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sartifyllc
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swahili-gte-base-en-v1.5-nli-matryoshka

Sentence Similarity
sentence-transformers
Safetensors
new
feature-extraction
Generated from Trainer
dataset_size:557850
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
custom_code
Eval Results
text-embeddings-inference
Inference Endpoints
Model card Files Community
Mollel commited on
Commit
8d775fe
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1 Parent(s): 76022c7

Add new SentenceTransformer model.

Browse files
Files changed (11) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +982 -0
  3. config.json +44 -0
  4. config_sentence_transformers.json +10 -0
  5. model.safetensors +3 -0
  6. modules.json +14 -0
  7. sentence_bert_config.json +4 -0
  8. special_tokens_map.json +37 -0
  9. tokenizer.json +0 -0
  10. tokenizer_config.json +62 -0
  11. vocab.txt +0 -0
1_Pooling/config.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "word_embedding_dimension": 768,
3
+ "pooling_mode_cls_token": true,
4
+ "pooling_mode_mean_tokens": false,
5
+ "pooling_mode_max_tokens": false,
6
+ "pooling_mode_mean_sqrt_len_tokens": false,
7
+ "pooling_mode_weightedmean_tokens": false,
8
+ "pooling_mode_lasttoken": false,
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+ "include_prompt": true
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+ }
README.md ADDED
@@ -0,0 +1,982 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ language: []
3
+ library_name: sentence-transformers
4
+ tags:
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+ - sentence-transformers
6
+ - sentence-similarity
7
+ - feature-extraction
8
+ - generated_from_trainer
9
+ - dataset_size:557850
10
+ - loss:MatryoshkaLoss
11
+ - loss:MultipleNegativesRankingLoss
12
+ base_model: Alibaba-NLP/gte-base-en-v1.5
13
+ datasets: []
14
+ metrics:
15
+ - pearson_cosine
16
+ - spearman_cosine
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+ - pearson_manhattan
18
+ - spearman_manhattan
19
+ - pearson_euclidean
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+ - spearman_euclidean
21
+ - pearson_dot
22
+ - spearman_dot
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+ - pearson_max
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+ - spearman_max
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+ widget:
26
+ - source_sentence: Mwanamume aliyepangwa vizuri anasimama kwa mguu mmoja karibu na
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+ pwani safi ya bahari.
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+ sentences:
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+ - mtu anacheka wakati wa kufua nguo
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+ - Mwanamume fulani yuko nje karibu na ufuo wa bahari.
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+ - Mwanamume fulani ameketi kwenye sofa yake.
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+ - source_sentence: Mwanamume mwenye ngozi nyeusi akivuta sigareti karibu na chombo
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+ cha taka cha kijani.
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+ sentences:
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+ - Karibu na chombo cha taka mwanamume huyo alisimama na kuvuta sigareti
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+ - Kitanda ni chafu.
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+ - Alipokuwa kwenye dimbwi la kuogelea mvulana huyo mwenye ugonjwa wa albino alijihadhari
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+ na jua kupita kiasi
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+ - source_sentence: Mwanamume kijana mwenye nywele nyekundu anaketi ukutani akisoma
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+ gazeti huku mwanamke na msichana mchanga wakipita.
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+ sentences:
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+ - Mwanamume aliyevalia shati la bluu amegonga ukuta kando ya barabara na gari la
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+ bluu na gari nyekundu lenye maji nyuma.
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+ - Mwanamume mchanga anatazama gazeti huku wanawake wawili wakipita karibu naye.
45
+ - Mwanamume huyo mchanga analala huku Mama akimwongoza binti yake kwenye bustani.
46
+ - source_sentence: Wasichana wako nje.
47
+ sentences:
48
+ - Wasichana wawili wakisafiri kwenye sehemu ya kusisimua.
49
+ - Kuna watu watatu wakiongoza gari linaloweza kugeuzwa-geuzwa wakipita watu wengine.
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+ - Wasichana watatu wamesimama pamoja katika chumba, mmoja anasikiliza, mwingine
51
+ anaandika ukutani na wa tatu anaongea nao.
52
+ - source_sentence: Mwanamume aliyevalia koti la bluu la kuzuia upepo, amelala uso
53
+ chini kwenye benchi ya bustani, akiwa na chupa ya pombe iliyofungwa kwenye mojawapo
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+ ya miguu ya benchi.
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+ sentences:
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+ - Mwanamume amelala uso chini kwenye benchi ya bustani.
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+ - Mwanamke anaunganisha uzi katika mipira kando ya rundo la mipira
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+ - Mwanamume fulani anacheza dansi kwenye klabu hiyo akifungua chupa.
