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Gameselo
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STS-multilingual-mpnet-base-v2

Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
mteb
feature-extraction
dataset_size:100K<n<1M
loss:AnglELoss
Eval Results (legacy)
text-embeddings-inference
Model card Files
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 Community
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Gameselo commited on
Commit
88edc46
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1 Parent(s): 1366ac4

Add new SentenceTransformer model.

Browse files
Files changed (12) hide show
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  2. 1_Pooling/config.json +10 -0
  3. README.md +1861 -0
  4. config.json +29 -0
  5. config_sentence_transformers.json +10 -0
  6. model.safetensors +3 -0
  7. modules.json +14 -0
  8. sentence_bert_config.json +4 -0
  9. sentencepiece.bpe.model +3 -0
  10. special_tokens_map.json +51 -0
  11. tokenizer.json +3 -0
  12. tokenizer_config.json +61 -0
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+ ---
2
+ language: []
3
+ library_name: sentence-transformers
4
+ tags:
5
+ - sentence-transformers
6
+ - sentence-similarity
7
+ - feature-extraction
8
+ - dataset_size:100K<n<1M
9
+ - loss:AnglELoss
10
+ base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
11
+ metrics:
12
+ - pearson_cosine
13
+ - spearman_cosine
14
+ - pearson_manhattan
15
+ - spearman_manhattan
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+ - pearson_euclidean
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+ - spearman_euclidean
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+ - pearson_dot
19
+ - spearman_dot
20
+ - pearson_max
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+ - spearman_max
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+ widget:
23
+ - source_sentence: 有些人在路上溜达。
24
+ sentences:
25
+ - Folk går
26
+ - Otururken gitar çalan adam.
27
+ - ארה"ב קבעה שסוריה השתמשה בנשק כימי
28
+ - source_sentence: 緬甸以前稱為緬甸。
29
+ sentences:
30
+ - 缅甸以前叫缅甸。
31
+ - This is very contradictory.
32
+ - 한 남자가 아기를 안고 의자에 앉아 잠들어 있다.
33
+ - source_sentence: אדם כותב.
34
+ sentences:
35
+ - האדם כותב.
36
+ - questa non è una risposta.
37
+ - 7 שוטרים נהרגו ו-4 שוטרים נפצעו.
38
+ - source_sentence: הם מפחדים.
39
+ sentences:
40
+ - liên quan đến rủi ro đáng kể;
41
+ - A man is playing a guitar.
42
+ - A man is playing a piano.
43
+ - source_sentence: 一个女人正在洗澡。
44
+ sentences:
45
+ - A woman is taking a bath.
46
+ - En jente børster håret sitt
47
+ - אדם מחלק תפוח אדמה.
48
+ pipeline_tag: sentence-similarity
49
+ model-index:
50
+ - name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2
51
+ results:
52
+ - task:
53
+ type: semantic-similarity
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+ name: Semantic Similarity
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+ dataset:
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+ name: sts dev
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+ type: sts-dev
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+ metrics:
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+ - type: pearson_cosine
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+ value: 0.9551466915019567
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+ name: Spearman Max
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+ - task:
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+ type: semantic-similarity
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+ name: Semantic Similarity
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+ dataset:
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+ name: sts test
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+ type: sts-test
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+ metrics:
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+ value: 0.9479585032380113
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+
998
+ # SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2
999
+
1000
+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
1001
+
1002
+ ## Model Details
1003
+
1004
+ ### Model Description
1005
+ - **Model Type:** Sentence Transformer
1006
+ - **Base model:** [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) <!-- at revision 79f2382ceacceacdf38563d7c5d16b9ff8d725d6 -->
1007
+ - **Maximum Sequence Length:** 128 tokens
1008
+ - **Output Dimensionality:** 768 tokens
1009
+ - **Similarity Function:** Cosine Similarity
1010
+ <!-- - **Training Dataset:** Unknown -->
1011
+ <!-- - **Language:** Unknown -->
1012
+ <!-- - **License:** Unknown -->
1013
+
1014
+ ### Model Sources
1015
+
1016
+ - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
1017
+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
1018
+ - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
1019
+
1020
+ ### Full Model Architecture
1021
+
1022
+ ```
1023
+ SentenceTransformer(
1024
+ (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
1025
+ (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
1026
+ )
1027
+ ```
1028
+
1029
+ ## Usage
1030
+
1031
+ ### Direct Usage (Sentence Transformers)
1032
+
1033
+ First install the Sentence Transformers library:
1034
+
1035
+ ```bash
1036
+ pip install -U sentence-transformers
1037
+ ```
1038
+
1039
+ Then you can load this model and run inference.
1040
+ ```python
1041
+ from sentence_transformers import SentenceTransformer
1042
+
1043
+ # Download from the 🤗 Hub
1044
+ model = SentenceTransformer("Gameselo/STS-multilingual-mpnet-base-v2")
1045
+ # Run inference
1046
+ sentences = [
1047
+ '一个女人正在洗澡。',
1048
+ 'A woman is taking a bath.',
1049
+ 'En jente børster håret sitt',
1050
+ ]
1051
+ embeddings = model.encode(sentences)
1052
+ print(embeddings.shape)
1053
+ # [3, 768]
1054
+
1055
+ # Get the similarity scores for the embeddings
1056
+ similarities = model.similarity(embeddings, embeddings)
1057
+ print(similarities.shape)
1058
+ # [3, 3]
1059
+ ```
1060
+
1061
+ <!--
1062
+ ### Direct Usage (Transformers)
1063
+
1064
+ <details><summary>Click to see the direct usage in Transformers</summary>
1065
+
1066
+ </details>
1067
+ -->
1068
+
1069
+ <!--
1070
+ ### Downstream Usage (Sentence Transformers)
1071
+
1072
+ You can finetune this model on your own dataset.
