100
|
542 |
+
value: 75.82600000000001
|
543 |
+
- type: map_at_1000
|
544 |
+
value: 75.837
|
545 |
+
- type: map_at_3
|
546 |
+
value: 73.74300000000001
|
547 |
+
- type: map_at_5
|
548 |
+
value: 74.87
|
549 |
+
- type: mrr_at_1
|
550 |
+
value: 68.754
|
551 |
+
- type: mrr_at_10
|
552 |
+
value: 76.145
|
553 |
+
- type: mrr_at_100
|
554 |
+
value: 76.432
|
555 |
+
- type: mrr_at_1000
|
556 |
+
value: 76.442
|
557 |
+
- type: mrr_at_3
|
558 |
+
value: 74.628
|
559 |
+
- type: mrr_at_5
|
560 |
+
value: 75.612
|
561 |
+
- type: ndcg_at_1
|
562 |
+
value: 68.754
|
563 |
+
- type: ndcg_at_10
|
564 |
+
value: 79.144
|
565 |
+
- type: ndcg_at_100
|
566 |
+
value: 80.60199999999999
|
567 |
+
- type: ndcg_at_1000
|
568 |
+
value: 80.886
|
569 |
+
- type: ndcg_at_3
|
570 |
+
value: 75.81599999999999
|
571 |
+
- type: ndcg_at_5
|
572 |
+
value: 77.729
|
573 |
+
- type: precision_at_1
|
574 |
+
value: 68.754
|
575 |
+
- type: precision_at_10
|
576 |
+
value: 9.544
|
577 |
+
- type: precision_at_100
|
578 |
+
value: 1.026
|
579 |
+
- type: precision_at_1000
|
580 |
+
value: 0.105
|
581 |
+
- type: precision_at_3
|
582 |
+
value: 28.534
|
583 |
+
- type: precision_at_5
|
584 |
+
value: 18.138
|
585 |
+
- type: recall_at_1
|
586 |
+
value: 66.428
|
587 |
+
- type: recall_at_10
|
588 |
+
value: 89.716
|
589 |
+
- type: recall_at_100
|
590 |
+
value: 96.313
|
591 |
+
- type: recall_at_1000
|
592 |
+
value: 98.541
|
593 |
+
- type: recall_at_3
|
594 |
+
value: 80.923
|
595 |
+
- type: recall_at_5
|
596 |
+
value: 85.48
|
597 |
+
- task:
|
598 |
+
type: Classification
|
599 |
+
dataset:
|
600 |
+
type: mteb/amazon_massive_intent
|
601 |
+
name: MTEB MassiveIntentClassification (zh-CN)
|
602 |
+
config: zh-CN
|
603 |
+
split: test
|
604 |
+
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
|
605 |
+
metrics:
|
606 |
+
- type: accuracy
|
607 |
+
value: 73.27841291190316
|
608 |
+
- type: f1
|
609 |
+
value: 70.65529957574735
|
610 |
+
- task:
|
611 |
+
type: Classification
|
612 |
+
dataset:
|
613 |
+
type: mteb/amazon_massive_scenario
|
614 |
+
name: MTEB MassiveScenarioClassification (zh-CN)
|
615 |
+
config: zh-CN
|
616 |
+
split: test
|
617 |
+
revision: 7d571f92784cd94a019292a1f45445077d0ef634
|
618 |
+
metrics:
|
619 |
+
- type: accuracy
|
620 |
+
value: 76.30127774041695
|
621 |
+
- type: f1
|
622 |
+
value: 76.10358226518304
|
623 |
+
- task:
|
624 |
+
type: Retrieval
|
625 |
+
dataset:
|
626 |
+
type: C-MTEB/MedicalRetrieval
|
627 |
+
name: MTEB MedicalRetrieval
|
628 |
+
config: default
|
629 |
+
split: dev
|
630 |
+
revision: None
|
631 |
+
metrics:
|
632 |
+
- type: map_at_1
|
633 |
+
value: 56.3
|
634 |
+
- type: map_at_10