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+ pipeline_tag: sentence-similarity
60
+ model-index:
61
+ - name: SentenceTransformer based on Alibaba-NLP/gte-base-en-v1.5
62
+ results:
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+ - task:
64
+ type: semantic-similarity
65
+ name: Semantic Similarity
66
+ dataset:
67
+ name: sts test 768
68
+ type: sts-test-768
69
+ metrics:
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+ - type: pearson_cosine
71
+ value: 0.7043347377864616
72
+ name: Pearson Cosine
73
+ - type: spearman_cosine
74
+ value: 0.6964343322647693
75
+ name: Spearman Cosine
76
+ - type: pearson_manhattan
77
+ value: 0.6909108013214409
78
+ name: Pearson Manhattan
79
+ - type: spearman_manhattan
80
+ value: 0.6918757829517036
81
+ name: Spearman Manhattan
82
+ - type: pearson_euclidean
83
+ value: 0.6929234868177542
84
+ name: Pearson Euclidean
85
+ - type: spearman_euclidean
86
+ value: 0.6937500609344119
87
+ name: Spearman Euclidean
88
+ - type: pearson_dot
89
+ value: 0.70124411699517
90
+ name: Pearson Dot
91
+ - type: spearman_dot
92
+ value: 0.6918131755587139
93
+ name: Spearman Dot
94
+ - type: pearson_max
95
+ value: 0.7043347377864616
96
+ name: Pearson Max
97
+ - type: spearman_max
98
+ value: 0.6964343322647693
99
+ name: Spearman Max
100
+ - task:
101
+ type: semantic-similarity
102
+ name: Semantic Similarity
103
+ dataset:
104
+ name: sts test 512
105
+ type: sts-test-512
106
+ metrics:
107
+ - type: pearson_cosine
108
+ value: 0.7024370656682521
109
+ name: Pearson Cosine
110
+ - type: spearman_cosine
111
+ value: 0.6960997397306026
112
+ name: Spearman Cosine
113
+ - type: pearson_manhattan
114
+ value: 0.6937121372484026
115
+ name: Pearson Manhattan
116
+ - type: spearman_manhattan
117
+ value: 0.6942680507505805
118
+ name: Spearman Manhattan
119
+ - type: pearson_euclidean
120
+ value: 0.6958879339072266
121
+ name: Pearson Euclidean
122
+ - type: spearman_euclidean
123
+ value: 0.6965067811247516
124
+ name: Spearman Euclidean
125
+ - type: pearson_dot
126
+ value: 0.6739585793600888
127
+ name: Pearson Dot
128
+ - type: spearman_dot
129
+ value: 0.6635969331239819
130
+ name: Spearman Dot
131
+ - type: pearson_max
132
+ value: 0.7024370656682521
133
+ name: Pearson Max
134
+ - type: spearman_max
135
+ value: 0.6965067811247516
136
+ name: Spearman Max
137
+ - task:
138
+ type: semantic-similarity
139
+ name: Semantic Similarity
140
+ dataset:
141
+ name: sts test 256
142
+ type: sts-test-256
143
+ metrics:
144
+ - type: pearson_cosine
145
+ value: 0.6975572102129655
146
+ name: Pearson Cosine
147
+ - type: spearman_cosine
148
+ value: 0.6922084123611896
149
+ name: Spearman Cosine
150
+ - type: pearson_manhattan
151
+ value: 0.7012769244476563
152
+ name: Pearson Manhattan
153
+ - type: spearman_manhattan
154
+ value: 0.7002000478097333
155
+ name: Spearman Manhattan
156
+ - type: pearson_euclidean
157
+ value: 0.7033203116396916
158
+ name: Pearson Euclidean
159
+ - type: spearman_euclidean
160
+ value: 0.7027884000644871
161
+ name: Spearman Euclidean
162
+ - type: pearson_dot
163
+ value: 0.6353839704898405
164
+ name: Pearson Dot
165
+ - type: spearman_dot
166
+ value: 0.6242173680909447
167
+ name: Spearman Dot
168
+ - type: pearson_max
169
+ value: 0.7033203116396916
170
+ name: Pearson Max
171
+ - type: spearman_max
172
+ value: 0.7027884000644871
173
+ name: Spearman Max
174
+ - task:
175
+ type: semantic-similarity
176
+ name: Semantic Similarity
177
+ dataset:
178
+ name: sts test 128
179
+ type: sts-test-128
180
+ metrics:
181
+ - type: pearson_cosine
182
+ value: 0.6909605436368886
183
+ name: Pearson Cosine
184
+ - type: spearman_cosine
185
+ value: 0.6880114885304113
186
+ name: Spearman Cosine
187
+ - type: pearson_manhattan
188
+ value: 0.7044693468919807
189
+ name: Pearson Manhattan
190
+ - type: spearman_manhattan
191
+ value: 0.7001174190718876
192
+ name: Spearman Manhattan
193
+ - type: pearson_euclidean
194
+ value: 0.7063530897910422
195
+ name: Pearson Euclidean
196
+ - type: spearman_euclidean
197
+ value: 0.7028721535481625
198
+ name: Spearman Euclidean
199
+ - type: pearson_dot
200
+ value: 0.5846530941942547
201
+ name: Pearson Dot
202
+ - type: spearman_dot
203
+ value: 0.5728728042034709
204
+ name: Spearman Dot
205
+ - type: pearson_max
206
+ value: 0.7063530897910422
207
+ name: Pearson Max
208
+ - type: spearman_max
209
+ value: 0.7028721535481625
210
+ name: Spearman Max
211
+ - task:
212
+ type: semantic-similarity
213
+ name: Semantic Similarity
214
+ dataset:
215
+ name: sts test 64
216
+ type: sts-test-64
217
+ metrics:
218
+ - type: pearson_cosine
219
+ value: 0.680996097859508
220
+ name: Pearson Cosine
221
+ - type: spearman_cosine
222
+ value: 0.6803001320954455
223
+ name: Spearman Cosine
224
+ - type: pearson_manhattan
225
+ value: 0.7053262249895214
226
+ name: Pearson Manhattan
227
+ - type: spearman_manhattan
228
+ value: 0.6987184531053297
229
+ name: Spearman Manhattan
230
+ - type: pearson_euclidean
231
+ value: 0.7061173611755747
232
+ name: Pearson Euclidean
233
+ - type: spearman_euclidean
234
+ value: 0.7003828247494553
235
+ name: Spearman Euclidean
236
+ - type: pearson_dot
237
+ value: 0.5177214664781289
238
+ name: Pearson Dot
239
+ - type: spearman_dot
240
+ value: 0.5019887605325859
241
+ name: Spearman Dot
242
+ - type: pearson_max
243
+ value: 0.7061173611755747
244
+ name: Pearson Max
245
+ - type: spearman_max
246
+ value: 0.7003828247494553
247
+ name: Spearman Max
248
+ ---
249
+
250
+ # SentenceTransformer based on Alibaba-NLP/gte-base-en-v1.5
251
+
252
+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5). 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.