1073
+
1074
+ <details><summary>Click to expand</summary>
1075
+
1076
+ </details>
1077
+ -->
1078
+
1079
+ <!--
1080
+ ### Out-of-Scope Use
1081
+
1082
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
1083
+ -->
1084
+
1085
+ ## Evaluation
1086
+
1087
+ ### Metrics
1088
+
1089
+ #### Semantic Similarity
1090
+ * Dataset: `sts-dev`
1091
+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
1092
+
1093
+ | Metric | Value |
1094
+ |:--------------------|:-----------|
1095
+ | pearson_cosine | 0.9551 |
1096
+ | **spearman_cosine** | **0.9593** |
1097
+ | pearson_manhattan | 0.927 |
1098
+ | spearman_manhattan | 0.9383 |
1099
+ | pearson_euclidean | 0.9278 |
1100
+ | spearman_euclidean | 0.9394 |
1101
+ | pearson_dot | 0.876 |
1102
+ | spearman_dot | 0.8865 |
1103
+ | pearson_max | 0.9551 |
1104
+ | spearman_max | 0.9593 |
1105
+
1106
+ #### Semantic Similarity
1107
+ * Dataset: `sts-test`
1108
+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
1109
+
1110
+ | Metric | Value |
1111
+ |:--------------------|:-----------|
1112
+ | pearson_cosine | 0.948 |
1113
+ | **spearman_cosine** | **0.9515** |
1114
+ | pearson_manhattan | 0.9252 |
1115
+ | spearman_manhattan | 0.9352 |
1116
+ | pearson_euclidean | 0.9258 |
1117
+ | spearman_euclidean | 0.9364 |
1118
+ | pearson_dot | 0.8443 |
1119
+ | spearman_dot | 0.8435 |
1120
+ | pearson_max | 0.948 |
1121
+ | spearman_max | 0.9515 |
1122
+
1123
+ #### Semantic Similarity
1124
+ * Dataset: `sts-test`
1125
+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
1126
+
1127
+ | Metric | Value |
1128
+ |:--------------------|:-----------|
1129
+ | pearson_cosine | 0.9725 |
1130
+ | **spearman_cosine** | **0.9766** |
1131
+ | pearson_manhattan | 0.9382 |
1132
+ | spearman_manhattan | 0.9487 |
1133
+ | pearson_euclidean | 0.9392 |
1134
+ | spearman_euclidean | 0.95 |
1135
+ | pearson_dot | 0.8531 |
1136
+ | spearman_dot | 0.8611 |
1137
+ | pearson_max | 0.9725 |
1138
+ | spearman_max | 0.9766 |
1139
+
1140
+ #### Semantic Similarity
1141
+ * Dataset: `sts-test`
1142
+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
1143
+
1144
+ | Metric | Value |
1145
+ |:--------------------|:-----------|
1146
+ | pearson_cosine | 0.8027 |
1147
+ | **spearman_cosine** | **0.8124** |
1148
+ | pearson_manhattan | 0.7839 |
1149
+ | spearman_manhattan | 0.79 |
1150
+ | pearson_euclidean | 0.7836 |
1151
+ | spearman_euclidean | 0.792 |
1152
+ | pearson_dot | 0.7699 |
1153
+ | spearman_dot | 0.782 |
1154
+ | pearson_max | 0.8027 |
1155
+ | spearman_max | 0.8124 |
1156
+
1157
+ #### Semantic Similarity
1158
+ * Dataset: `sts-test`
1159
+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
1160
+
1161
+ | Metric | Value |
1162
+ |:--------------------|:-----------|
1163
+ | pearson_cosine | 0.7796 |
1164
+ | **spearman_cosine** | **0.7703** |
1165
+ | pearson_manhattan | 0.7904 |
1166
+ | spearman_manhattan | 0.783 |
1167
+ | pearson_euclidean | 0.7912 |
1168
+ | spearman_euclidean | 0.7842 |
1169
+ | pearson_dot | 0.7077 |
1170
+ | spearman_dot | 0.6914 |
1171
+ | pearson_max | 0.7912 |
1172
+ | spearman_max | 0.7842 |
1173
+
1174
+ #### Semantic Similarity
1175
+ * Dataset: `sts-test`
1176
+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
1177
+
1178
+ | Metric | Value |
1179
+ |:--------------------|:-----------|
1180
+ | pearson_cosine | 0.9113 |
1181
+ | **spearman_cosine** | **0.9109** |
1182
+ | pearson_manhattan | 0.897 |
1183
+ | spearman_manhattan | 0.8934 |
1184
+ | pearson_euclidean | 0.8986 |
1185
+ | spearman_euclidean | 0.8955 |
1186
+ | pearson_dot | 0.8844 |
1187
+ | spearman_dot | 0.8923 |
1188
+ | pearson_max | 0.9113 |
1189
+ | spearman_max | 0.9109 |
1190
+
1191
+ #### Semantic Similarity
1192
+ * Dataset: `sts-test`
1193
+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
1194
+
1195
+ | Metric | Value |
1196
+ |:--------------------|:-----------|
1197
+ | pearson_cosine | 0.9362 |
1198
+ | **spearman_cosine** | **0.9379** |
