Hugging Face's logo

sartifyllc
/
swahili-paraphrase-multilingual-mpnet-base-v2-nli-matryoshka

Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:557850
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results
text-embeddings-inference
Inference Endpoints
Model card Files Community
Mollel commited on
Commit
4cfa0b5
•
1 Parent(s): 5c97b74

Add new SentenceTransformer model.

Browse files
Files changed (12) hide show
  1. .gitattributes +1 -0
  2. 1_Pooling/config.json +10 -0
  3. README.md +810 -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
.gitattributes CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
33
  *.zip filter=lfs diff=lfs merge=lfs -text
34
  *.zst filter=lfs diff=lfs merge=lfs -text
35
  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
33
  *.zip filter=lfs diff=lfs merge=lfs -text
34
  *.zst filter=lfs diff=lfs merge=lfs -text
35
  *tfevents* filter=lfs diff=lfs merge=lfs -text
36
+ tokenizer.json filter=lfs diff=lfs merge=lfs -text
1_Pooling/config.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "word_embedding_dimension": 768,
3
+ "pooling_mode_cls_token": false,
4
+ "pooling_mode_mean_tokens": true,
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,
9
+ "include_prompt": true
10
+ }
README.md ADDED
@@ -0,0 +1,810 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ language: []
3
+ library_name: sentence-transformers
4
+ tags:
5
+ - 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: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
13
+ datasets: []
14
+ metrics:
15
+ - pearson_cosine
16
+ - spearman_cosine
17
+ - pearson_manhattan
18
+ - spearman_manhattan
19
+ - pearson_euclidean
20
+ - spearman_euclidean
21
+ - pearson_dot
22
+ - spearman_dot
23
+ - pearson_max
24
+ - spearman_max
25
+ widget:
26
+ - source_sentence: Mwanamume aliyepangwa vizuri anasimama kwa mguu mmoja karibu na
27
+ pwani safi ya bahari.
28
+ sentences:
29
+ - mtu anacheka wakati wa kufua nguo
30
+ - Mwanamume fulani yuko nje karibu na ufuo wa bahari.
31
+ - Mwanamume fulani ameketi kwenye sofa yake.
32
+ - source_sentence: Mwanamume mwenye ngozi nyeusi akivuta sigareti karibu na chombo
33
+ cha taka cha kijani.
34
+ sentences:
35
+ - Karibu na chombo cha taka mwanamume huyo alisimama na kuvuta sigareti
36
+ - Kitanda ni chafu.
37
+ - Alipokuwa kwenye dimbwi la kuogelea mvulana huyo mwenye ugonjwa wa albino alijihadhari
38
+ na jua kupita kiasi
39
+ - source_sentence: Mwanamume kijana mwenye nywele nyekundu anaketi ukutani akisoma
40
+ gazeti huku mwanamke na msichana mchanga wakipita.
41
+ sentences:
42
+ - Mwanamume aliyevalia shati la bluu amegonga ukuta kando ya barabara na gari la
43
+ bluu na gari nyekundu lenye maji nyuma.
44
+ - 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.
50
+ - 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
54
+ ya miguu ya benchi.
55
+ sentences:
56
+ - Mwanamume amelala uso chini kwenye benchi ya bustani.
57
+ - Mwanamke anaunganisha uzi katika mipira kando ya rundo la mipira
58
+ - Mwanamume fulani anacheza dansi kwenye klabu hiyo akifungua chupa.
59
+ pipeline_tag: sentence-similarity
60
+ model-index:
61
+ - name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2
62
+ results:
63
+ - task:
64
+ type: semantic-similarity
65
+ name: Semantic Similarity
66
+ dataset:
67
+ name: sts test 768
68
+ type: sts-test-768
69
+ metrics:
70
+ - type: pearson_cosine
71
+ value: 0.7073072916945755
72
+ name: Pearson Cosine
73
+ - type: spearman_cosine
74
+ value: 0.7038302407401268
75
+ name: Spearman Cosine
76
+ - type: pearson_manhattan
77
+ value: 0.6971720016143421
78
+ name: Pearson Manhattan
79
+ - type: spearman_manhattan
80
+ value: 0.6937589389074208
81
+ name: Spearman Manhattan
82
+ - type: pearson_euclidean
83
+ value: 0.6995677027369716
84
+ name: Pearson Euclidean
85
+ - type: spearman_euclidean
86
+ value: 0.6964739266023146
87
+ name: Spearman Euclidean
88
+ - type: pearson_dot
89