253
+
254
+ ## Model Details
255
+
256
+ ### Model Description
257
+ - **Model Type:** Sentence Transformer
258
+ - **Base model:** [Alibaba-NLP/gte-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) <!-- at revision 269b9aca14a582d83e31b8c76b2e85a266fc1d77 -->
259
+ - **Maximum Sequence Length:** 8192 tokens
260
+ - **Output Dimensionality:** 768 tokens
261
+ - **Similarity Function:** Cosine Similarity
262
+ <!-- - **Training Dataset:** Unknown -->
263
+ <!-- - **Language:** Unknown -->
264
+ <!-- - **License:** Unknown -->
265
+
266
+ ### Model Sources
267
+
268
+ - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
269
+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
270
+ - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
271
+
272
+ ### Full Model Architecture
273
+
274
+ ```
275
+ SentenceTransformer(
276
+ (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: NewModel
277
+ (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
278
+ )
279
+ ```
280
+
281
+ ## Usage
282
+
283
+ ### Direct Usage (Sentence Transformers)
284
+
285
+ First install the Sentence Transformers library:
286
+
287
+ ```bash
288
+ pip install -U sentence-transformers
289
+ ```
290
+
291
+ Then you can load this model and run inference.
292
+ ```python
293
+ from sentence_transformers import SentenceTransformer
294
+
295
+ # Download from the 🤗 Hub
296
+ model = SentenceTransformer("sartifyllc/swahili-gte-base-en-v1.5-nli-matryoshka")
297
+ # Run inference
298
+ sentences = [
299
+ 'Mwanamume aliyevalia koti la bluu la kuzuia upepo, amelala uso chini kwenye benchi ya bustani, akiwa na chupa ya pombe iliyofungwa kwenye mojawapo ya miguu ya benchi.',
300
+ 'Mwanamume amelala uso chini kwenye benchi ya bustani.',
301
+ 'Mwanamume fulani anacheza dansi kwenye klabu hiyo akifungua chupa.',
302
+ ]
303
+ embeddings = model.encode(sentences)
304
+ print(embeddings.shape)
305
+ # [3, 768]
306
+
307
+ # Get the similarity scores for the embeddings
308
+ similarities = model.similarity(embeddings, embeddings)
309
+ print(similarities.shape)
310
+ # [3, 3]
311
+ ```
312
+
313
+ <!--
314
+ ### Direct Usage (Transformers)
315
+
316
+ <details><summary>Click to see the direct usage in Transformers</summary>
317
+
318
+ </details>
319
+ -->
320
+
321
+ <!--
322
+ ### Downstream Usage (Sentence Transformers)
323
+
324
+ You can finetune this model on your own dataset.
325
+
326
+ <details><summary>Click to expand</summary>
327
+
328
+ </details>
329
+ -->
330
+
331
+ <!--
332
+ ### Out-of-Scope Use
333
+
334
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
335
+ -->
336
+
337
+ ## Evaluation
338
+
339
+ ### Metrics
340
+
341
+ #### Semantic Similarity
342
+ * Dataset: `sts-test-768`
343
+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
344
+
345
+ | Metric | Value |
346
+ |:--------------------|:-----------|
347
+ | pearson_cosine | 0.7043 |
348
+ | **spearman_cosine** | **0.6964** |
349
+ | pearson_manhattan | 0.6909 |
350
+ | spearman_manhattan | 0.6919 |
351
+ | pearson_euclidean | 0.6929 |
352
+ | spearman_euclidean | 0.6938 |
353
+ | pearson_dot | 0.7012 |
354
+ | spearman_dot | 0.6918 |
355
+ | pearson_max | 0.7043 |
356
+ | spearman_max | 0.6964 |
357
+
358
+ #### Semantic Similarity
359
+ * Dataset: `sts-test-512`
360
+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
361
+
362
+ | Metric | Value |
363
+ |:--------------------|:-----------|
364
+ | pearson_cosine | 0.7024 |
365
+ | **spearman_cosine** | **0.6961** |
366
+ | pearson_manhattan | 0.6937 |
367
+ | spearman_manhattan | 0.6943 |
368
+ | pearson_euclidean | 0.6959 |
369
+ | spearman_euclidean | 0.6965 |
370
+ | pearson_dot | 0.674 |
371
+ | spearman_dot | 0.6636 |
372
+ | pearson_max | 0.7024 |
373
+ | spearman_max | 0.6965 |
374
+
375
+ #### Semantic Similarity
376
+ * Dataset: `sts-test-256`
377