1199
+ | pearson_manhattan | 0.923 |
1200
+ | spearman_manhattan | 0.9245 |
1201
+ | pearson_euclidean | 0.9231 |
1202
+ | spearman_euclidean | 0.9251 |
1203
+ | pearson_dot | 0.907 |
1204
+ | spearman_dot | 0.9186 |
1205
+ | pearson_max | 0.9362 |
1206
+ | spearman_max | 0.9379 |
1207
+
1208
+ #### Semantic Similarity
1209
+ * Dataset: `sts-test`
1210
+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
1211
+
1212
+ | Metric | Value |
1213
+ |:--------------------|:-----------|
1214
+ | pearson_cosine | 0.8049 |
1215
+ | **spearman_cosine** | **0.7987** |
1216
+ | pearson_manhattan | 0.8018 |
1217
+ | spearman_manhattan | 0.7828 |
1218
+ | pearson_euclidean | 0.8007 |
1219
+ | spearman_euclidean | 0.7825 |
1220
+ | pearson_dot | 0.7895 |
1221
+ | spearman_dot | 0.7819 |
1222
+ | pearson_max | 0.8049 |
1223
+ | spearman_max | 0.7987 |
1224
+
1225
+ #### Semantic Similarity
1226
+ * Dataset: `sts-test`
1227
+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
1228
+
1229
+ | Metric | Value |
1230
+ |:--------------------|:-----------|
1231
+ | pearson_cosine | 0.852 |
1232
+ | **spearman_cosine** | **0.8553** |
1233
+ | pearson_manhattan | 0.8464 |
1234
+ | spearman_manhattan | 0.841 |
1235
+ | pearson_euclidean | 0.8468 |
1236
+ | spearman_euclidean | 0.8459 |
1237
+ | pearson_dot | 0.8093 |
1238
+ | spearman_dot | 0.8154 |
1239
+ | pearson_max | 0.852 |
1240
+ | spearman_max | 0.8553 |
1241
+
1242
+ #### Semantic Similarity
1243
+ * Dataset: `sts-test`
1244
+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
1245
+
1246
+ | Metric | Value |
1247
+ |:--------------------|:-----------|
1248
+ | pearson_cosine | 0.8752 |
1249
+ | **spearman_cosine** | **0.8727** |
1250
+ | pearson_manhattan | 0.8745 |
1251
+ | spearman_manhattan | 0.8661 |
1252
+ | pearson_euclidean | 0.8748 |
1253
+ | spearman_euclidean | 0.8668 |
1254
+ | pearson_dot | 0.8603 |
1255
+ | spearman_dot | 0.852 |
1256
+ | pearson_max | 0.8752 |
1257
+ | spearman_max | 0.8727 |
1258
+
1259
+ #### Semantic Similarity
1260
+ * Dataset: `sts-test`
1261
+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
1262
+
1263
+ | Metric | Value |
1264
+ |:--------------------|:-----------|
1265
+ | pearson_cosine | 0.9082 |
1266
+ | **spearman_cosine** | **0.9068** |
1267
+ | pearson_manhattan | 0.8908 |
1268
+ | spearman_manhattan | 0.8852 |
1269
+ | pearson_euclidean | 0.8908 |
1270
+ | spearman_euclidean | 0.8851 |
1271
+ | pearson_dot | 0.8889 |
1272
+ | spearman_dot | 0.8966 |
1273
+ | pearson_max | 0.9082 |
1274
+ | spearman_max | 0.9068 |
1275
+
1276
+ #### Semantic Similarity
1277
+ * Dataset: `sts-test`
1278
+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
1279
+
1280
+ | Metric | Value |
1281
+ |:--------------------|:-----------|
1282
+ | pearson_cosine | 0.925 |
1283
+ | **spearman_cosine** | **0.9247** |
1284
+ | pearson_manhattan | 0.9084 |
1285
+ | spearman_manhattan | 0.9029 |
1286
+ | pearson_euclidean | 0.9116 |
1287
+ | spearman_euclidean | 0.9084 |
1288
+ | pearson_dot | 0.9001 |
1289
+ | spearman_dot | 0.907 |
1290
+ | pearson_max | 0.925 |
1291
+ | spearman_max | 0.9247 |
1292
+
1293
+ #### Semantic Similarity
1294
+ * Dataset: `sts-test`
1295
+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
1296
+
1297
+ | Metric | Value |
1298
+ |:--------------------|:-----------|
1299
+ | pearson_cosine | 0.9133 |
1300
+ | **spearman_cosine** | **0.9115** |
1301
+ | pearson_manhattan | 0.8977 |
1302
+ | spearman_manhattan | 0.8933 |
1303
+ | pearson_euclidean | 0.8979 |
1304
+ | spearman_euclidean | 0.8937 |
1305
+ | pearson_dot | 0.8912 |
1306
+ | spearman_dot | 0.8988 |
1307
+ | pearson_max | 0.9133 |
1308
+ | spearman_max | 0.9115 |
1309
+
1310
+ #### Semantic Similarity
1311
+ * Dataset: `sts-test`
1312
+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
1313
+
1314
+ | Metric | Value |
1315
+ |:--------------------|:-----------|
1316
+ | pearson_cosine | 0.8985 |