+ value: 0.610950397941133
90
+ name: Pearson Dot
91
+ - type: spearman_dot
92
+ value: 0.593970670155461
93
+ name: Spearman Dot
94
+ - type: pearson_max
95
+ value: 0.7073072916945755
96
+ name: Pearson Max
97
+ - type: spearman_max
98
+ value: 0.7038302407401268
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: 0.7045540268598433
109
+ name: Pearson Cosine
110
+ - type: spearman_cosine
111
+ value: 0.7023621139637947
112
+ name: Spearman Cosine
113
+ - type: pearson_manhattan
114
+ value: 0.6975397258794529
115
+ name: Pearson Manhattan
116
+ - type: spearman_manhattan
117
+ value: 0.6927560109749419
118
+ name: Spearman Manhattan
119
+ - type: pearson_euclidean
120
+ value: 0.6985549032977664
121
+ name: Pearson Euclidean
122
+ - type: spearman_euclidean
123
+ value: 0.6941789537125014
124
+ name: Spearman Euclidean
125
+ - type: pearson_dot
126
+ value: 0.582046017636523
127
+ name: Pearson Dot
128
+ - type: spearman_dot
129
+ value: 0.565355081915806
130
+ name: Spearman Dot
131
+ - type: pearson_max
132
+ value: 0.7045540268598433
133
+ name: Pearson Max
134
+ - type: spearman_max
135
+ value: 0.7023621139637947
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: 0.7012922000956245
146
+ name: Pearson Cosine
147
+ - type: spearman_cosine
148
+ value: 0.7016107934280537
149
+ name: Spearman Cosine
150
+ - type: pearson_manhattan
151
+ value: 0.69508092429561
152
+ name: Pearson Manhattan
153
+ - type: spearman_manhattan
154
+ value: 0.6879849400335534
155
+ name: Spearman Manhattan
156
+ - type: pearson_euclidean
157
+ value: 0.6956451936598814
158
+ name: Pearson Euclidean
159
+ - type: spearman_euclidean
160
+ value: 0.6890266593479353
161
+ name: Spearman Euclidean
162
+ - type: pearson_dot
163
+ value: 0.5501625376986127
164
+ name: Pearson Dot
165
+ - type: spearman_dot
166
+ value: 0.5332005894675337
167
+ name: Spearman Dot
168
+ - type: pearson_max
169
+ value: 0.7012922000956245
170
+ name: Pearson Max
171
+ - type: spearman_max
172
+ value: 0.7016107934280537
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: 0.6980581280594836
183
+ name: Pearson Cosine
184
+ - type: spearman_cosine
185
+ value: 0.7000311940508227
186
+ name: Spearman Cosine
187
+ - type: pearson_manhattan
188
+ value: 0.6910651227829323
189
+ name: Pearson Manhattan
190
+ - type: spearman_manhattan
191
+ value: 0.6823572623875095
192
+ name: Spearman Manhattan
193
+ - type: pearson_euclidean
194
+ value: 0.6922658508243149
195
+ name: Pearson Euclidean
196
+ - type: spearman_euclidean
197
+ value: 0.6838439746630024
198
+ name: Spearman Euclidean
199
+ - type: pearson_dot
200
+ value: 0.5156063038618797
201
+ name: Pearson Dot
202
+ - type: spearman_dot
203
+ value: 0.5006742054178095
204
+ name: Spearman Dot
205
+ - type: pearson_max
206
+ value: 0.6980581280594836
207
+ name: Pearson Max
208
+ - type: spearman_max
209
+ value: 0.7000311940508227
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: 0.6865218902262467
220
+ name: Pearson Cosine
221
+ - type: spearman_cosine
222
+ value: 0.6901005120722546
223
+ name: Spearman Cosine
224
+ - type: pearson_manhattan
225
+ value: 0.681113101036276
226
+ name: Pearson Manhattan
227
+ - type: spearman_manhattan
228
+ value: 0.6713556700583071
229
+ name: Spearman Manhattan
230
+ - type: pearson_euclidean
231
+ value: 0.6816926483485075
232
+ name: Pearson Euclidean
233
+ - type: spearman_euclidean
234
+ value: 0.6701163777153826
235
+ name: Spearman Euclidean
236
+ - type: pearson_dot
237
+ value: 0.4686654293362901
238
+ name: Pearson Dot
239
+ - type: spearman_dot
240
+ value: 0.4520420017929889
241
+ name: Spearman Dot
242
+ - type: pearson_max
243
+ value: 0.6865218902262467
244
+ name: Pearson Max
245
+ - type: spearman_max
246
+ value: 0.6901005120722546
247
+ name: Spearman Max
248
+ ---
249
+
250
+ # SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2
251
+
252
+ 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.