|
635 |
+
value: 62.193
|
636 |
+
- type: map_at_100
|
637 |
+
value: 62.722
|
638 |
+
- type: map_at_1000
|
639 |
+
value: 62.765
|
640 |
+
- type: map_at_3
|
641 |
+
value: 60.633
|
642 |
+
- type: map_at_5
|
643 |
+
value: 61.617999999999995
|
644 |
+
- type: mrr_at_1
|
645 |
+
value: 56.3
|
646 |
+
- type: mrr_at_10
|
647 |
+
value: 62.193
|
648 |
+
- type: mrr_at_100
|
649 |
+
value: 62.722
|
650 |
+
- type: mrr_at_1000
|
651 |
+
value: 62.765
|
652 |
+
- type: mrr_at_3
|
653 |
+
value: 60.633
|
654 |
+
- type: mrr_at_5
|
655 |
+
value: 61.617999999999995
|
656 |
+
- type: ndcg_at_1
|
657 |
+
value: 56.3
|
658 |
+
- type: ndcg_at_10
|
659 |
+
value: 65.176
|
660 |
+
- type: ndcg_at_100
|
661 |
+
value: 67.989
|
662 |
+
- type: ndcg_at_1000
|
663 |
+
value: 69.219
|
664 |
+
- type: ndcg_at_3
|
665 |
+
value: 62.014
|
666 |
+
- type: ndcg_at_5
|
667 |
+
value: 63.766
|
668 |
+
- type: precision_at_1
|
669 |
+
value: 56.3
|
670 |
+
- type: precision_at_10
|
671 |
+
value: 7.46
|
672 |
+
- type: precision_at_100
|
673 |
+
value: 0.8829999999999999
|
674 |
+
- type: precision_at_1000
|
675 |
+
value: 0.098
|
676 |
+
- type: precision_at_3
|
677 |
+
value: 22.0
|
678 |
+
- type: precision_at_5
|
679 |
+
value: 14.04
|
680 |
+
- type: recall_at_1
|
681 |
+
value: 56.3
|
682 |
+
- type: recall_at_10
|
683 |
+
value: 74.6
|
684 |
+
- type: recall_at_100
|
685 |
+
value: 88.3
|
686 |
+
- type: recall_at_1000
|
687 |
+
value: 98.1
|
688 |
+
- type: recall_at_3
|
689 |
+
value: 66.0
|
690 |
+
- type: recall_at_5
|
691 |
+
value: 70.19999999999999
|
692 |
+
- task:
|
693 |
+
type: Classification
|
694 |
+
dataset:
|
695 |
+
type: C-MTEB/MultilingualSentiment-classification
|
696 |
+
name: MTEB MultilingualSentiment
|
697 |
+
config: default
|
698 |
+
split: validation
|
699 |
+
revision: None
|
700 |
+
metrics:
|
701 |
+
- type: accuracy
|
702 |
+
value: 76.44666666666666
|
703 |
+
- type: f1
|
704 |
+
value: 76.34548655475949
|
705 |
+
- task:
|
706 |
+
type: PairClassification
|
707 |
+
dataset:
|
708 |
+
type: C-MTEB/OCNLI
|
709 |
+
name: MTEB Ocnli
|
710 |
+
config: default
|
711 |
+
split: validation
|
712 |
+
revision: None
|
713 |
+
metrics:
|
714 |
+
- type: cos_sim_accuracy
|
715 |
+
value: 82.34975636166757
|
716 |
+
- type: cos_sim_ap
|
717 |
+
value: 85.44149338593267
|
718 |
+
- type: cos_sim_f1
|
719 |
+
value: 83.68654509610647
|
720 |
+
- type: cos_sim_precision
|
721 |
+
value: 78.46580406654344
|
722 |
+
- type: cos_sim_recall
|
723 |
+
value: 89.65153115100317
|
724 |
+
- type: dot_accuracy
|
725 |
+
value: 82.34975636166757
|
726 |
+
- type: dot_ap
|
727 |
+
value: 85.4415701376729
|
728 |
+
- type: dot_f1