+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
378
+
379
+ | Metric | Value |
380
+ |:--------------------|:-----------|
381
+ | pearson_cosine | 0.6976 |
382
+ | **spearman_cosine** | **0.6922** |
383
+ | pearson_manhattan | 0.7013 |
384
+ | spearman_manhattan | 0.7002 |
385
+ | pearson_euclidean | 0.7033 |
386
+ | spearman_euclidean | 0.7028 |
387
+ | pearson_dot | 0.6354 |
388
+ | spearman_dot | 0.6242 |
389
+ | pearson_max | 0.7033 |
390
+ | spearman_max | 0.7028 |
391
+
392
+ #### Semantic Similarity
393
+ * Dataset: `sts-test-128`
394
+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
395
+
396
+ | Metric | Value |
397
+ |:--------------------|:----------|
398
+ | pearson_cosine | 0.691 |
399
+ | **spearman_cosine** | **0.688** |
400
+ | pearson_manhattan | 0.7045 |
401
+ | spearman_manhattan | 0.7001 |
402
+ | pearson_euclidean | 0.7064 |
403
+ | spearman_euclidean | 0.7029 |
404
+ | pearson_dot | 0.5847 |
405
+ | spearman_dot | 0.5729 |
406
+ | pearson_max | 0.7064 |
407
+ | spearman_max | 0.7029 |
408
+
409
+ #### Semantic Similarity
410
+ * Dataset: `sts-test-64`
411
+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
412
+
413
+ | Metric | Value |
414
+ |:--------------------|:-----------|
415
+ | pearson_cosine | 0.681 |
416
+ | **spearman_cosine** | **0.6803** |
417
+ | pearson_manhattan | 0.7053 |
418
+ | spearman_manhattan | 0.6987 |
419
+ | pearson_euclidean | 0.7061 |
420
+ | spearman_euclidean | 0.7004 |
421
+ | pearson_dot | 0.5177 |
422
+ | spearman_dot | 0.502 |
423
+ | pearson_max | 0.7061 |
424
+ | spearman_max | 0.7004 |
425
+
426
+ <!--
427
+ ## Bias, Risks and Limitations
428
+
429
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
430
+ -->
431
+
432
+ <!--
433
+ ### Recommendations
434
+
435
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
436
+ -->
437
+
438
+ ## Training Details
439
+
440
+ ### Training Hyperparameters
441
+ #### Non-Default Hyperparameters
442
+
443
+ - `num_train_epochs`: 1
444
+ - `warmup_ratio`: 0.1
445
+ - `fp16`: True
446
+ - `batch_sampler`: no_duplicates
447
+
448
+ #### All Hyperparameters
449
+ <details><summary>Click to expand</summary>
450
+
451
+ - `overwrite_output_dir`: False
452
+ - `do_predict`: False
453
+ - `prediction_loss_only`: True
454
+ - `per_device_train_batch_size`: 8
455
+ - `per_device_eval_batch_size`: 8
456
+ - `per_gpu_train_batch_size`: None
457
+ - `per_gpu_eval_batch_size`: None
458
+ - `gradient_accumulation_steps`: 1
459
+ - `eval_accumulation_steps`: None
460
+ - `learning_rate`: 5e-05
461
+ - `weight_decay`: 0.0
462
+ - `adam_beta1`: 0.9
463
+ - `adam_beta2`: 0.999
464
+ - `adam_epsilon`: 1e-08
465
+ - `max_grad_norm`: 1.0
466
+ - `num_train_epochs`: 1
467
+ - `max_steps`: -1
468
+ - `lr_scheduler_type`: linear
469
+ - `lr_scheduler_kwargs`: {}
470
+ - `warmup_ratio`: 0.1
471
+ - `warmup_steps`: 0
472
+ - `log_level`: passive
473
+ - `log_level_replica`: warning
474
+ - `log_on_each_node`: True
475
+ - `logging_nan_inf_filter`: True
476
+ - `save_safetensors`: True
477
+ - `save_on_each_node`: False
478
+ - `save_only_model`: False
479
+ - `no_cuda`: False
480
+ - `use_cpu`: False
481
+ - `use_mps_device`: False
482
+ - `seed`: 42
483
+ - `data_seed`: None
484
+ - `jit_mode_eval`: False
485
+ - `use_ipex`: False
486
+ - `bf16`: False
487
+ - `fp16`: True
488
+ - `fp16_opt_level`: O1
489
+ - `half_precision_backend`: auto
490
+ - `bf16_full_eval`: False
491
+ - `fp16_full_eval`: False
492
+ - `tf32`: None
493
+ - `local_rank`: 0
494
+ - `ddp_backend`: None
495
+ - `tpu_num_cores`: None
496
+ - `tpu_metrics_debug`: False
497
+ - `debug`: []
498
+ - `dataloader_drop_last`: False
499
+ - `dataloader_num_workers`: 0
500
+ - `dataloader_prefetch_factor`: None
501
+ - `past_index`: -1
502
+ - `disable_tqdm`: False
503
+ - `remove_unused_columns`: True
504
+ - `label_names`: None
505
+ - `load_best_model_at_end`: False