253
+
254
+ ## Model Details
255
+
256
+ ### Model Description
257
+ - **Model Type:** Sentence Transformer
258
+ - **Base model:** [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) <!-- at revision 79f2382ceacceacdf38563d7c5d16b9ff8d725d6 -->
259
+ - **Maximum Sequence Length:** 128 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': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
277
+ (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})
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-paraphrase-multilingual-mpnet-base-v2-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 | 0.7073 |
348
+ | **spearman_cosine** | **0.7038** |
349
+ | pearson_manhattan | 0.6972 |
350
+ | spearman_manhattan | 0.6938 |
351
+ | pearson_euclidean | 0.6996 |
352
+ | spearman_euclidean | 0.6965 |
353
+ | pearson_dot | 0.611 |
354
+ | spearman_dot | 0.594 |
355
+ | pearson_max | 0.7073 |
356
+ | spearman_max | 0.7038 |
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 | 0.7046 |
365
+ | **spearman_cosine** | **0.7024** |
366
+ | pearson_manhattan | 0.6975 |
367
+ | spearman_manhattan | 0.6928 |
368
+ | pearson_euclidean | 0.6986 |
369
+ | spearman_euclidean | 0.6942 |
370
+ | pearson_dot | 0.582 |
371
+ | spearman_dot | 0.5654 |
372
+ | pearson_max | 0.7046 |
373
+ | spearman_max | 0.7024 |
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 | 0.7013 |
382
+ | **spearman_cosine** | **0.7016** |
383
+ | pearson_manhattan | 0.6951 |
384
+ | spearman_manhattan | 0.688 |
385
+ | pearson_euclidean | 0.6956 |
386
+ | spearman_euclidean | 0.689 |
387
+ | pearson_dot | 0.5502 |
388
+ | spearman_dot | 0.5332 |
389
+ | pearson_max | 0.7013 |
390
+ | spearman_max | 0.7016 |
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 | 0.6981 |
399
+ | **spearman_cosine** | **0.7** |
400
+ | pearson_manhattan | 0.6911 |
401
+ | spearman_manhattan | 0.6824 |
402
+ | pearson_euclidean | 0.6923 |
403
+ | spearman_euclidean | 0.6838 |
404
+ | pearson_dot | 0.5156 |
405
+ | spearman_dot | 0.5007 |
406
+ | pearson_max | 0.6981 |
407
+ | spearman_max | 0.7 |
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 | 0.6865 |
416
+ | **spearman_cosine** | **0.6901** |
417
+ | pearson_manhattan | 0.6811 |
418
+ | spearman_manhattan | 0.6714 |
419
+ | pearson_euclidean | 0.6817 |
420
+ | spearman_euclidean | 0.6701 |
421
+ | pearson_dot | 0.4687 |
422
+ | spearman_dot | 0.452 |
423
+ | pearson_max | 0.6865 |
424
+ | spearman_max | 0.6901 |
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
+ - `per_device_train_batch_size`: 16
444
+ - `per_device_eval_batch_size`: 16
445
+ - `num_train_epochs`: 1
446
+ - `warmup_ratio`: 0.1
447
+ - `fp16`: True
448
+ - `batch_sampler`: no_duplicates
449
+
450
+ #### All Hyperparameters
451
+ <details><summary>Click to expand</summary>
452
+
453
+ - `overwrite_output_dir`: False
454
+ - `do_predict`: False
455
+ - `prediction_loss_only`: True
456
+ - `per_device_train_batch_size`: 16
457
+ - `per_device_eval_batch_size`: 16
458
+ - `per_gpu_train_batch_size`: None
459
+ - `per_gpu_eval_batch_size`: None
460
+ - `gradient_accumulation_steps`: 1
461
+ - `eval_accumulation_steps`: None
462
+ - `learning_rate`: 5e-05
463
+ - `weight_decay`: 0.0
464
+ - `adam_beta1`: 0.9
465
+ - `adam_beta2`: 0.999
466
+ - `adam_epsilon`: 1e-08
467
+ - `max_grad_norm`: 1.0
468
+ - `num_train_epochs`: 1
469
+ - `max_steps`: -1
470
+ - `lr_scheduler_type`: linear
471
+ - `lr_scheduler_kwargs`: {}
472
+ - `warmup_ratio`: 0.1
473
+ - `warmup_steps`: 0
474
+ - `log_level`: passive
475
+ - `log_level_replica`: warning
476
+ - `log_on_each_node`: True
477