1317
+ | **spearman_cosine** | **0.8452** |
1318
+ | pearson_manhattan | 0.8715 |
1319
+ | spearman_manhattan | 0.8452 |
1320
+ | pearson_euclidean | 0.8809 |
1321
+ | spearman_euclidean | 0.8452 |
1322
+ | pearson_dot | 0.8538 |
1323
+ | spearman_dot | 0.8452 |
1324
+ | pearson_max | 0.8985 |
1325
+ | spearman_max | 0.8452 |
1326
+
1327
+ #### Semantic Similarity
1328
+ * Dataset: `sts-test`
1329
+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
1330
+
1331
+ | Metric | Value |
1332
+ |:--------------------|:-----------|
1333
+ | pearson_cosine | 0.6495 |
1334
+ | **spearman_cosine** | **0.6385** |
1335
+ | pearson_manhattan | 0.6429 |
1336
+ | spearman_manhattan | 0.6474 |
1337
+ | pearson_euclidean | 0.6443 |
1338
+ | spearman_euclidean | 0.6445 |
1339
+ | pearson_dot | 0.6128 |
1340
+ | spearman_dot | 0.6108 |
1341
+ | pearson_max | 0.6495 |
1342
+ | spearman_max | 0.6474 |
1343
+
1344
+ #### Semantic Similarity
1345
+ * Dataset: `sts-test`
1346
+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
1347
+
1348
+ | Metric | Value |
1349
+ |:--------------------|:-----------|
1350
+ | pearson_cosine | 0.7441 |
1351
+ | **spearman_cosine** | **0.7518** |
1352
+ | pearson_manhattan | 0.7339 |
1353
+ | spearman_manhattan | 0.7367 |
1354
+ | pearson_euclidean | 0.7337 |
1355
+ | spearman_euclidean | 0.7342 |
1356
+ | pearson_dot | 0.6886 |
1357
+ | spearman_dot | 0.6986 |
1358
+ | pearson_max | 0.7441 |
1359
+ | spearman_max | 0.7518 |
1360
+
1361
+ #### Semantic Similarity
1362
+ * Dataset: `sts-test`
1363
+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
1364
+
1365
+ | Metric | Value |
1366
+ |:--------------------|:-----------|
1367
+ | pearson_cosine | 0.6279 |
1368
+ | **spearman_cosine** | **0.6319** |
1369
+ | pearson_manhattan | 0.5435 |
1370
+ | spearman_manhattan | 0.6002 |
1371
+ | pearson_euclidean | 0.54 |
1372
+ | spearman_euclidean | 0.5955 |
1373
+ | pearson_dot | 0.5658 |
1374
+ | spearman_dot | 0.6069 |
1375
+ | pearson_max | 0.6279 |
1376
+ | spearman_max | 0.6319 |
1377
+
1378
+ #### Semantic Similarity
1379
+ * Dataset: `sts-test`
1380
+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
1381
+
1382
+ | Metric | Value |
1383
+ |:--------------------|:-----------|
1384
+ | pearson_cosine | 0.7779 |
1385
+ | **spearman_cosine** | **0.7876** |
1386
+ | pearson_manhattan | 0.7426 |
1387
+ | spearman_manhattan | 0.7789 |
1388
+ | pearson_euclidean | 0.7437 |
1389
+ | spearman_euclidean | 0.7806 |
1390
+ | pearson_dot | 0.7214 |
1391
+ | spearman_dot | 0.7489 |
1392
+ | pearson_max | 0.7779 |
1393
+ | spearman_max | 0.7876 |
1394
+
1395
+ #### Semantic Similarity
1396
+ * Dataset: `sts-test`
1397
+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
1398
+
1399
+ | Metric | Value |
1400
+ |:--------------------|:-----------|
1401
+ | pearson_cosine | 0.5268 |
1402
+ | **spearman_cosine** | **0.5774** |
1403
+ | pearson_manhattan | 0.4171 |
1404
+ | spearman_manhattan | 0.56 |
1405
+ | pearson_euclidean | 0.4219 |
1406
+ | spearman_euclidean | 0.5665 |
1407
+ | pearson_dot | 0.4981 |
1408
+ | spearman_dot | 0.5367 |
1409
+ | pearson_max | 0.5268 |
1410
+ | spearman_max | 0.5774 |
1411
+
1412
+ #### Semantic Similarity
1413
+ * Dataset: `sts-test`
1414
+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
1415
+
1416
+ | Metric | Value |
1417
+ |:--------------------|:-----------|
1418
+ | pearson_cosine | 0.6306 |
1419
+ | **spearman_cosine** | **0.6384** |
1420
+ | pearson_manhattan | 0.6034 |
1421
+ | spearman_manhattan | 0.6168 |
1422
+ | pearson_euclidean | 0.6081 |
1423
+ | spearman_euclidean | 0.622 |
1424
+ | pearson_dot | 0.5767 |
1425
+ | spearman_dot | 0.5831 |
1426
+ | pearson_max | 0.6306 |
1427
+ | spearman_max | 0.6384 |
1428
+
1429
+ #### Semantic Similarity
1430
+ * Dataset: `sts-test`
1431
+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
1432
+
1433
+ | Metric | Value |
1434
+ |:--------------------|:-----------|