506
+ - `ignore_data_skip`: False
507
+ - `fsdp`: []
508
+ - `fsdp_min_num_params`: 0
509
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
510
+ - `fsdp_transformer_layer_cls_to_wrap`: None
511
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'gradient_accumulation_kwargs': None}
512
+ - `deepspeed`: None
513
+ - `label_smoothing_factor`: 0.0
514
+ - `optim`: adamw_torch
515
+ - `optim_args`: None
516
+ - `adafactor`: False
517
+ - `group_by_length`: False
518
+ - `length_column_name`: length
519
+ - `ddp_find_unused_parameters`: None
520
+ - `ddp_bucket_cap_mb`: None
521
+ - `ddp_broadcast_buffers`: False
522
+ - `dataloader_pin_memory`: True
523
+ - `dataloader_persistent_workers`: False
524
+ - `skip_memory_metrics`: True
525
+ - `use_legacy_prediction_loop`: False
526
+ - `push_to_hub`: False
527
+ - `resume_from_checkpoint`: None
528
+ - `hub_model_id`: None
529
+ - `hub_strategy`: every_save
530
+ - `hub_private_repo`: False
531
+ - `hub_always_push`: False
532
+ - `gradient_checkpointing`: False
533
+ - `gradient_checkpointing_kwargs`: None
534
+ - `include_inputs_for_metrics`: False
535
+ - `eval_do_concat_batches`: True
536
+ - `fp16_backend`: auto
537
+ - `push_to_hub_model_id`: None
538
+ - `push_to_hub_organization`: None
539
+ - `mp_parameters`:
540
+ - `auto_find_batch_size`: False
541
+ - `full_determinism`: False
542
+ - `torchdynamo`: None
543
+ - `ray_scope`: last
544
+ - `ddp_timeout`: 1800
545
+ - `torch_compile`: False
546
+ - `torch_compile_backend`: None
547
+ - `torch_compile_mode`: None
548
+ - `dispatch_batches`: None
549
+ - `split_batches`: None
550
+ - `include_tokens_per_second`: False
551
+ - `include_num_input_tokens_seen`: False
552
+ - `neftune_noise_alpha`: None
553
+ - `optim_target_modules`: None
554
+ - `batch_sampler`: no_duplicates
555
+ - `multi_dataset_batch_sampler`: proportional
556
+
557
+ </details>
558
+
559
+ ### Training Logs
560
+ <details><summary>Click to expand</summary>
561
+
562
+ | Epoch | Step | Training Loss | sts-test-128_spearman_cosine | sts-test-256_spearman_cosine | sts-test-512_spearman_cosine | sts-test-64_spearman_cosine | sts-test-768_spearman_cosine |
563
+ |:------:|:-----:|:-------------:|:----------------------------:|:----------------------------:|:----------------------------:|:---------------------------:|:----------------------------:|
564
+ | 0.0029 | 100 | 13.2716 | - | - | - | - | - |
565
+ | 0.0057 | 200 | 9.83 | - | - | - | - | - |
566
+ | 0.0086 | 300 | 9.9047 | - | - | - | - | - |
567
+ | 0.0115 | 400 | 7.5137 | - | - | - | - | - |
568
+ | 0.0143 | 500 | 7.6419 | - | - | - | - | - |
569
+ | 0.0172 | 600 | 6.9603 | - | - | - | - | - |
570
+ | 0.0201 | 700 | 7.3009 | - | - | - | - | - |
571
+ | 0.0229 | 800 | 7.1397 | - | - | - | - | - |
572
+ | 0.0258 | 900 | 8.1352 | - | - | - | - | - |
573
+ | 0.0287 | 1000 | 7.5945 | - | - | - | - | - |
574
+ | 0.0315 | 1100 | 7.0476 | - | - | - | - | - |
575
+ | 0.0344 | 1200 | 5.3356 | - | - | - | - | - |
576
+ | 0.0373 | 1300 | 5.1529 | - | - | - | - | - |
577
+ | 0.0402 | 1400 | 4.9726 | - | - | - | - | - |
578
+ | 0.0430 | 1500 | 5.1683 | - | - | - | - | - |
579
+ | 0.0459 | 1600 | 4.7945 | - | - | - | - | - |
580
+ | 0.0488 | 1700 | 4.9624 | - | - | - | - | - |
581
+ | 0.0516 | 1800 | 4.4254 | - | - | - | - | - |
582
+ | 0.0545 | 1900 | 4.4379 | - | - | - | - | - |
583
+ | 0.0574 | 2000 | 4.0327 | - | - | - | - | - |
584
+ | 0.0602 | 2100 | 3.5138 | - | - | - | - | - |
585
+ | 0.0631 | 2200 | 4.5055 | - | - | - | - | - |
586
+ | 0.0660 | 2300 | 3.8966 | - | - | - | - | - |
587
+ | 0.0688 | 2400 | 4.4884 | - | - | - | - | - |
588
+ | 0.0717 | 2500 | 3.5825 | - | - | - | - | - |
589
+ | 0.0746 | 2600 | 4.0155 | - | - | - | - | - |
590
+ | 0.0774 | 2700 | 4.9842 | - | - | - | - | - |
591
+ | 0.0803 | 2800 | 4.7732 | - | - | - | - | - |