|
729 |
+
value: 83.68654509610647
|
730 |
+
- type: dot_precision
|
731 |
+
value: 78.46580406654344
|
732 |
+
- type: dot_recall
|
733 |
+
value: 89.65153115100317
|
734 |
+
- type: euclidean_accuracy
|
735 |
+
value: 82.34975636166757
|
736 |
+
- type: euclidean_ap
|
737 |
+
value: 85.4415701376729
|
738 |
+
- type: euclidean_f1
|
739 |
+
value: 83.68654509610647
|
740 |
+
- type: euclidean_precision
|
741 |
+
value: 78.46580406654344
|
742 |
+
- type: euclidean_recall
|
743 |
+
value: 89.65153115100317
|
744 |
+
- type: manhattan_accuracy
|
745 |
+
value: 81.97076340010828
|
746 |
+
- type: manhattan_ap
|
747 |
+
value: 84.83614660756733
|
748 |
+
- type: manhattan_f1
|
749 |
+
value: 83.34167083541772
|
750 |
+
- type: manhattan_precision
|
751 |
+
value: 79.18250950570342
|
752 |
+
- type: manhattan_recall
|
753 |
+
value: 87.96198521647307
|
754 |
+
- type: max_accuracy
|
755 |
+
value: 82.34975636166757
|
756 |
+
- type: max_ap
|
757 |
+
value: 85.4415701376729
|
758 |
+
- type: max_f1
|
759 |
+
value: 83.68654509610647
|
760 |
+
- task:
|
761 |
+
type: Classification
|
762 |
+
dataset:
|
763 |
+
type: C-MTEB/OnlineShopping-classification
|
764 |
+
name: MTEB OnlineShopping
|
765 |
+
config: default
|
766 |
+
split: test
|
767 |
+
revision: None
|
768 |
+
metrics:
|
769 |
+
- type: accuracy
|
770 |
+
value: 93.24
|
771 |
+
- type: ap
|
772 |
+
value: 91.3586656455605
|
773 |
+
- type: f1
|
774 |
+
value: 93.22999314249503
|
775 |
+
- task:
|
776 |
+
type: STS
|
777 |
+
dataset:
|
778 |
+
type: C-MTEB/PAWSX
|
779 |
+
name: MTEB PAWSX
|
780 |
+
config: default
|
781 |
+
split: test
|
782 |
+
revision: None
|
783 |
+
metrics:
|
784 |
+
- type: cos_sim_pearson
|
785 |
+
value: 39.05676042449009
|
786 |
+
- type: cos_sim_spearman
|
787 |
+
value: 44.996534098358545
|
788 |
+
- type: euclidean_pearson
|
789 |
+
value: 44.42418609172825
|
790 |
+
- type: euclidean_spearman
|
791 |
+
value: 44.995941361058996
|
792 |
+
- type: manhattan_pearson
|
793 |
+
value: 43.98118203238076
|
794 |
+
- type: manhattan_spearman
|
795 |
+
value: 44.51414152788784
|
796 |
+
- task:
|
797 |
+
type: STS
|
798 |
+
dataset:
|
799 |
+
type: C-MTEB/QBQTC
|
800 |
+
name: MTEB QBQTC
|
801 |
+
config: default
|
802 |
+
split: test
|
803 |
+
revision: None
|
804 |
+
metrics:
|
805 |
+
- type: cos_sim_pearson
|
806 |
+
value: 36.694269474438045
|
807 |
+
- type: cos_sim_spearman
|
808 |
+
value: 38.686738967031616
|
809 |
+
- type: euclidean_pearson
|
810 |
+
value: 36.822540068407235
|
811 |
+
- type: euclidean_spearman
|
812 |
+
value: 38.68690745429757
|
813 |
+
- type: manhattan_pearson
|
814 |
+
value: 36.77180703308932
|
815 |
+