+ - `logging_nan_inf_filter`: True
478
+ - `save_safetensors`: True
479
+ - `save_on_each_node`: False
480
+ - `save_only_model`: False
481
+ - `no_cuda`: False
482
+ - `use_cpu`: False
483
+ - `use_mps_device`: False
484
+ - `seed`: 42
485
+ - `data_seed`: None
486
+ - `jit_mode_eval`: False
487
+ - `use_ipex`: False
488
+ - `bf16`: False
489
+ - `fp16`: True
490
+ - `fp16_opt_level`: O1
491
+ - `half_precision_backend`: auto
492
+ - `bf16_full_eval`: False
493
+ - `fp16_full_eval`: False
494
+ - `tf32`: None
495
+ - `local_rank`: 0
496
+ - `ddp_backend`: None
497
+ - `tpu_num_cores`: None
498
+ - `tpu_metrics_debug`: False
499
+ - `debug`: []
500
+ - `dataloader_drop_last`: False
501
+ - `dataloader_num_workers`: 0
502
+ - `dataloader_prefetch_factor`: None
503
+ - `past_index`: -1
504
+ - `disable_tqdm`: False
505
+ - `remove_unused_columns`: True
506
+ - `label_names`: None
507
+ - `load_best_model_at_end`: False
508
+ - `ignore_data_skip`: False
509
+ - `fsdp`: []
510
+ - `fsdp_min_num_params`: 0
511
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
512
+ - `fsdp_transformer_layer_cls_to_wrap`: None
513
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'gradient_accumulation_kwargs': None}
514
+ - `deepspeed`: None
515
+ - `label_smoothing_factor`: 0.0
516
+ - `optim`: adamw_torch
517
+ - `optim_args`: None
518
+ - `adafactor`: False
519
+ - `group_by_length`: False
520
+ - `length_column_name`: length
521
+ - `ddp_find_unused_parameters`: None
522
+ - `ddp_bucket_cap_mb`: None
523
+ - `ddp_broadcast_buffers`: False
524
+ - `dataloader_pin_memory`: True
525
+ - `dataloader_persistent_workers`: False
526
+ - `skip_memory_metrics`: True
527
+ - `use_legacy_prediction_loop`: False
528
+ - `push_to_hub`: False
529
+ - `resume_from_checkpoint`: None
530
+ - `hub_model_id`: None
531
+ - `hub_strategy`: every_save
532
+ - `hub_private_repo`: False
533
+ - `hub_always_push`: False
534
+ - `gradient_checkpointing`: False
535
+ - `gradient_checkpointing_kwargs`: None
536
+ - `include_inputs_for_metrics`: False
537
+ - `eval_do_concat_batches`: True
538
+ - `fp16_backend`: auto
539
+ - `push_to_hub_model_id`: None
540
+ - `push_to_hub_organization`: None
541
+ - `mp_parameters`:
542
+ - `auto_find_batch_size`: False
543
+ - `full_determinism`: False
544
+ - `torchdynamo`: None
545
+ - `ray_scope`: last
546
+ - `ddp_timeout`: 1800
547
+ - `torch_compile`: False
548
+ - `torch_compile_backend`: None
549
+ - `torch_compile_mode`: None
550
+ - `dispatch_batches`: None
551
+ - `split_batches`: None
552
+ - `include_tokens_per_second`: False
553
+ - `include_num_input_tokens_seen`: False
554
+ - `neftune_noise_alpha`: None
555
+ - `optim_target_modules`: None
556
+ - `batch_sampler`: no_duplicates
557
+ - `multi_dataset_batch_sampler`: proportional
558
+
559
+ </details>
560
+
561
+ ### Training Logs
562
+ <details><summary>Click to expand</summary>
563
+
564
+ | 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 |
565
+ |:------:|:-----:|:-------------:|:----------------------------:|:----------------------------:|:----------------------------:|:---------------------------:|:----------------------------:|
566
+ | 0.0057 | 100 | 18.7677 | - | - | - | - | - |
567
+ | 0.0115 | 200 | 10.5065 | - | - | - | - | - |
568
+ | 0.0172 | 300 | 8.0917 | - | - | - | - | - |
569
+ | 0.0229 | 400 | 8.4617 | - | - | - | - | - |
570
+ | 0.0287 | 500 | 8.0789 | - | - | - | - | - |
571
+ | 0.0344 | 600 | 7.3287 | - | - | - | - | - |
572
+ | 0.0402 | 700 | 6.3282 | - | - | - | - | - |
573
+ | 0.0459 | 800 | 5.5327 | - | - | - | - | - |
574
+ | 0.0516 | 900 | 5.7985 | - | - | - | - | - |