592
+ | 0.0832 | 2900 | 4.5095 | - | - | - | - | - |
593
+ | 0.0860 | 3000 | 4.2526 | - | - | - | - | - |
594
+ | 0.0889 | 3100 | 4.033 | - | - | - | - | - |
595
+ | 0.0918 | 3200 | 4.0052 | - | - | - | - | - |
596
+ | 0.0946 | 3300 | 3.197 | - | - | - | - | - |
597
+ | 0.0975 | 3400 | 3.3423 | - | - | - | - | - |
598
+ | 0.1004 | 3500 | 2.9528 | - | - | - | - | - |
599
+ | 0.1033 | 3600 | 3.9315 | - | - | - | - | - |
600
+ | 0.1061 | 3700 | 3.7733 | - | - | - | - | - |
601
+ | 0.1090 | 3800 | 3.5153 | - | - | - | - | - |
602
+ | 0.1119 | 3900 | 4.1326 | - | - | - | - | - |
603
+ | 0.1147 | 4000 | 5.2179 | - | - | - | - | - |
604
+ | 0.1176 | 4100 | 6.4314 | - | - | - | - | - |
605
+ | 0.1205 | 4200 | 6.3485 | - | - | - | - | - |
606
+ | 0.1233 | 4300 | 4.7771 | - | - | - | - | - |
607
+ | 0.1262 | 4400 | 4.9055 | - | - | - | - | - |
608
+ | 0.1291 | 4500 | 3.9025 | - | - | - | - | - |
609
+ | 0.1319 | 4600 | 4.4638 | - | - | - | - | - |
610
+ | 0.1348 | 4700 | 5.0049 | - | - | - | - | - |
611
+ | 0.1377 | 4800 | 4.3124 | - | - | - | - | - |
612
+ | 0.1405 | 4900 | 4.0027 | - | - | - | - | - |
613
+ | 0.1434 | 5000 | 4.3173 | - | - | - | - | - |
614
+ | 0.1463 | 5100 | 3.6629 | - | - | - | - | - |
615
+ | 0.1491 | 5200 | 4.2759 | - | - | - | - | - |
616
+ | 0.1520 | 5300 | 3.4621 | - | - | - | - | - |
617
+ | 0.1549 | 5400 | 3.9251 | - | - | - | - | - |
618
+ | 0.1577 | 5500 | 4.2294 | - | - | - | - | - |
619
+ | 0.1606 | 5600 | 3.6244 | - | - | - | - | - |
620
+ | 0.1635 | 5700 | 4.283 | - | - | - | - | - |
621
+ | 0.1664 | 5800 | 4.4665 | - | - | - | - | - |
622
+ | 0.1692 | 5900 | 4.956 | - | - | - | - | - |
623
+ | 0.1721 | 6000 | 4.795 | - | - | - | - | - |
624
+ | 0.1750 | 6100 | 4.998 | - | - | - | - | - |
625
+ | 0.1778 | 6200 | 5.3316 | - | - | - | - | - |
626
+ | 0.1807 | 6300 | 5.2247 | - | - | - | - | - |
627
+ | 0.1836 | 6400 | 4.6554 | - | - | - | - | - |
628
+ | 0.1864 | 6500 | 5.2474 | - | - | - | - | - |
629
+ | 0.1893 | 6600 | 5.1168 | - | - | - | - | - |
630
+ | 0.1922 | 6700 | 5.1372 | - | - | - | - | - |
631
+ | 0.1950 | 6800 | 4.1564 | - | - | - | - | - |
632
+ | 0.1979 | 6900 | 4.6997 | - | - | - | - | - |
633
+ | 0.2008 | 7000 | 4.1854 | - | - | - | - | - |
634
+ | 0.2036 | 7100 | 4.4574 | - | - | - | - | - |
635
+ | 0.2065 | 7200 | 4.1859 | - | - | - | - | - |
636
+ | 0.2094 | 7300 | 4.8306 | - | - | - | - | - |
637
+ | 0.2122 | 7400 | 4.4487 | - | - | - | - | - |
638
+ | 0.2151 | 7500 | 4.4606 | - | - | - | - | - |
639
+ | 0.2180 | 7600 | 4.4222 | - | - | - | - | - |
640
+ | 0.2208 | 7700 | 4.7836 | - | - | - | - | - |
641
+ | 0.2237 | 7800 | 4.1475 | - | - | - | - | - |
642
+ | 0.2266 | 7900 | 5.1679 | - | - | - | - | - |
643
+ | 0.2294 | 8000 | 5.0106 | - | - | - | - | - |
644
+ | 0.2323 | 8100 | 4.1899 | - | - | - | - | - |
645
+ | 0.2352 | 8200 | 4.9873 | - | - | - | - | - |
646
+ | 0.2381 | 8300 | 4.3656 | - | - | - | - | - |
647
+ | 0.2409 | 8400 | 4.6117 | - | - | - | - | - |
648
+ | 0.2438 | 8500 | 4.1785 | - | - | - | - | - |
649
+ | 0.2467 | 8600 | 3.7809 | - | - | - | - | - |
650
+ | 0.2495 | 8700 | 4.9116 | - | - | - | - | - |
651
+ | 0.2524 | 8800 | 4.553 | - | - | - | - | - |
652
+ | 0.2553 | 8900 | 4.3178 | - | - | - | - | - |
653
+ | 0.2581 | 9000 | 5.6111 | - | - | - | - | - |
654
+ | 0.2610 | 9100 | 5.4219 | - | - | - | - | - |
655
+ | 0.2639 | 9200 | 5.5628 | - | - | - | - | - |
656
+ | 0.2667 | 9300 | 4.4221 | - | - | - | - | - |
657
+ | 0.2696 | 9400 | 4.7988 | - | - | - | - | - |
658
+ | 0.2725 | 9500 | 4.9361 | - | - | - | - | - |
659
+ | 0.2753 | 9600 | 4.7225 | - | - | - | - | - |
660
+ | 0.2782 | 9700 | 4.7258 | - | - | - | - | - |
661
+ | 0.2811 | 9800 | 4.7071 | - | - | - | - | - |
662
+ | 0.2839 | 9900 | 4.5519 | - | - | - | - | - |
663
+ | 0.2868 | 10000 | 4.5354 | - | - | - | - | - |
664
+ | 0.2897 | 10100 | 4.3893 | - | - | - | - | - |
