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tomaarsen
/
bert-base-uncased-gooaq-peft

Sentence Similarity
sentence-transformers
Safetensors
English
feature-extraction
Generated from Trainer
dataset_size:3012496
loss:MatryoshkaLoss
loss:CachedMultipleNegativesRankingLoss
Eval Results
Inference Endpoints
Model card Files Community
tomaarsen HF staff commited on
Commit
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1 Parent(s): 2799cd7

Add new SentenceTransformer model

Browse files
Files changed (11) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +1336 -0
  3. adapter_config.json +29 -0
  4. adapter_model.safetensors +3 -0
  5. config_sentence_transformers.json +10 -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": 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 +1,1336 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ language:
3
+ - en
4
+ license: apache-2.0
5
+ tags:
6
+ - sentence-transformers
7
+ - sentence-similarity
8
+ - feature-extraction
9
+ - generated_from_trainer
10
+ - dataset_size:3012496
11
+ - loss:MatryoshkaLoss
12
+ - loss:CachedMultipleNegativesRankingLoss
13
+ base_model: google-bert/bert-base-uncased
14
+ widget:
15
+ - source_sentence: are the sequels better than the prequels?
16
+ sentences:
17
+ - '[''Automatically.'', ''When connected to car Bluetooth and,'', ''Manually.'']'
18
+ - The prequels are also not scared to take risks, making movies which are very different
19
+ from the original trilogy. The sequel saga, on the other hand, are technically
20
+ better made films, the acting is more consistent, the CGI is better and the writing
21
+ is stronger, however it falls down in many other places.
22
+ - While both public and private sectors use budgets as a key planning tool, public
23
+ bodies balance budgets, while private sector firms use budgets to predict operating
24
+ results. The public sector budget matches expenditures on mandated assets and
25
+ services with receipts of public money such as taxes and fees.
26
+ - source_sentence: are there bbqs at lake leschenaultia?
27
+ sentences:
28
+ - Vestavia Hills. The hummingbird, or, el zunzún as they are often called in the
29
+ Caribbean, have such a nickname because of their quick movements. The ruby-throated
30
+ hummingbird, the most commonly seen hummingbird in Alabama, is the inspiration
31
+ for this restaurant.
32
+ - Common causes of abdominal tenderness Abdominal tenderness is generally a sign
33
+ of inflammation or other acute processes in one or more organs. The organs are
34
+ located around the tender area. Acute processes mean sudden pressure caused by
35
+ something. For example, twisted or blocked organs can cause point tenderness.
36
+ - ​Located on 168 hectares of nature reserve, Lake Leschenaultia is the perfect
37
+ spot for a family day out in the Perth Hills. The Lake offers canoeing, swimming,
38
+ walk and cycle trails, as well as picnic, BBQ and camping facilities. ... There
39
+ are picnic tables set amongst lovely Wandoo trees.
40
+ - source_sentence: how much folic acid should you take prenatal?
41
+ sentences:
42
+ - Folic acid is a pregnancy superhero! Taking a prenatal vitamin with the recommended
43
+ 400 micrograms (mcg) of folic acid before and during pregnancy can help prevent
44
+ birth defects of your baby's brain and spinal cord. Take it every day and go ahead
45
+ and have a bowl of fortified cereal, too.
46
+ - '[''You must be unemployed through no fault of your own, as defined by Virginia
47
+ law.'', ''You must have earned at least a minimum amount in wages before you were
48
+ unemployed.'', ''You must be able and available to work, and you must be actively
49
+ seeking employment.'']'
50
+ - Wallpaper is printed in batches of rolls. It is important to have the same batch
51
+ number, to ensure colours match exactly. The batch number is usually located on
52
+ the wallpaper label close to the pattern number. Remember batch numbers also apply
53
+ to white wallpapers, as different batches can be different shades of white.
54
+ - source_sentence: what is the difference between minerals and electrolytes?
55
+ sentences:
56
+ - 'North: Just head north of Junk Junction like so. South: Head below Lucky Landing.
57
+ East: You''re basically landing between Lonely Lodge and the Racetrack. West:
58
+ The sign is west of Snobby Shores.'
59
+ - The fasting glucose tolerance test is the simplest and fastest way to measure
60
+ blood glucose and diagnose diabetes. Fasting means that you have had nothing to
61
+ eat or drink (except water) for 8 to 12 hours before the test.
62
+ - In other words, the term “electrolyte” typically implies ionized minerals dissolved
63
+ within water and beverages. Electrolytes are typically minerals, whereas minerals
64
+ may or may not be electrolytes.
65
+ - source_sentence: how can i download youtube videos with internet download manager?