- type: manhattan_spearman
|
816 |
+
value: 38.45414914148094
|
817 |
+
- task:
|
818 |
+
type: STS
|
819 |
+
dataset:
|
820 |
+
type: mteb/sts22-crosslingual-sts
|
821 |
+
name: MTEB STS22 (zh)
|
822 |
+
config: zh
|
823 |
+
split: test
|
824 |
+
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
|
825 |
+
metrics:
|
826 |
+
- type: cos_sim_pearson
|
827 |
+
value: 65.81209017614124
|
828 |
+
- type: cos_sim_spearman
|
829 |
+
value: 66.5255285833172
|
830 |
+
- type: euclidean_pearson
|
831 |
+
value: 66.01848701752732
|
832 |
+
- type: euclidean_spearman
|
833 |
+
value: 66.5255285833172
|
834 |
+
- type: manhattan_pearson
|
835 |
+
value: 66.66433676370542
|
836 |
+
- type: manhattan_spearman
|
837 |
+
value: 67.07086311480214
|
838 |
+
- task:
|
839 |
+
type: STS
|
840 |
+
dataset:
|
841 |
+
type: C-MTEB/STSB
|
842 |
+
name: MTEB STSB
|
843 |
+
config: default
|
844 |
+
split: test
|
845 |
+
revision: None
|
846 |
+
metrics:
|
847 |
+
- type: cos_sim_pearson
|
848 |
+
value: 80.60785761283502
|
849 |
+
- type: cos_sim_spearman
|
850 |
+
value: 82.80278693241074
|
851 |
+
- type: euclidean_pearson
|
852 |
+
value: 82.47573315938638
|
853 |
+
- type: euclidean_spearman
|
854 |
+
value: 82.80290808593806
|
855 |
+
- type: manhattan_pearson
|
856 |
+
value: 82.49682028989669
|
857 |
+
- type: manhattan_spearman
|
858 |
+
value: 82.84565039346022
|
859 |
+
- task:
|
860 |
+
type: Reranking
|
861 |
+
dataset:
|
862 |
+
type: C-MTEB/T2Reranking
|
863 |
+
name: MTEB T2Reranking
|
864 |
+
config: default
|
865 |
+
split: dev
|
866 |
+
revision: None
|
867 |
+
metrics:
|
868 |
+
- type: map
|
869 |
+
value: 66.37886004738723
|
870 |
+
- type: mrr
|
871 |
+
value: 76.08501655006394
|
872 |
+
- task:
|
873 |
+
type: Retrieval
|
874 |
+
dataset:
|
875 |
+
type: C-MTEB/T2Retrieval
|
876 |
+
name: MTEB T2Retrieval
|
877 |
+
config: default
|
878 |
+
split: dev
|
879 |
+
revision: None
|
880 |
+
metrics:
|
881 |
+
- type: map_at_1
|
882 |
+
value: 28.102
|
883 |
+
- type: map_at_10
|
884 |
+
value: 78.071
|
885 |
+
- type: map_at_100
|
886 |
+
value: 81.71000000000001
|
887 |
+
- type: map_at_1000
|
888 |
+
value: 81.773
|
889 |
+
- type: map_at_3
|
890 |
+
value: 55.142
|
891 |
+
- type: map_at_5
|
892 |
+
value: 67.669
|
893 |
+
- type: mrr_at_1
|
894 |
+
value: 90.9
|
895 |
+
- type: mrr_at_10
|
896 |
+
value: 93.29499999999999
|
897 |
+
- type: mrr_at_100
|
898 |
+
value: 93.377
|
899 |
+
- type: mrr_at_1000
|
900 |
+
value: 93.379
|
901 |
+
- type: mrr_at_3
|
902 |
+
value: 92.901
|
903 |
+
- type: mrr_at_5
|
904 |
+
value: 93.152
|
905 |
+
- type: ndcg_at_1
|
906 |
+
value: 90.9
|
907 |
+
- type: ndcg_at_10
|
908 |
+
value: 85.564