575
+ | 0.0574 | 1000 | 6.1129 | - | - | - | - | - |
576
+ | 0.0631 | 1100 | 6.1784 | - | - | - | - | - |
577
+ | 0.0688 | 1200 | 6.3647 | - | - | - | - | - |
578
+ | 0.0746 | 1300 | 7.4443 | - | - | - | - | - |
579
+ | 0.0803 | 1400 | 6.6881 | - | - | - | - | - |
580
+ | 0.0860 | 1500 | 6.09 | - | - | - | - | - |
581
+ | 0.0918 | 1600 | 5.4176 | - | - | - | - | - |
582
+ | 0.0975 | 1700 | 5.4563 | - | - | - | - | - |
583
+ | 0.1033 | 1800 | 5.7071 | - | - | - | - | - |
584
+ | 0.1090 | 1900 | 7.0201 | - | - | - | - | - |
585
+ | 0.1147 | 2000 | 6.2688 | - | - | - | - | - |
586
+ | 0.1205 | 2100 | 6.1499 | - | - | - | - | - |
587
+ | 0.1262 | 2200 | 5.947 | - | - | - | - | - |
588
+ | 0.1319 | 2300 | 5.5437 | - | - | - | - | - |
589
+ | 0.1377 | 2400 | 5.4958 | - | - | - | - | - |
590
+ | 0.1434 | 2500 | 5.5032 | - | - | - | - | - |
591
+ | 0.1491 | 2600 | 4.8026 | - | - | - | - | - |
592
+ | 0.1549 | 2700 | 5.0879 | - | - | - | - | - |
593
+ | 0.1606 | 2800 | 5.6166 | - | - | - | - | - |
594
+ | 0.1664 | 2900 | 5.8146 | - | - | - | - | - |
595
+ | 0.1721 | 3000 | 6.4168 | - | - | - | - | - |
596
+ | 0.1778 | 3100 | 6.5094 | - | - | - | - | - |
597
+ | 0.1836 | 3200 | 5.9273 | - | - | - | - | - |
598
+ | 0.1893 | 3300 | 5.6202 | - | - | - | - | - |
599
+ | 0.1950 | 3400 | 5.1419 | - | - | - | - | - |
600
+ | 0.2008 | 3500 | 5.9303 | - | - | - | - | - |
601
+ | 0.2065 | 3600 | 5.3225 | - | - | - | - | - |
602
+ | 0.2122 | 3700 | 5.5183 | - | - | - | - | - |
603
+ | 0.2180 | 3800 | 5.6644 | - | - | - | - | - |
604
+ | 0.2237 | 3900 | 6.2006 | - | - | - | - | - |
605
+ | 0.2294 | 4000 | 5.8684 | - | - | - | - | - |
606
+ | 0.2352 | 4100 | 5.5406 | - | - | - | - | - |
607
+ | 0.2409 | 4200 | 5.1763 | - | - | - | - | - |
608
+ | 0.2467 | 4300 | 5.7639 | - | - | - | - | - |
609
+ | 0.2524 | 4400 | 5.8734 | - | - | - | - | - |
610
+ | 0.2581 | 4500 | 6.0215 | - | - | - | - | - |
611
+ | 0.2639 | 4600 | 5.5183 | - | - | - | - | - |
612
+ | 0.2696 | 4700 | 5.5938 | - | - | - | - | - |
613
+ | 0.2753 | 4800 | 5.6869 | - | - | - | - | - |
614
+ | 0.2811 | 4900 | 5.1235 | - | - | - | - | - |
615
+ | 0.2868 | 5000 | 5.189 | - | - | - | - | - |
616
+ | 0.2925 | 5100 | 5.081 | - | - | - | - | - |
617
+ | 0.2983 | 5200 | 5.4992 | - | - | - | - | - |
618
+ | 0.3040 | 5300 | 5.6662 | - | - | - | - | - |
619
+ | 0.3098 | 5400 | 5.5772 | - | - | - | - | - |
620
+ | 0.3155 | 5500 | 5.3595 | - | - | - | - | - |
621
+ | 0.3212 | 5600 | 4.805 | - | - | - | - | - |
622
+ | 0.3270 | 5700 | 5.1821 | - | - | - | - | - |
623
+ | 0.3327 | 5800 | 5.3221 | - | - | - | - | - |
624
+ | 0.3384 | 5900 | 5.4223 | - | - | - | - | - |
625
+ | 0.3442 | 6000 | 5.2718 | - | - | - | - | - |
626
+ | 0.3499 | 6100 | 5.2213 | - | - | - | - | - |
627
+ | 0.3556 | 6200 | 5.5453 | - | - | - | - | - |
628
+ | 0.3614 | 6300 | 4.8502 | - | - | - | - | - |
629
+ | 0.3671 | 6400 | 4.8912 | - | - | - | - | - |
630
+ | 0.3729 | 6500 | 4.8791 | - | - | - | - | - |
631
+ | 0.3786 | 6600 | 5.2418 | - | - | - | - | - |
632
+ | 0.3843 | 6700 | 4.7621 | - | - | - | - | - |
633
+ | 0.3901 | 6800 | 4.9017 | - | - | - | - | - |
634
+ | 0.3958 | 6900 | 4.8965 | - | - | - | - | - |
635
+ | 0.4015 | 7000 | 4.6081 | - | - | - | - | - |
636
+ | 0.4073 | 7100 | 5.4256 | - | - | - | - | - |
637
+ | 0.4130 | 7200 | 5.0878 | - | - | - | - | - |
638
+ | 0.4187 | 7300 | 4.9899 | - | - | - | - | - |
639
+ | 0.4245 | 7400 | 4.8508 | - | - | - | - | - |
640
+ | 0.4302 | 7500 | 5.253 | - | - | - | - | - |
641
+ | 0.4360 | 7600 | 4.8363 | - | - | - | - | - |
642
+ | 0.4417 | 7700 | 4.5555 | - | - | - | - | - |
643