66
+ sentences:
67
+ - '[''Go to settings and then click on extensions (top left side in chrome).'',
68
+ ''Minimise your browser and open the location (folder) where IDM is installed.
69
+ ... '', ''Find the file “IDMGCExt. ... '', ''Drag this file to your chrome browser
70
+ and drop to install the IDM extension.'']'
71
+ - Coca-Cola might rot your teeth and load your body with sugar and calories, but
72
+ it's actually an effective and safe first line of treatment for some stomach blockages,
73
+ researchers say.
74
+ - To fix a disabled iPhone or iPad without iTunes, you have to erase your device.
75
+ Click on the "Erase iPhone" option and confirm your selection. Wait for a while
76
+ as the "Find My iPhone" feature will remotely erase your iOS device. Needless
77
+ to say, it will also disable its lock.
78
+ datasets:
79
+ - sentence-transformers/gooaq
80
+ pipeline_tag: sentence-similarity
81
+ library_name: sentence-transformers
82
+ metrics:
83
+ - cosine_accuracy@1
84
+ - cosine_accuracy@3
85
+ - cosine_accuracy@5
86
+ - cosine_accuracy@10
87
+ - cosine_precision@1
88
+ - cosine_precision@3
89
+ - cosine_precision@5
90
+ - cosine_precision@10
91
+ - cosine_recall@1
92
+ - cosine_recall@3
93
+ - cosine_recall@5
94
+ - cosine_recall@10
95
+ - cosine_ndcg@10
96
+ - cosine_mrr@10
97
+ - cosine_map@100
98
+ co2_eq_emissions:
99
+ emissions: 249.86917485332245
100
+ energy_consumed: 0.6428296609055844
101
+ source: codecarbon
102
+ training_type: fine-tuning
103
+ on_cloud: false
104
+ cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
105
+ ram_total_size: 31.777088165283203
106
+ hours_used: 1.727
107
+ hardware_used: 1 x NVIDIA GeForce RTX 3090
108
+ model-index:
109
+ - name: bert-base-uncased adapter finetuned on GooAQ pairs
110
+ results:
111
+ - task:
112
+ type: information-retrieval
113
+ name: Information Retrieval
114
+ dataset:
115
+ name: NanoClimateFEVER
116
+ type: NanoClimateFEVER
117
+ metrics:
118
+ - type: cosine_accuracy@1
119
+ value: 0.3
120
+ name: Cosine Accuracy@1
121
+ - type: cosine_accuracy@3
122
+ value: 0.42
123
+ name: Cosine Accuracy@3
124
+ - type: cosine_accuracy@5
125
+ value: 0.48
126
+ name: Cosine Accuracy@5
127
+ - type: cosine_accuracy@10
128
+ value: 0.54
129
+ name: Cosine Accuracy@10
130
+ - type: cosine_precision@1
131
+ value: 0.3
132
+ name: Cosine Precision@1
133
+ - type: cosine_precision@3
134
+ value: 0.16
135
+ name: Cosine Precision@3
136
+ - type: cosine_precision@5
137
+ value: 0.11600000000000002
138
+ name: Cosine Precision@5
139
+ - type: cosine_precision@10
140
+ value: 0.066
141
+ name: Cosine Precision@10
142
+ - type: cosine_recall@1
143
+ value: 0.14833333333333332
144
+ name: Cosine Recall@1
145
+ - type: cosine_recall@3
146
+ value: 0.21
147
+ name: Cosine Recall@3
148
+ - type: cosine_recall@5
149
+ value: 0.25666666666666665
150
+ name: Cosine Recall@5
151
+ - type: cosine_recall@10
152
+ value: 0.2866666666666667
153
+ name: Cosine Recall@10
154
+ - type: cosine_ndcg@10
155
+ value: 0.2612531493211831
156
+ name: Cosine Ndcg@10
157
+ - type: cosine_mrr@10
158
+ value: 0.3718333333333333
159
+ name: Cosine Mrr@10
160
+ - type: cosine_map@100
161
+ value: 0.2163485410063536
162
+ name: Cosine Map@100
163
+ - task:
164
+ type: information-retrieval
165
+ name: Information Retrieval
166
+ dataset:
167
+ name: NanoDBPedia
168
+ type: NanoDBPedia
169
+ metrics:
170
+ - type: cosine_accuracy@1
171
+ value: 0.48
172
+ name: Cosine Accuracy@1
173