665
+ | 0.2925 | 10200 | 4.7848 | - | - | - | - | - |
666
+ | 0.2954 | 10300 | 4.7195 | - | - | - | - | - |
667
+ | 0.2983 | 10400 | 4.0155 | - | - | - | - | - |
668
+ | 0.3012 | 10500 | 5.1602 | - | - | - | - | - |
669
+ | 0.3040 | 10600 | 4.6345 | - | - | - | - | - |
670
+ | 0.3069 | 10700 | 5.39 | - | - | - | - | - |
671
+ | 0.3098 | 10800 | 4.7974 | - | - | - | - | - |
672
+ | 0.3126 | 10900 | 4.9736 | - | - | - | - | - |
673
+ | 0.3155 | 11000 | 5.0949 | - | - | - | - | - |
674
+ | 0.3184 | 11100 | 4.6704 | - | - | - | - | - |
675
+ | 0.3212 | 11200 | 4.7001 | - | - | - | - | - |
676
+ | 0.3241 | 11300 | 4.2913 | - | - | - | - | - |
677
+ | 0.3270 | 11400 | 4.7536 | - | - | - | - | - |
678
+ | 0.3298 | 11500 | 4.8349 | - | - | - | - | - |
679
+ | 0.3327 | 11600 | 4.2567 | - | - | - | - | - |
680
+ | 0.3356 | 11700 | 4.6754 | - | - | - | - | - |
681
+ | 0.3384 | 11800 | 4.8534 | - | - | - | - | - |
682
+ | 0.3413 | 11900 | 4.7486 | - | - | - | - | - |
683
+ | 0.3442 | 12000 | 4.9194 | - | - | - | - | - |
684
+ | 0.3470 | 12100 | 4.4572 | - | - | - | - | - |
685
+ | 0.3499 | 12200 | 4.6173 | - | - | - | - | - |
686
+ | 0.3528 | 12300 | 5.1292 | - | - | - | - | - |
687
+ | 0.3556 | 12400 | 4.6138 | - | - | - | - | - |
688
+ | 0.3585 | 12500 | 4.6884 | - | - | - | - | - |
689
+ | 0.3614 | 12600 | 4.4245 | - | - | - | - | - |
690
+ | 0.3643 | 12700 | 4.7534 | - | - | - | - | - |
691
+ | 0.3671 | 12800 | 4.7027 | - | - | - | - | - |
692
+ | 0.3700 | 12900 | 4.5186 | - | - | - | - | - |
693
+ | 0.3729 | 13000 | 3.8917 | - | - | - | - | - |
694
+ | 0.3757 | 13100 | 4.507 | - | - | - | - | - |
695
+ | 0.3786 | 13200 | 5.4866 | - | - | - | - | - |
696
+ | 0.3815 | 13300 | 4.0424 | - | - | - | - | - |
697
+ | 0.3843 | 13400 | 4.4017 | - | - | - | - | - |
698
+ | 0.3872 | 13500 | 4.0016 | - | - | - | - | - |
699
+ | 0.3901 | 13600 | 4.0695 | - | - | - | - | - |
700
+ | 0.3929 | 13700 | 4.4957 | - | - | - | - | - |
701
+ | 0.3958 | 13800 | 4.4655 | - | - | - | - | - |
702
+ | 0.3987 | 13900 | 4.5717 | - | - | - | - | - |
703
+ | 0.4015 | 14000 | 4.134 | - | - | - | - | - |
704
+ | 0.4044 | 14100 | 4.2704 | - | - | - | - | - |
705
+ | 0.4073 | 14200 | 4.7712 | - | - | - | - | - |
706
+ | 0.4101 | 14300 | 4.3946 | - | - | - | - | - |
707
+ | 0.4130 | 14400 | 4.5848 | - | - | - | - | - |
708
+ | 0.4159 | 14500 | 4.4655 | - | - | - | - | - |
709
+ | 0.4187 | 14600 | 4.278 | - | - | - | - | - |
710
+ | 0.4216 | 14700 | 4.2877 | - | - | - | - | - |
711
+ | 0.4245 | 14800 | 3.9299 | - | - | - | - | - |
712
+ | 0.4274 | 14900 | 4.7078 | - | - | - | - | - |
713
+ | 0.4302 | 15000 | 4.8527 | - | - | - | - | - |
714
+ | 0.4331 | 15100 | 4.3476 | - | - | - | - | - |
715
+ | 0.4360 | 15200 | 4.2012 | - | - | - | - | - |
716
+ | 0.4388 | 15300 | 4.1766 | - | - | - | - | - |
717
+ | 0.4417 | 15400 | 3.9842 | - | - | - | - | - |
718
+ | 0.4446 | 15500 | 4.1244 | - | - | - | - | - |
719
+ | 0.4474 | 15600 | 4.7983 | - | - | - | - | - |
720
+ | 0.4503 | 15700 | 4.2341 | - | - | - | - | - |
721
+ | 0.4532 | 15800 | 4.9829 | - | - | - | - | - |
722
+ | 0.4560 | 15900 | 4.0221 | - | - | - | - | - |
723
+ | 0.4589 | 16000 | 4.1082 | - | - | - | - | - |
724
+ | 0.4618 | 16100 | 3.8922 | - | - | - | - | - |
725
+ | 0.4646 | 16200 | 4.5382 | - | - | - | - | - |
726
+ | 0.4675 | 16300 | 4.4428 | - | - | - | - | - |
727
+ | 0.4704 | 16400 | 3.9087 | - | - | - | - | - |
728
+ | 0.4732 | 16500 | 3.7465 | - | - | - | - | - |
729
+ | 0.4761 | 16600 | 4.149 | - | - | - | - | - |
730
+ | 0.4790 | 16700 | 4.5691 | - | - | - | - | - |
731
+ | 0.4818 | 16800 | 3.8776 | - | - | - | - | - |
732
+ | 0.4847 | 16900 | 3.7354 | - | - | - | - | - |
733
+ | 0.4876 | 17000 | 4.25 | - | - | - | - | - |
734
+ | 0.4904 | 17100 | 4.4119 | - | - | - | - | - |
735
+ | 0.4933 | 17200 | 4.2319 | - | - | - | - | - |
736