|
909 |
+
- type: ndcg_at_100
|
910 |
+
value: 89.11200000000001
|
911 |
+
- type: ndcg_at_1000
|
912 |
+
value: 89.693
|
913 |
+
- type: ndcg_at_3
|
914 |
+
value: 87.024
|
915 |
+
- type: ndcg_at_5
|
916 |
+
value: 85.66
|
917 |
+
- type: precision_at_1
|
918 |
+
value: 90.9
|
919 |
+
- type: precision_at_10
|
920 |
+
value: 42.208
|
921 |
+
- type: precision_at_100
|
922 |
+
value: 5.027
|
923 |
+
- type: precision_at_1000
|
924 |
+
value: 0.517
|
925 |
+
- type: precision_at_3
|
926 |
+
value: 75.872
|
927 |
+
- type: precision_at_5
|
928 |
+
value: 63.566
|
929 |
+
- type: recall_at_1
|
930 |
+
value: 28.102
|
931 |
+
- type: recall_at_10
|
932 |
+
value: 84.44500000000001
|
933 |
+
- type: recall_at_100
|
934 |
+
value: 95.91300000000001
|
935 |
+
- type: recall_at_1000
|
936 |
+
value: 98.80799999999999
|
937 |
+
- type: recall_at_3
|
938 |
+
value: 56.772999999999996
|
939 |
+
- type: recall_at_5
|
940 |
+
value: 70.99499999999999
|
941 |
+
- task:
|
942 |
+
type: Classification
|
943 |
+
dataset:
|
944 |
+
type: C-MTEB/TNews-classification
|
945 |
+
name: MTEB TNews
|
946 |
+
config: default
|
947 |
+
split: validation
|
948 |
+
revision: None
|
949 |
+
metrics:
|
950 |
+
- type: accuracy
|
951 |
+
value: 53.10599999999999
|
952 |
+
- type: f1
|
953 |
+
value: 51.40415523558322
|
954 |
+
- task:
|
955 |
+
type: Clustering
|
956 |
+
dataset:
|
957 |
+
type: C-MTEB/ThuNewsClusteringP2P
|
958 |
+
name: MTEB ThuNewsClusteringP2P
|
959 |
+
config: default
|
960 |
+
split: test
|
961 |
+
revision: None
|
962 |
+
metrics:
|
963 |
+
- type: v_measure
|
964 |
+
value: 69.6145576098232
|
965 |
+
- task:
|
966 |
+
type: Clustering
|
967 |
+
dataset:
|
968 |
+
type: C-MTEB/ThuNewsClusteringS2S
|
969 |
+
name: MTEB ThuNewsClusteringS2S
|
970 |
+
config: default
|
971 |
+
split: test
|
972 |
+
revision: None
|
973 |
+
metrics:
|
974 |
+
- type: v_measure
|
975 |
+
value: 63.7129548775017
|
976 |
+
- task:
|
977 |
+
type: Retrieval
|
978 |
+
dataset:
|
979 |
+
type: C-MTEB/VideoRetrieval
|
980 |
+
name: MTEB VideoRetrieval
|
981 |
+
config: default
|
982 |
+
split: dev
|
983 |
+
revision: None
|
984 |
+
metrics:
|
985 |
+
- type: map_at_1
|
986 |
+
value: 60.199999999999996
|
987 |
+
- type: map_at_10
|
988 |
+
value: 69.724
|
989 |
+
- type: map_at_100
|
990 |
+
value: 70.185
|
991 |
+
- type: map_at_1000
|
992 |
+
value: 70.196
|
993 |
+
- type: map_at_3
|
994 |
+
value: 67.95
|
995 |
+
- type: map_at_5
|
996 |
+
value: 69.155
|
997 |
+
- type: mrr_at_1
|
998 |
+
value: 60.199999999999996
|
999 |
+
- type: mrr_at_10
|
1000 |
+
value: 69.724
|
1001 |
+
- type: mrr_at_100
|
1002 |
+
value: 70.185
|
1003 |
+
- type: mrr_at_1000