+ - type: cosine_accuracy@3
174
+ value: 0.78
175
+ name: Cosine Accuracy@3
176
+ - type: cosine_accuracy@5
177
+ value: 0.82
178
+ name: Cosine Accuracy@5
179
+ - type: cosine_accuracy@10
180
+ value: 0.92
181
+ name: Cosine Accuracy@10
182
+ - type: cosine_precision@1
183
+ value: 0.48
184
+ name: Cosine Precision@1
185
+ - type: cosine_precision@3
186
+ value: 0.4599999999999999
187
+ name: Cosine Precision@3
188
+ - type: cosine_precision@5
189
+ value: 0.4159999999999999
190
+ name: Cosine Precision@5
191
+ - type: cosine_precision@10
192
+ value: 0.39
193
+ name: Cosine Precision@10
194
+ - type: cosine_recall@1
195
+ value: 0.04444293833661297
196
+ name: Cosine Recall@1
197
+ - type: cosine_recall@3
198
+ value: 0.10924065240694858
199
+ name: Cosine Recall@3
200
+ - type: cosine_recall@5
201
+ value: 0.14497857436843284
202
+ name: Cosine Recall@5
203
+ - type: cosine_recall@10
204
+ value: 0.24069548747927993
205
+ name: Cosine Recall@10
206
+ - type: cosine_ndcg@10
207
+ value: 0.45073427319400694
208
+ name: Cosine Ndcg@10
209
+ - type: cosine_mrr@10
210
+ value: 0.6354682539682539
211
+ name: Cosine Mrr@10
212
+ - type: cosine_map@100
213
+ value: 0.3182747550673792
214
+ name: Cosine Map@100
215
+ - task:
216
+ type: information-retrieval
217
+ name: Information Retrieval
218
+ dataset:
219
+ name: NanoFEVER
220
+ type: NanoFEVER
221
+ metrics:
222
+ - type: cosine_accuracy@1
223
+ value: 0.6
224
+ name: Cosine Accuracy@1
225
+ - type: cosine_accuracy@3
226
+ value: 0.84
227
+ name: Cosine Accuracy@3
228
+ - type: cosine_accuracy@5
229
+ value: 0.9
230
+ name: Cosine Accuracy@5
231
+ - type: cosine_accuracy@10
232
+ value: 0.96
233
+ name: Cosine Accuracy@10
234
+ - type: cosine_precision@1
235
+ value: 0.6
236
+ name: Cosine Precision@1
237
+ - type: cosine_precision@3
238
+ value: 0.28
239
+ name: Cosine Precision@3
240
+ - type: cosine_precision@5
241
+ value: 0.184
242
+ name: Cosine Precision@5
243
+ - type: cosine_precision@10
244
+ value: 0.09799999999999998
245
+ name: Cosine Precision@10
246
+ - type: cosine_recall@1
247
+ value: 0.59
248
+ name: Cosine Recall@1
249
+ - type: cosine_recall@3
250
+ value: 0.8
251
+ name: Cosine Recall@3
252
+ - type: cosine_recall@5
253
+ value: 0.8566666666666666
254
+ name: Cosine Recall@5
255
+ - type: cosine_recall@10
256
+ value: 0.9066666666666667
257
+ name: Cosine Recall@10
258
+ - type: cosine_ndcg@10
259
+ value: 0.7556216606985078
260
+ name: Cosine Ndcg@10
261
+ - type: cosine_mrr@10
262
+ value: 0.719190476190476
263
+ name: Cosine Mrr@10
264
+ - type: cosine_map@100
265
+ value: 0.701651515151515
266
+ name: Cosine Map@100
267
+ - task:
268
+ type: information-retrieval
269
+ name: Information Retrieval
270
+ dataset:
271
+ name: NanoFiQA2018
272
+ type: NanoFiQA2018
273
+ metrics:
274
+ - type: cosine_accuracy@1
275
+ value: 0.22
276
+ name: Cosine Accuracy@1
277
+ - type: cosine_accuracy@3
278
+ value: 0.4
279
+ name: Cosine Accuracy@3
280
+ - type: cosine_accuracy@5
281
+ value: 0.5
282
+ name: Cosine Accuracy@5
283
+ - type: cosine_accuracy@10
284
+ value: 0.6
285
+ name: Cosine Accuracy@10
286
+ - type: cosine_precision@1
287
+ value: 0.22
288
+ name: Cosine Precision@1
289
+ - type: cosine_precision@3
290
+ value: 0.18
291
+ name: Cosine Precision@3
292
+ - type: cosine_precision@5
293
+ value: 0.14
294
+ name: Cosine Precision@5
295
+ - type: cosine_precision@10
296
+ value: 0.09799999999999999
297
+ name: Cosine Precision@10
298