1435
+ | pearson_cosine | 0.5568 |
1436
+ | **spearman_cosine** | **0.5867** |
1437
+ | pearson_manhattan | 0.4924 |
1438
+ | spearman_manhattan | 0.5738 |
1439
+ | pearson_euclidean | 0.4906 |
1440
+ | spearman_euclidean | 0.5762 |
1441
+ | pearson_dot | 0.4307 |
1442
+ | spearman_dot | 0.5471 |
1443
+ | pearson_max | 0.5568 |
1444
+ | spearman_max | 0.5867 |
1445
+
1446
+ #### Semantic Similarity
1447
+ * Dataset: `sts-test`
1448
+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
1449
+
1450
+ | Metric | Value |
1451
+ |:--------------------|:----------|
1452
+ | pearson_cosine | 0.5776 |
1453
+ | **spearman_cosine** | **0.575** |
1454
+ | pearson_manhattan | 0.5718 |
1455
+ | spearman_manhattan | 0.5501 |
1456
+ | pearson_euclidean | 0.5695 |
1457
+ | spearman_euclidean | 0.5532 |
1458
+ | pearson_dot | 0.5315 |
1459
+ | spearman_dot | 0.5191 |
1460
+ | pearson_max | 0.5776 |
1461
+ | spearman_max | 0.575 |
1462
+
1463
+ #### Semantic Similarity
1464
+ * Dataset: `sts-test`
1465
+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
1466
+
1467
+ | Metric | Value |
1468
+ |:--------------------|:-----------|
1469
+ | pearson_cosine | 0.3572 |
1470
+ | **spearman_cosine** | **0.4336** |
1471
+ | pearson_manhattan | 0.2081 |
1472
+ | spearman_manhattan | 0.4355 |
1473
+ | pearson_euclidean | 0.2086 |
1474
+ | spearman_euclidean | 0.4402 |
1475
+ | pearson_dot | 0.2234 |
1476
+ | spearman_dot | 0.3707 |
1477
+ | pearson_max | 0.3572 |
1478
+ | spearman_max | 0.4402 |
1479
+
1480
+ #### Semantic Similarity
1481
+ * Dataset: `sts-test`
1482
+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
1483
+
1484
+ | Metric | Value |
1485
+ |:--------------------|:-----------|
1486
+ | pearson_cosine | 0.6863 |
1487
+ | **spearman_cosine** | **0.6621** |
1488
+ | pearson_manhattan | 0.6429 |
1489
+ | spearman_manhattan | 0.6484 |
1490
+ | pearson_euclidean | 0.6424 |
1491
+ | spearman_euclidean | 0.6486 |
1492
+ | pearson_dot | 0.6352 |
1493
+ | spearman_dot | 0.6159 |
1494
+ | pearson_max | 0.6863 |
1495
+ | spearman_max | 0.6621 |
1496
+
1497
+ #### Semantic Similarity
1498
+ * Dataset: `sts-test`
1499
+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
1500
+
1501
+ | Metric | Value |
1502
+ |:--------------------|:-----------|
1503
+ | pearson_cosine | 0.757 |
1504
+ | **spearman_cosine** | **0.7511** |
1505
+ | pearson_manhattan | 0.7191 |
1506
+ | spearman_manhattan | 0.714 |
1507
+ | pearson_euclidean | 0.7204 |
1508
+ | spearman_euclidean | 0.7258 |
1509
+ | pearson_dot | 0.7144 |
1510
+ | spearman_dot | 0.7284 |
1511
+ | pearson_max | 0.757 |
1512
+ | spearman_max | 0.7511 |
1513
+
1514
+ #### Semantic Similarity
1515
+ * Dataset: `sts-test`
1516
+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
1517
+
1518
+ | Metric | Value |
1519
+ |:--------------------|:-----------|
1520
+ | pearson_cosine | 0.6503 |
1521
+ | **spearman_cosine** | **0.6625** |
1522
+ | pearson_manhattan | 0.6474 |
1523
+ | spearman_manhattan | 0.659 |
1524
+ | pearson_euclidean | 0.6517 |
1525
+ | spearman_euclidean | 0.6667 |
1526
+ | pearson_dot | 0.5647 |
1527
+ | spearman_dot | 0.5702 |
1528
+ | pearson_max | 0.6517 |
1529
+ | spearman_max | 0.6667 |
1530
+
1531
+ #### Semantic Similarity
1532
+ * Dataset: `sts-test`
1533
+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
1534
+
1535
+ | Metric | Value |
1536
+ |:--------------------|:-----------|
1537
+ | pearson_cosine | 0.6774 |
1538
+ | **spearman_cosine** | **0.6537** |
1539
+ | pearson_manhattan | 0.6825 |
1540
+ | spearman_manhattan | 0.6325 |
1541
+ | pearson_euclidean | 0.6906 |
1542
+ | spearman_euclidean | 0.6407 |
1543
+ | pearson_dot | 0.5835 |
1544
+ | spearman_dot | 0.5962 |
1545
+ | pearson_max | 0.6906 |
1546
+ | spearman_max | 0.6537 |
1547
+
1548
+ #### Semantic Similarity
1549
+ * Dataset: `sts-test`
1550
+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
1551
+
1552
+ | Metric | Value |
1553