|
1004 |
+
value: 70.196
|
1005 |
+
- type: mrr_at_3
|
1006 |
+
value: 67.95
|
1007 |
+
- type: mrr_at_5
|
1008 |
+
value: 69.155
|
1009 |
+
- type: ndcg_at_1
|
1010 |
+
value: 60.199999999999996
|
1011 |
+
- type: ndcg_at_10
|
1012 |
+
value: 73.888
|
1013 |
+
- type: ndcg_at_100
|
1014 |
+
value: 76.02799999999999
|
1015 |
+
- type: ndcg_at_1000
|
1016 |
+
value: 76.344
|
1017 |
+
- type: ndcg_at_3
|
1018 |
+
value: 70.384
|
1019 |
+
- type: ndcg_at_5
|
1020 |
+
value: 72.541
|
1021 |
+
- type: precision_at_1
|
1022 |
+
value: 60.199999999999996
|
1023 |
+
- type: precision_at_10
|
1024 |
+
value: 8.67
|
1025 |
+
- type: precision_at_100
|
1026 |
+
value: 0.9650000000000001
|
1027 |
+
- type: precision_at_1000
|
1028 |
+
value: 0.099
|
1029 |
+
- type: precision_at_3
|
1030 |
+
value: 25.8
|
1031 |
+
- type: precision_at_5
|
1032 |
+
value: 16.520000000000003
|
1033 |
+
- type: recall_at_1
|
1034 |
+
value: 60.199999999999996
|
1035 |
+
- type: recall_at_10
|
1036 |
+
value: 86.7
|
1037 |
+
- type: recall_at_100
|
1038 |
+
value: 96.5
|
1039 |
+
- type: recall_at_1000
|
1040 |
+
value: 99.0
|
1041 |
+
- type: recall_at_3
|
1042 |
+
value: 77.4
|
1043 |
+
- type: recall_at_5
|
1044 |
+
value: 82.6
|
1045 |
+
- task:
|
1046 |
+
type: Classification
|
1047 |
+
dataset:
|
1048 |
+
type: C-MTEB/waimai-classification
|
1049 |
+
name: MTEB Waimai
|
1050 |
+
config: default
|
1051 |
+
split: test
|
1052 |
+
revision: None
|
1053 |
+
metrics:
|
1054 |
+
- type: accuracy
|
1055 |
+
value: 88.08
|
1056 |
+
- type: ap
|
1057 |
+
value: 72.66435456846166
|
1058 |
+
- type: f1
|
1059 |
+
value: 86.55995793551286
|
1060 |
---
|
1061 |
+
|
1062 |
+
# 1 开源清单
|
1063 |
+
|
1064 |
+
本次开源2个通用向量编码模型和一个针对dialogue进行编码的向量模型,同时开源全量160万对话重写数据集和20万的难负例的检索数据集。
|
1065 |
+
|
1066 |
+
**开源模型:**
|
1067 |
+
|
1068 |
+
| ModelName | ModelSize | MaxTokens | EmbeddingDimensions | Language | Scenario | C-MTEB Score |
|
1069 |
+
|---------------------------------------------------------------------------------------------------------------|-----------|-----------|---------------------|----------|----------|--------------|
|
1070 |
+
| [infgrad/stella-base-zh-v3-1792d](https://huggingface.co/infgrad/stella-base-zh-v3-1792d) | 0.4GB | 512 | 1792 | zh-CN | 通用文本 | 67.96 |
|
1071 |
+
| [infgrad/stella-large-zh-v3-1792d](https://huggingface.co/infgrad/stella-large-zh-v3-1792d) | 1.3GB | 512 | 1792 | zh-CN | 通用文本 | 68.48 |
|
1072 |
+
| [infgrad/stella-dialogue-large-zh-v3-1792d](https://huggingface.co/infgrad/stella-dialogue-large-zh-v3-1792d) | 1.3GB | 512 | 1792 | zh-CN | **对话文本** | 不适用 |
|
1073 |
+
|
1074 |
+
**开源数据:**
|
1075 |
+
|
1076 |
+