+ | 0.4962 | 17300 | 4.3736 | - | - | - | - | - |
737
+ | 0.4991 | 17400 | 4.5345 | - | - | - | - | - |
738
+ | 0.5019 | 17500 | 4.1824 | - | - | - | - | - |
739
+ | 0.5048 | 17600 | 4.0033 | - | - | - | - | - |
740
+ | 0.5077 | 17700 | 4.277 | - | - | - | - | - |
741
+ | 0.5105 | 17800 | 4.3553 | - | - | - | - | - |
742
+ | 0.5134 | 17900 | 3.9528 | - | - | - | - | - |
743
+ | 0.5163 | 18000 | 4.068 | - | - | - | - | - |
744
+ | 0.5191 | 18100 | 4.0464 | - | - | - | - | - |
745
+ | 0.5220 | 18200 | 4.1665 | - | - | - | - | - |
746
+ | 0.5249 | 18300 | 3.7445 | - | - | - | - | - |
747
+ | 0.5277 | 18400 | 4.2248 | - | - | - | - | - |
748
+ | 0.5306 | 18500 | 3.9295 | - | - | - | - | - |
749
+ | 0.5335 | 18600 | 3.546 | - | - | - | - | - |
750
+ | 0.5363 | 18700 | 3.7463 | - | - | - | - | - |
751
+ | 0.5392 | 18800 | 3.9798 | - | - | - | - | - |
752
+ | 0.5421 | 18900 | 4.4773 | - | - | - | - | - |
753
+ | 0.5449 | 19000 | 4.3534 | - | - | - | - | - |
754
+ | 0.5478 | 19100 | 4.2347 | - | - | - | - | - |
755
+ | 0.5507 | 19200 | 3.8113 | - | - | - | - | - |
756
+ | 0.5535 | 19300 | 4.4689 | - | - | - | - | - |
757
+ | 0.5564 | 19400 | 4.2188 | - | - | - | - | - |
758
+ | 0.5593 | 19500 | 4.1266 | - | - | - | - | - |
759
+ | 0.5622 | 19600 | 3.9222 | - | - | - | - | - |
760
+ | 0.5650 | 19700 | 4.38 | - | - | - | - | - |
761
+ | 0.5679 | 19800 | 4.4557 | - | - | - | - | - |
762
+ | 0.5708 | 19900 | 4.7566 | - | - | - | - | - |
763
+ | 0.5736 | 20000 | 3.8922 | - | - | - | - | - |
764
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815
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817
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818
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912
+ | 1.0 | 34866 | - | 0.6880 | 0.6922 | 0.6961 | 0.6803 | 0.6964 |
913
+
914
+ </details>
915
+
916
+ ### Framework Versions
917
+ - Python: 3.11.9
918
+ - Sentence Transformers: 3.0.1
919
+ - Transformers: 4.40.1
920
+ - PyTorch: 2.3.0+cu121
921
+ - Accelerate: 0.29.3
922
+ - Datasets: 2.19.0
923
+ - Tokenizers: 0.19.1
924
+
925
+ ## Citation
926
+
927
+ ### BibTeX
928
+
929
+ #### Sentence Transformers
930
+ ```bibtex
931
+ @inproceedings{reimers-2019-sentence-bert,
932
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
933
+ author = "Reimers, Nils and Gurevych, Iryna",
934
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
935
+ month = "11",
936
+ year = "2019",
937
+ publisher = "Association for Computational Linguistics",
938
+ url = "https://arxiv.org/abs/1908.10084",
939
+ }
940
+ ```
941
+
942
+ #### MatryoshkaLoss
943
+ ```bibtex
944
+ @misc{kusupati2024matryoshka,
945
+ title={Matryoshka Representation Learning},
946
+ author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
947
+ year={2024},
948
+ eprint={2205.13147},
949
+ archivePrefix={arXiv},
950
+ primaryClass={cs.LG}
951
+ }
952
+ ```
953
+
954
+ #### MultipleNegativesRankingLoss
955
+ ```bibtex
956
+ @misc{henderson2017efficient,
957
+ title={Efficient Natural Language Response Suggestion for Smart Reply},
958
+ author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
959
+ year={2017},
960
+ eprint={1705.00652},
961
+ archivePrefix={arXiv},
962
+ primaryClass={cs.CL}
963
+ }
964
+ ```
965
+
966
+ <!--
967
+ ## Glossary
968
+
969
+ *Clearly define terms in order to be accessible across audiences.*
970
+ -->
971
+
972
+ <!--
973
+ ## Model Card Authors
974
+
975
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
976
+ -->
977
+
978
+ <!--
979
+ ## Model Card Contact
980
+
981
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
982
+ -->
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+ "tokenizer_class": "BertTokenizer",
59
+ "truncation_side": "right",
60
+ "truncation_strategy": "longest_first",
61
+ "unk_token": "[UNK]"
62
+ }
vocab.txt ADDED
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