+ |:--------------------|:-----------|
1554
+ | pearson_cosine | 0.6709 |
1555
+ | **spearman_cosine** | **0.6847** |
1556
+ | pearson_manhattan | 0.6613 |
1557
+ | spearman_manhattan | 0.6907 |
1558
+ | pearson_euclidean | 0.6607 |
1559
+ | spearman_euclidean | 0.6881 |
1560
+ | pearson_dot | 0.6098 |
1561
+ | spearman_dot | 0.6195 |
1562
+ | pearson_max | 0.6709 |
1563
+ | spearman_max | 0.6907 |
1564
+
1565
+ #### Semantic Similarity
1566
+ * Dataset: `sts-test`
1567
+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
1568
+
1569
+ | Metric | Value |
1570
+ |:--------------------|:-----------|
1571
+ | pearson_cosine | 0.5977 |
1572
+ | **spearman_cosine** | **0.5799** |
1573
+ | pearson_manhattan | 0.5974 |
1574
+ | spearman_manhattan | 0.5953 |
1575
+ | pearson_euclidean | 0.5949 |
1576
+ | spearman_euclidean | 0.5936 |
1577
+ | pearson_dot | 0.5043 |
1578
+ | spearman_dot | 0.4968 |
1579
+ | pearson_max | 0.5977 |
1580
+ | spearman_max | 0.5953 |
1581
+
1582
+ #### Semantic Similarity
1583
+ * Dataset: `sts-test`
1584
+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
1585
+
1586
+ | Metric | Value |
1587
+ |:--------------------|:-----------|
1588
+ | pearson_cosine | 0.4562 |
1589
+ | **spearman_cosine** | **0.4422** |
1590
+ | pearson_manhattan | 0.4155 |
1591
+ | spearman_manhattan | 0.3837 |
1592
+ | pearson_euclidean | 0.4111 |
1593
+ | spearman_euclidean | 0.3822 |
1594
+ | pearson_dot | 0.4863 |
1595
+ | spearman_dot | 0.5303 |
1596
+ | pearson_max | 0.4863 |
1597
+ | spearman_max | 0.5303 |
1598
+
1599
+ #### Semantic Similarity
1600
+ * Dataset: `sts-test`
1601
+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
1602
+
1603
+ | Metric | Value |
1604
+ |:--------------------|:-----------|
1605
+ | pearson_cosine | 0.593 |
1606
+ | **spearman_cosine** | **0.6266** |
1607
+ | pearson_manhattan | 0.5608 |
1608
+ | spearman_manhattan | 0.6229 |
1609
+ | pearson_euclidean | 0.558 |
1610
+ | spearman_euclidean | 0.6202 |
1611
+ | pearson_dot | 0.4578 |
1612
+ | spearman_dot | 0.5628 |
1613
+ | pearson_max | 0.593 |
1614
+ | spearman_max | 0.6266 |
1615
+
1616
+ <!--
1617
+ ## Bias, Risks and Limitations
1618
+
1619
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
1620
+ -->
1621
+
1622
+ <!--
1623
+ ### Recommendations
1624
+
1625
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
1626
+ -->
1627
+
1628
+ ## Training Details
1629
+
1630
+ ### Training Dataset
1631
+
1632
+ #### Unnamed Dataset
1633
+
1634
+
1635
+ * Size: 226,547 training samples
1636
+ * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
1637
+ * Approximate statistics based on the first 1000 samples:
1638
+ | | sentence_0 | sentence_1 | label |
1639
+ |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------|
1640
+ | type | string | string | float |
1641
+ | details | <ul><li>min: 3 tokens</li><li>mean: 20.05 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 19.94 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 1.92</li><li>max: 398.6</li></ul> |
1642
+ * Samples:
1643
+ | sentence_0 | sentence_1 | label |
1644
+ |:-------------------------------------------------------------------|:----------------------------------------------------------------|:---------------------------------|
1645
+ | <code>Bir kadın makineye dikiş dikiyor.</code> | <code>Bir kadın biraz et ekiyor.</code> | <code>0.12</code> |
1646
+ | <code>Snowden 'gegeven vluchtelingendocument door Ecuador'.</code> | <code>Snowden staat op het punt om uit Moskou te vliegen</code> | <code>0.24000000953674316</code> |
1647
+ | <code>Czarny pies idzie mostem przez wodę</code> | <code>Czarny pies nie idzie mostem przez wodę</code> | <code>0.74000000954</code> |
1648
+ * Loss: [<code>AnglELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#angleloss) with these parameters:
1649
+ ```json
1650
+ {
1651
+ "scale": 20.0,
1652
+ "similarity_fct": "pairwise_angle_sim"
1653
+ }
1654
+ ```
1655
+
1656