1. [全量对话重写数据集](https://huggingface.co/datasets/infgrad/dialogue_rewrite_llm) 约160万
|
1077 |
+
2. [部分带有难负例的检索数据集](https://huggingface.co/datasets/infgrad/retrieval_data_llm) 约20万
|
1078 |
+
|
1079 |
+
上述数据集均使用LLM构造,欢迎各位贡献数据集。
|
1080 |
+
|
1081 |
+
# 2 使用方法
|
1082 |
+
|
1083 |
+
## 2.1 通用编码模型使用方法
|
1084 |
+
|
1085 |
+
直接SentenceTransformer加载即可:
|
1086 |
+
|
1087 |
+
```python
|
1088 |
+
from sentence_transformers import SentenceTransformer
|
1089 |
+
|
1090 |
+
model = SentenceTransformer("infgrad/stella-base-zh-v3-1792d")
|
1091 |
+
# model = SentenceTransformer("infgrad/stella-large-zh-v3-1792d")
|
1092 |
+
vectors = model.encode(["text1", "text2"])
|
1093 |
+
```
|
1094 |
+
|
1095 |
+
## 2.2 dialogue编码模型使用方法
|
1096 |
+
|
1097 |
+
**使用场景:**
|
1098 |
+
**在一段对话中,需要根据用户语句去检索相关文本,但是对话中的用户语句存在大量的指代和省略,导致直接使用通用编码模型效果不好,
|
1099 |
+
可以使用本项目的专门的dialogue编码模型进行编码**
|
1100 |
+
|
1101 |
+
**使用要点:**
|
1102 |
+
|
1103 |
+
1. 对dialogue进行编码时,dialogue中的每个utterance需要是如下格式:`"{ROLE}: {TEXT}"`,然后使用`[SEP]` join一下
|
1104 |
+
2. 整个对话都要送入模型进行编码,如果长度不够就删掉早期的对话,**编码后的向量本质是对话中最后一句话的重写版本的向量!!**
|
1105 |
+
3. 对话用stella-dialogue-large-zh-v3-1792d编码,被检索文本使用stella-large-zh-v3-1792d进行编码,所以本场景是需要2个编码模型的
|
1106 |
+
|
1107 |
+
如果对使用方法还有疑惑,请到下面章节阅读该模型是如何训练的。
|
1108 |
+
|
1109 |
+
使用示例:
|
1110 |
+
|
1111 |
+
```python
|
1112 |
+
from sentence_transformers import SentenceTransformer
|
1113 |
+
|
1114 |
+
dial_model = SentenceTransformer("infgrad/stella-dialogue-large-zh-v3-1792d")
|
1115 |
+
general_model = SentenceTransformer("infgrad/stella-large-zh-v3-1792d")
|
1116 |
+
# dialogue = ["张三: 吃饭吗", "李四: 等会去"]
|
1117 |
+
dialogue = ["A: 最近去打篮球了吗", "B: 没有"]
|
1118 |
+
corpus = ["B没打篮球是因为受伤了。", "B没有打乒乓球"]
|
1119 |
+
last_utterance_vector = dial_model.encode(["[SEP]".join(dialogue)], normalize_embeddings=True)
|
1120 |
+
corpus_vectors = general_model.encode(corpus, normalize_embeddings=True)
|
1121 |
+
# 计算相似度
|
1122 |
+
sims = (last_utterance_vector * corpus_vectors).sum(axis=1)
|
1123 |
+
print(sims)
|
1124 |
+
```
|
1125 |
+
|
1126 |
+
# 3 通用编码模型训练技巧分享
|
1127 |
+
|
1128 |
+
## hard negative
|
1129 |
+
|
1130 |
+
难负例挖掘也是个经典的trick了,几乎总能提升效果
|
1131 |
+
|
1132 |
+
## dropout-1d
|
1133 |
+
|
1134 |
+
dropout已经是深度学习的标配,我们可以稍微改造下使其更适合句向量的训练。
|
1135 |
+
我们在训练时会尝试让每一个token-embedding都可以表征整个句子,而在推理时使用mean_pooling从而达到类似模型融合的效果。
|
1136 |
+
具体操作是在mean_pooling时加入dropout_1d,torch代码如下:
|
1137 |
+
|
1138 |
+
```python
|
1139 |
+
vector_dropout = nn.Dropout1d(0.3) # 算力有限,试了0.3和0.5 两个参数,其中0.3更优
|
1140 |
+
last_hidden_state = bert_model(...)[0]
|
1141 |
+
last_hidden = last_hidden_state.masked_fill(~attention_mask[..., None].bool(), 0.0)
|
1142 |
+
last_hidden = vector_dropout(last_hidden)