+ | 0.4474 | 7800 | 4.9668 | - | - | - | - | - |
644
+ | 0.4532 | 7900 | 5.1911 | - | - | - | - | - |
645
+ | 0.4589 | 8000 | 4.468 | - | - | - | - | - |
646
+ | 0.4646 | 8100 | 4.8253 | - | - | - | - | - |
647
+ | 0.4704 | 8200 | 4.89 | - | - | - | - | - |
648
+ | 0.4761 | 8300 | 4.5547 | - | - | - | - | - |
649
+ | 0.4818 | 8400 | 4.9499 | - | - | - | - | - |
650
+ | 0.4876 | 8500 | 4.777 | - | - | - | - | - |
651
+ | 0.4933 | 8600 | 4.8066 | - | - | - | - | - |
652
+ | 0.4991 | 8700 | 5.0615 | - | - | - | - | - |
653
+ | 0.5048 | 8800 | 4.9215 | - | - | - | - | - |
654
+ | 0.5105 | 8900 | 4.8484 | - | - | - | - | - |
655
+ | 0.5163 | 9000 | 4.6272 | - | - | - | - | - |
656
+ | 0.5220 | 9100 | 4.8225 | - | - | - | - | - |
657
+ | 0.5277 | 9200 | 4.7131 | - | - | - | - | - |
658
+ | 0.5335 | 9300 | 4.3969 | - | - | - | - | - |
659
+ | 0.5392 | 9400 | 4.4143 | - | - | - | - | - |
660
+ | 0.5449 | 9500 | 4.9588 | - | - | - | - | - |
661
+ | 0.5507 | 9600 | 4.7358 | - | - | - | - | - |
662
+ | 0.5564 | 9700 | 5.0527 | - | - | - | - | - |
663
+ | 0.5622 | 9800 | 4.852 | - | - | - | - | - |
664
+ | 0.5679 | 9900 | 5.0855 | - | - | - | - | - |
665
+ | 0.5736 | 10000 | 4.8507 | - | - | - | - | - |
666
+ | 0.5794 | 10100 | 4.8007 | - | - | - | - | - |
667
+ | 0.5851 | 10200 | 4.7279 | - | - | - | - | - |
668
+ | 0.5908 | 10300 | 5.0171 | - | - | - | - | - |
669
+ | 0.5966 | 10400 | 4.5288 | - | - | - | - | - |
670
+ | 0.6023 | 10500 | 4.4488 | - | - | - | - | - |
671
+ | 0.6080 | 10600 | 4.6557 | - | - | - | - | - |
672
+ | 0.6138 | 10700 | 4.6881 | - | - | - | - | - |
673
+ | 0.6195 | 10800 | 5.0514 | - | - | - | - | - |
674
+ | 0.6253 | 10900 | 4.6301 | - | - | - | - | - |
675
+ | 0.6310 | 11000 | 4.8233 | - | - | - | - | - |
676
+ | 0.6367 | 11100 | 5.0136 | - | - | - | - | - |
677
+ | 0.6425 | 11200 | 4.3774 | - | - | - | - | - |
678
+ | 0.6482 | 11300 | 5.1213 | - | - | - | - | - |
679
+ | 0.6539 | 11400 | 4.528 | - | - | - | - | - |
680
+ | 0.6597 | 11500 | 4.8555 | - | - | - | - | - |
681
+ | 0.6654 | 11600 | 4.2198 | - | - | - | - | - |
682
+ | 0.6711 | 11700 | 5.0931 | - | - | - | - | - |
683
+ | 0.6769 | 11800 | 4.9511 | - | - | - | - | - |
684
+ | 0.6826 | 11900 | 4.5414 | - | - | - | - | - |
685
+ | 0.6883 | 12000 | 4.5039 | - | - | - | - | - |
686
+ | 0.6941 | 12100 | 4.8238 | - | - | - | - | - |
687
+ | 0.6998 | 12200 | 4.6237 | - | - | - | - | - |
688
+ | 0.7056 | 12300 | 4.6771 | - | - | - | - | - |
689
+ | 0.7113 | 12400 | 4.6187 | - | - | - | - | - |
690
+ | 0.7170 | 12500 | 4.4485 | - | - | - | - | - |
691
+ | 0.7228 | 12600 | 4.2029 | - | - | - | - | - |
692
+ | 0.7285 | 12700 | 4.5829 | - | - | - | - | - |
693
+ | 0.7342 | 12800 | 4.617 | - | - | - | - | - |
694
+ | 0.7400 | 12900 | 4.5606 | - | - | - | - | - |
695
+ | 0.7457 | 13000 | 4.5745 | - | - | - | - | - |
696
+ | 0.7514 | 13100 | 4.1457 | - | - | - | - | - |
697
+ | 0.7572 | 13200 | 7.2499 | - | - | - | - | - |
698
+ | 0.7629 | 13300 | 6.3681 | - | - | - | - | - |
699
+ | 0.7687 | 13400 | 6.2052 | - | - | - | - | - |
700
+ | 0.7744 | 13500 | 5.9569 | - | - | - | - | - |
701
+ | 0.7801 | 13600 | 5.2649 | - | - | - | - | - |
702
+ | 0.7859 | 13700 | 5.5198 | - | - | - | - | - |
703
+ | 0.7916 | 13800 | 5.2808 | - | - | - | - | - |
704
+ | 0.7973 | 13900 | 5.1534 | - | - | - | - | - |
705
+ | 0.8031 | 14000 | 4.7831 | - | - | - | - | - |
706
+ | 0.8088 | 14100 | 4.5975 | - | - | - | - | - |
707
+ | 0.8145 | 14200 | 4.6134 | - | - | - | - | - |
708
+ | 0.8203 | 14300 | 4.5497 | - | - | - | - | - |
709
+ | 0.8260 | 14400 | 4.6003 | - | - | - | - | - |
710