+ ### Training Hyperparameters
1657
+ #### Non-Default Hyperparameters
1658
+
1659
+ - `per_device_train_batch_size`: 256
1660
+ - `per_device_eval_batch_size`: 256
1661
+ - `num_train_epochs`: 10
1662
+ - `multi_dataset_batch_sampler`: round_robin
1663
+
1664
+ #### All Hyperparameters
1665
+ <details><summary>Click to expand</summary>
1666
+
1667
+ - `overwrite_output_dir`: False
1668
+ - `do_predict`: False
1669
+ - `prediction_loss_only`: True
1670
+ - `per_device_train_batch_size`: 256
1671
+ - `per_device_eval_batch_size`: 256
1672
+ - `per_gpu_train_batch_size`: None
1673
+ - `per_gpu_eval_batch_size`: None
1674
+ - `gradient_accumulation_steps`: 1
1675
+ - `eval_accumulation_steps`: None
1676
+ - `learning_rate`: 5e-05
1677
+ - `weight_decay`: 0.0
1678
+ - `adam_beta1`: 0.9
1679
+ - `adam_beta2`: 0.999
1680
+ - `adam_epsilon`: 1e-08
1681
+ - `max_grad_norm`: 1
1682
+ - `num_train_epochs`: 10
1683
+ - `max_steps`: -1
1684
+ - `lr_scheduler_type`: linear
1685
+ - `lr_scheduler_kwargs`: {}
1686
+ - `warmup_ratio`: 0.0
1687
+ - `warmup_steps`: 0
1688
+ - `log_level`: passive
1689
+ - `log_level_replica`: warning
1690
+ - `log_on_each_node`: True
1691
+ - `logging_nan_inf_filter`: True
1692
+ - `save_safetensors`: True
1693
+ - `save_on_each_node`: False
1694
+ - `save_only_model`: False
1695
+ - `no_cuda`: False
1696
+ - `use_cpu`: False
1697
+ - `use_mps_device`: False
1698
+ - `seed`: 42
1699
+ - `data_seed`: None
1700
+ - `jit_mode_eval`: False
1701
+ - `use_ipex`: False
1702
+ - `bf16`: False
1703
+ - `fp16`: False
1704
+ - `fp16_opt_level`: O1
1705
+ - `half_precision_backend`: auto
1706
+ - `bf16_full_eval`: False
1707
+ - `fp16_full_eval`: False
1708
+ - `tf32`: None
1709
+ - `local_rank`: 0
1710
+ - `ddp_backend`: None
1711
+ - `tpu_num_cores`: None
1712
+ - `tpu_metrics_debug`: False
1713
+ - `debug`: []
1714
+ - `dataloader_drop_last`: False
1715
+ - `dataloader_num_workers`: 0
1716
+ - `dataloader_prefetch_factor`: None
1717
+ - `past_index`: -1
1718
+ - `disable_tqdm`: False
1719
+ - `remove_unused_columns`: True
1720
+ - `label_names`: None
1721
+ - `load_best_model_at_end`: False
1722
+ - `ignore_data_skip`: False
1723
+ - `fsdp`: []
1724
+ - `fsdp_min_num_params`: 0
1725
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
1726
+ - `fsdp_transformer_layer_cls_to_wrap`: None
1727
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'gradient_accumulation_kwargs': None}
1728
+ - `deepspeed`: None
1729
+ - `label_smoothing_factor`: 0.0
1730
+ - `optim`: adamw_torch
1731
+ - `optim_args`: None
1732
+ - `adafactor`: False
1733
+ - `group_by_length`: False
1734
+ - `length_column_name`: length
1735
+ - `ddp_find_unused_parameters`: None
1736
+ - `ddp_bucket_cap_mb`: None
1737
+ - `ddp_broadcast_buffers`: False
1738
+ - `dataloader_pin_memory`: True
1739
+ - `dataloader_persistent_workers`: False
1740
+ - `skip_memory_metrics`: True
1741
+ - `use_legacy_prediction_loop`: False
1742
+ - `push_to_hub`: False
1743
+ - `resume_from_checkpoint`: None
1744
+ - `hub_model_id`: None
1745
+ - `hub_strategy`: every_save
1746
+ - `hub_private_repo`: False
1747
+ - `hub_always_push`: False
1748
+ - `gradient_checkpointing`: False
1749
+ - `gradient_checkpointing_kwargs`: None
1750
+ - `include_inputs_for_metrics`: False
1751
+ - `eval_do_concat_batches`: True
1752
+ - `fp16_backend`: auto
1753
+ - `push_to_hub_model_id`: None
1754
+ - `push_to_hub_organization`: None
1755
+ - `mp_parameters`:
1756
+ - `auto_find_batch_size`: False
1757
+ - `full_determinism`: False
1758
+ - `torchdynamo`: None
1759
+ - `ray_scope`: last
1760
+ - `ddp_timeout`: 1800
1761
+ - `torch_compile`: False
1762
+ - `torch_compile_backend`: None
1763
+ - `torch_compile_mode`: None
1764
+ - `dispatch_batches`: None
1765
+ - `split_batches`: None
1766
+ - `include_tokens_per_second`: False
1767
+ - `include_num_input_tokens_seen`: False
1768
+ - `neftune_noise_alpha`: None
1769
+ - `optim_target_modules`: None