|
1143 |
+
vectors = last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
|
1144 |
+
```
|
1145 |
+
|
1146 |
+
# 4 dialogue编码模型细节
|
1147 |
+
|
1148 |
+
## 4.1 为什么需要一个dialogue编码模型?
|
1149 |
+
|
1150 |
+
参见本人历史文章:https://www.zhihu.com/pin/1674913544847077376
|
1151 |
+
|
1152 |
+
## 4.2 训练数据
|
1153 |
+
|
1154 |
+
单条数据示例:
|
1155 |
+
|
1156 |
+
```json
|
1157 |
+
{
|
1158 |
+
"dialogue": [
|
1159 |
+
"A: 最近去打篮球了吗",
|
1160 |
+
"B: 没有"
|
1161 |
+
],
|
1162 |
+
"last_utterance_rewrite": "B: 我最近没有去打篮球"
|
1163 |
+
}
|
1164 |
+
```
|
1165 |
+
|
1166 |
+
## 4.3 训练Loss
|
1167 |
+
|
1168 |
+
```
|
1169 |
+
loss = cosine_loss( dial_model.encode(dialogue), existing_model.encode(last_utterance_rewrite) )
|
1170 |
+
```
|
1171 |
+
|
1172 |
+
dial_model就是要被训练的模型,本人是以stella-large-zh-v3-1792d作为base-model进行继续训练的
|
1173 |
+
|
1174 |
+
existing_model就是现有训练好的**通用编码模型**,本人使用的是stella-large-zh-v3-1792d
|
1175 |
+
|
1176 |
+
已开源dialogue-embedding的全量训练数据,理论上可以复现本模型效果。
|
1177 |
+
|
1178 |
+
Loss下降情况:
|
1179 |
+
|
1180 |
+
<div align="center">
|
1181 |
+
<img src="dial_loss.png" alt="icon" width="2000px"/>
|
1182 |
+
</div>
|
1183 |
+
|
1184 |
+
## 4.4 效果
|
1185 |
+
|
1186 |
+
目前还没有专门测试集,本人简单测试了下是有效果的,部分测试结果见文件`dial_retrieval_test.xlsx`。
|
1187 |
+
|
1188 |
+
# 5 后续TODO
|
1189 |
+
|
1190 |
+
1. 更多的dial-rewrite数据
|
1191 |
+
2. 不同EmbeddingDimensions的编码模型
|
1192 |
+
|
1193 |
+
# 6 FAQ
|
1194 |
+
|
1195 |
+
Q: 为什么向量维度是1792?\
|
1196 |
+
A: 最初考虑发布768、1024,768+768,1024+1024,1024+768维度,但是时间有限,先做了1792就只发布1792维度的模型。理论上维度越高效果越好。
|
1197 |
+
|
1198 |
+
Q: 如何复现CMTEB效果?\
|
1199 |
+
A: SentenceTransformer加载后直接用官方评测脚本就行,注意对于Classification任务向量需要先normalize一下
|
1200 |
+
|
1201 |
+
Q: 复现的CMTEB效果和本文不一致?\
|
1202 |
+
A: 聚类不一致正常,官方评测代码没有设定seed,其他不一致建议检查代码或联系本人。
|
1203 |
+
|
1204 |
+
Q: 如何选择向量模型?\
|
1205 |
+
A: 没有免费的午餐,在自己测试集上试试,本人推荐bge、e5和stella.
|
1206 |
+
|
1207 |
+
Q: 长度为什么只有512,能否更长?\
|
1208 |
+
A: 可以但没必要,长了效果普遍不好,这是当前训练方法和数据导致的,几乎无解,建议长文本还是走分块。
|
1209 |
+
|
1210 |
+
Q: 训练资源和算力?\
|
1211 |
+
A: 亿级别的数据,单卡A100要一个月起步
|