+ | 0.8318 | 14500 | 4.7011 | - | - | - | - | - |
711
+ | 0.8375 | 14600 | 4.4208 | - | - | - | - | - |
712
+ | 0.8432 | 14700 | 4.4052 | - | - | - | - | - |
713
+ | 0.8490 | 14800 | 4.1121 | - | - | - | - | - |
714
+ | 0.8547 | 14900 | 4.2418 | - | - | - | - | - |
715
+ | 0.8604 | 15000 | 4.2314 | - | - | - | - | - |
716
+ | 0.8662 | 15100 | 3.8679 | - | - | - | - | - |
717
+ | 0.8719 | 15200 | 4.0173 | - | - | - | - | - |
718
+ | 0.8776 | 15300 | 4.0758 | - | - | - | - | - |
719
+ | 0.8834 | 15400 | 3.8581 | - | - | - | - | - |
720
+ | 0.8891 | 15500 | 4.0601 | - | - | - | - | - |
721
+ | 0.8949 | 15600 | 3.8738 | - | - | - | - | - |
722
+ | 0.9006 | 15700 | 4.0744 | - | - | - | - | - |
723
+ | 0.9063 | 15800 | 3.917 | - | - | - | - | - |
724
+ | 0.9121 | 15900 | 3.7996 | - | - | - | - | - |
725
+ | 0.9178 | 16000 | 3.7511 | - | - | - | - | - |
726
+ | 0.9235 | 16100 | 3.7654 | - | - | - | - | - |
727
+ | 0.9293 | 16200 | 3.6185 | - | - | - | - | - |
728
+ | 0.9350 | 16300 | 3.5877 | - | - | - | - | - |
729
+ | 0.9407 | 16400 | 3.8974 | - | - | - | - | - |
730
+ | 0.9465 | 16500 | 3.5654 | - | - | - | - | - |
731
+ | 0.9522 | 16600 | 3.6 | - | - | - | - | - |
732
+ | 0.9580 | 16700 | 3.6468 | - | - | - | - | - |
733
+ | 0.9637 | 16800 | 3.7221 | - | - | - | - | - |
734
+ | 0.9694 | 16900 | 3.5939 | - | - | - | - | - |
735
+ | 0.9752 | 17000 | 3.8597 | - | - | - | - | - |
736
+ | 0.9809 | 17100 | 3.6323 | - | - | - | - | - |
737
+ | 0.9866 | 17200 | 3.5251 | - | - | - | - | - |
738
+ | 0.9924 | 17300 | 3.6949 | - | - | - | - | - |
739
+ | 0.9981 | 17400 | 3.5682 | - | - | - | - | - |
740
+ | 1.0 | 17433 | - | 0.7000 | 0.7016 | 0.7024 | 0.6901 | 0.7038 |
741
+
742
+ </details>
743
+
744
+ ### Framework Versions
745
+ - Python: 3.11.9
746
+ - Sentence Transformers: 3.0.1
747
+ - Transformers: 4.40.1
748
+ - PyTorch: 2.3.0+cu121
749
+ - Accelerate: 0.29.3
750
+ - Datasets: 2.19.0
751
+ - Tokenizers: 0.19.1
752
+
753
+ ## Citation
754
+
755
+ ### BibTeX
756
+
757
+ #### Sentence Transformers
758
+ ```bibtex
759
+ @inproceedings{reimers-2019-sentence-bert,
760
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
761
+ author = "Reimers, Nils and Gurevych, Iryna",
762
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
763
+ month = "11",
764
+ year = "2019",
765
+ publisher = "Association for Computational Linguistics",
766
+ url = "https://arxiv.org/abs/1908.10084",
767
+ }
768
+ ```
769
+
770
+ #### MatryoshkaLoss
771
+ ```bibtex
772
+ @misc{kusupati2024matryoshka,
773
+ title={Matryoshka Representation Learning},
774
+ 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},
775
+ year={2024},
776
+ eprint={2205.13147},
777
+ archivePrefix={arXiv},
778
+ primaryClass={cs.LG}
779
+ }
780
+ ```
781
+
782
+ #### MultipleNegativesRankingLoss
783
+ ```bibtex
784
+ @misc{henderson2017efficient,
785
+ title={Efficient Natural Language Response Suggestion for Smart Reply},
786
+ 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},
787
+ year={2017},
788
+ eprint={1705.00652},
789
+ archivePrefix={arXiv},
790
+ primaryClass={cs.CL}
791
+ }
792
+ ```
793
+
794
+ <!--
795
+ ## Glossary
796
+
797
+ *Clearly define terms in order to be accessible across audiences.*
798
+ -->
799
+
800
+ <!--
801
+ ## Model Card Authors
802
+
803
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
804
+ -->
805
+
806
+ <!--
807
+ ## Model Card Contact
808
+
809
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
810
+ -->
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
+ "__version__": {
3
+ "sentence_transformers": "3.0.1",
4
+ "transformers": "4.40.1",
5
+ "pytorch": "2.3.0+cu121"
6