1770
+ - `batch_sampler`: batch_sampler
1771
+ - `multi_dataset_batch_sampler`: round_robin
1772
+
1773
+ </details>
1774
+
1775
+ ### Training Logs
1776
+ | Epoch | Step | Training Loss | sts-dev_spearman_cosine | sts-test_spearman_cosine |
1777
+ |:------:|:----:|:-------------:|:-----------------------:|:------------------------:|
1778
+ | 0.5650 | 500 | 10.9426 | - | - |
1779
+ | 1.0 | 885 | - | 0.9202 | - |
1780
+ | 1.1299 | 1000 | 9.7184 | - | - |
1781
+ | 1.6949 | 1500 | 9.5348 | - | - |
1782
+ | 2.0 | 1770 | - | 0.9400 | - |
1783
+ | 2.2599 | 2000 | 9.4412 | - | - |
1784
+ | 2.8249 | 2500 | 9.3097 | - | - |
1785
+ | 3.0 | 2655 | - | 0.9489 | - |
1786
+ | 3.3898 | 3000 | 9.2357 | - | - |
1787
+ | 3.9548 | 3500 | 9.1594 | - | - |
1788
+ | 4.0 | 3540 | - | 0.9528 | - |
1789
+ | 4.5198 | 4000 | 9.0963 | - | - |
1790
+ | 5.0 | 4425 | - | 0.9553 | - |
1791
+ | 5.0847 | 4500 | 9.0382 | - | - |
1792
+ | 5.6497 | 5000 | 8.9837 | - | - |
1793
+ | 6.0 | 5310 | - | 0.9567 | - |
1794
+ | 6.2147 | 5500 | 8.9403 | - | - |
1795
+ | 6.7797 | 6000 | 8.8841 | - | - |
1796
+ | 7.0 | 6195 | - | 0.9581 | - |
1797
+ | 7.3446 | 6500 | 8.8513 | - | - |
1798
+ | 7.9096 | 7000 | 8.81 | - | - |
1799
+ | 8.0 | 7080 | - | 0.9582 | - |
1800
+ | 8.4746 | 7500 | 8.8069 | - | - |
1801
+ | 9.0 | 7965 | - | 0.9589 | - |
1802
+ | 9.0395 | 8000 | 8.7616 | - | - |
1803
+ | 9.6045 | 8500 | 8.7521 | - | - |
1804
+ | 10.0 | 8850 | - | 0.9593 | 0.6266 |
1805
+
1806
+
1807
+ ### Framework Versions
1808
+ - Python: 3.9.7
1809
+ - Sentence Transformers: 3.0.0
1810
+ - Transformers: 4.40.1
1811
+ - PyTorch: 2.3.0+cu121
1812
+ - Accelerate: 0.29.3
1813
+ - Datasets: 2.19.0
1814
+ - Tokenizers: 0.19.1
1815
+
1816
+ ## Citation
1817
+
1818
+ ### BibTeX
1819
+
1820
+ #### Sentence Transformers
1821
+ ```bibtex
1822
+ @inproceedings{reimers-2019-sentence-bert,
1823
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
1824
+ author = "Reimers, Nils and Gurevych, Iryna",
1825
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
1826
+ month = "11",
1827
+ year = "2019",
1828
+ publisher = "Association for Computational Linguistics",
1829
+ url = "https://arxiv.org/abs/1908.10084",
1830
+ }
1831
+ ```
1832
+
1833
+ #### AnglELoss
1834
+ ```bibtex
1835
+ @misc{li2023angleoptimized,
1836
+ title={AnglE-optimized Text Embeddings},
1837
+ author={Xianming Li and Jing Li},
1838
+ year={2023},
1839
+ eprint={2309.12871},
1840
+ archivePrefix={arXiv},
1841
+ primaryClass={cs.CL}
1842
+ }
1843
+ ```
1844
+
1845
+ <!--
1846
+ ## Glossary
1847
+
1848
+ *Clearly define terms in order to be accessible across audiences.*
1849
+ -->
1850
+
1851
+ <!--
1852
+ ## Model Card Authors
1853
+
1854
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
1855
+ -->
1856
+
1857
+ <!--
1858
+ ## Model Card Contact
1859
+
1860
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
1861
+ -->
config.json ADDED
@@ -0,0 +1,29 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "sentence-transformers/paraphrase-multilingual-mpnet-base-v2",
3
+ "architectures": [
4
+ "XLMRobertaModel"
5
+ ],
6
+ "attention_probs_dropout_prob": 0.1,
7
+ "bos_token_id": 0,
8
+ "classifier_dropout": null,
9
+ "eos_token_id": 2,
10
+ "gradient_checkpointing": false,
11
+ "hidden_act": "gelu",
12
+ "hidden_dropout_prob": 0.1,
13
+ "hidden_size": 768,
14
+ "initializer_range": 0.02,
15
+ "intermediate_size": 3072,
16
+ "layer_norm_eps": 1e-05,
17
+ "max_position_embeddings": 514,
18
+ "model_type": "xlm-roberta",
19
+ "num_attention_heads": 12,
20
+ "num_hidden_layers": 12,
21
+ "output_past": true,
22
+ "pad_token_id": 1,
23
+ "position_embedding_type": "absolute",
24
+ "torch_dtype": "float32",
25
+ "transformers_version": "4.40.1",
26
+ "type_vocab_size": 1,
27
+ "use_cache": true,
28
+ "vocab_size": 250002
29
+ }
config_sentence_transformers.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
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