+ },
7
+ "prompts": {},
8
+ "default_prompt_name": null,
9
+ "similarity_fn_name": null
10
+ }
model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:3f6ac18c9bdebf19b12fa2ea92e7714f885614f3e5f168e6c89c732a3e3de94f
3
+ size 1112197096
modules.json ADDED
@@ -0,0 +1,14 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ [
2
+ {
3
+ "idx": 0,
4
+ "name": "0",
5
+ "path": "",
6
+ "type": "sentence_transformers.models.Transformer"
7
+ },
8
+ {
9
+ "idx": 1,
10
+ "name": "1",
11
+ "path": "1_Pooling",
12
+ "type": "sentence_transformers.models.Pooling"
13
+ }
14
+ ]
sentence_bert_config.json ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ {
2
+ "max_seq_length": 128,
3
+ "do_lower_case": false
4
+ }
sentencepiece.bpe.model ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:cfc8146abe2a0488e9e2a0c56de7952f7c11ab059eca145a0a727afce0db2865
3
+ size 5069051
special_tokens_map.json ADDED
@@ -0,0 +1,51 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "bos_token": {
3
+ "content": "<s>",
4
+ "lstrip": false,
5
+ "normalized": false,
6
+ "rstrip": false,
7
+ "single_word": false
8
+ },
9
+ "cls_token": {
10
+ "content": "<s>",
11
+ "lstrip": false,
12
+ "normalized": false,
13
+ "rstrip": false,
14
+ "single_word": false
15
+ },
16
+ "eos_token": {
17
+ "content": "</s>",
18
+ "lstrip": false,
19
+ "normalized": false,
20
+ "rstrip": false,
21
+ "single_word": false
22
+ },
23
+ "mask_token": {
24
+ "content": "<mask>",
25
+ "lstrip": true,
26
+ "normalized": false,
27
+ "rstrip": false,
28
+ "single_word": false
29
+ },
30
+ "pad_token": {
31
+ "content": "<pad>",
32
+ "lstrip": false,
33
+ "normalized": false,
34
+ "rstrip": false,
35
+ "single_word": false
36
+ },
37
+ "sep_token": {
38
+ "content": "</s>",
39
+ "lstrip": false,
40
+ "normalized": false,
41
+ "rstrip": false,
42
+ "single_word": false
43
+ },
44
+ "unk_token": {
45
+ "content": "<unk>",
46
+ "lstrip": false,
47
+ "normalized": false,
48
+ "rstrip": false,
49
+ "single_word": false
50
+ }
51
+ }
tokenizer.json ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:cad551d5600a84242d0973327029452a1e3672ba6313c2a3c3d69c4310e12719
3
+ size 17082987
tokenizer_config.json ADDED
@@ -0,0 +1,61 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "added_tokens_decoder": {
3
+ "0": {
4
+ "content": "<s>",
5
+ "lstrip": false,
6
+ "normalized": false,
7
+ "rstrip": false,
8
+ "single_word": false,
9
+ "special": true
10
+ },
11
+ "1": {
12
+ "content": "<pad>",
13
+ "lstrip": false,
14
+ "normalized": false,
15
+ "rstrip": false,
16
+ "single_word": false,
17
+ "special": true
18
+ },
19
+ "2": {
20
+ "content": "</s>",
21
+ "lstrip": false,
22
+ "normalized": false,
23
+ "rstrip": false,
24
+ "single_word": false,
25
+ "special": true
26
+ },
27
+ "3": {
28
+ "content": "<unk>",
29
+ "lstrip": false,
30
+ "normalized": false,
31
+ "rstrip": false,
32
+ "single_word": false,
33
+ "special": true
34
+ },
35
+ "250001": {
36
+ "content": "<mask>",
37
+ "lstrip": true,
38
+ "normalized": false,
39
+ "rstrip": false,
40
+ "single_word": false,
41
+ "special": true
42
+ }
43
+ },
44
+ "bos_token": "<s>",
45
+ "clean_up_tokenization_spaces": true,
46
+ "cls_token": "<s>",
47
+ "eos_token": "</s>",
48
+ "mask_token": "<mask>",
49
+ "max_length": 128,
50
+ "model_max_length": 128,
51
+ "pad_to_multiple_of": null,
52
+ "pad_token": "<pad>",
53
+ "pad_token_type_id": 0,
54
+ "padding_side": "right",
55
+ "sep_token": "</s>",
56
+ "stride": 0,
57
+ "tokenizer_class": "XLMRobertaTokenizer",
58
+ "truncation_side": "right",
59
+ "truncation_strategy": "longest_first",
60
+ "unk_token": "<unk>"
61
+ }