+ - type: cosine_recall@1
299
+ value: 0.11441269841269841
300
+ name: Cosine Recall@1
301
+ - type: cosine_recall@3
302
+ value: 0.21891269841269842
303
+ name: Cosine Recall@3
304
+ - type: cosine_recall@5
305
+ value: 0.3109126984126984
306
+ name: Cosine Recall@5
307
+ - type: cosine_recall@10
308
+ value: 0.40793650793650793
309
+ name: Cosine Recall@10
310
+ - type: cosine_ndcg@10
311
+ value: 0.2963633422018188
312
+ name: Cosine Ndcg@10
313
+ - type: cosine_mrr@10
314
+ value: 0.33072222222222225
315
+ name: Cosine Mrr@10
316
+ - type: cosine_map@100
317
+ value: 0.23341351928423923
318
+ name: Cosine Map@100
319
+ - task:
320
+ type: information-retrieval
321
+ name: Information Retrieval
322
+ dataset:
323
+ name: NanoHotpotQA
324
+ type: NanoHotpotQA
325
+ metrics:
326
+ - type: cosine_accuracy@1
327
+ value: 0.64
328
+ name: Cosine Accuracy@1
329
+ - type: cosine_accuracy@3
330
+ value: 0.74
331
+ name: Cosine Accuracy@3
332
+ - type: cosine_accuracy@5
333
+ value: 0.82
334
+ name: Cosine Accuracy@5
335
+ - type: cosine_accuracy@10
336
+ value: 0.84
337
+ name: Cosine Accuracy@10
338
+ - type: cosine_precision@1
339
+ value: 0.64
340
+ name: Cosine Precision@1
341
+ - type: cosine_precision@3
342
+ value: 0.31333333333333335
343
+ name: Cosine Precision@3
344
+ - type: cosine_precision@5
345
+ value: 0.22399999999999998
346
+ name: Cosine Precision@5
347
+ - type: cosine_precision@10
348
+ value: 0.11799999999999997
349
+ name: Cosine Precision@10
350
+ - type: cosine_recall@1
351
+ value: 0.32
352
+ name: Cosine Recall@1
353
+ - type: cosine_recall@3
354
+ value: 0.47
355
+ name: Cosine Recall@3
356
+ - type: cosine_recall@5
357
+ value: 0.56
358
+ name: Cosine Recall@5
359
+ - type: cosine_recall@10
360
+ value: 0.59
361
+ name: Cosine Recall@10
362
+ - type: cosine_ndcg@10
363
+ value: 0.5584295792789493
364
+ name: Cosine Ndcg@10
365
+ - type: cosine_mrr@10
366
+ value: 0.7015
367
+ name: Cosine Mrr@10
368
+ - type: cosine_map@100
369
+ value: 0.49543351785464007
370
+ name: Cosine Map@100
371
+ - task:
372
+ type: information-retrieval
373
+ name: Information Retrieval
374
+ dataset:
375
+ name: NanoMSMARCO
376
+ type: NanoMSMARCO
377
+ metrics:
378
+ - type: cosine_accuracy@1
379
+ value: 0.22
380
+ name: Cosine Accuracy@1
381
+ - type: cosine_accuracy@3
382
+ value: 0.46
383
+ name: Cosine Accuracy@3
384
+ - type: cosine_accuracy@5
385
+ value: 0.54
386
+ name: Cosine Accuracy@5
387
+ - type: cosine_accuracy@10
388
+ value: 0.68
389
+ name: Cosine Accuracy@10
390
+ - type: cosine_precision@1
391
+ value: 0.22
392
+ name: Cosine Precision@1
393
+ - type: cosine_precision@3
394
+ value: 0.15333333333333332
395
+ name: Cosine Precision@3
396
+ - type: cosine_precision@5
397
+ value: 0.10800000000000001
398
+ name: Cosine Precision@5
399
+ - type: cosine_precision@10
400
+ value: 0.068
401
+ name: Cosine Precision@10
402
+ - type: cosine_recall@1
403
+ value: 0.22
404
+ name: Cosine Recall@1
405
+ - type: cosine_recall@3
406
+ value: 0.46
407
+ name: Cosine Recall@3
408
+ - type: cosine_recall@5
409
+ value: 0.54
410
+ name: Cosine Recall@5
411
+ - type: cosine_recall@10
412
+ value: 0.68
413
+ name: Cosine Recall@10
414
+ - type: cosine_ndcg@10
415
+ value: 0.44155458168172074
416
+ name: Cosine Ndcg@10
417
+ - type: cosine_mrr@10
418
+ value: 0.3666904761904761
419
+ name: Cosine Mrr@10
420
+ - type: cosine_map@100
421
+ value: 0.38140126670451624
422
+ name: Cosine Map@100
423
+ - task:
424
+ type: information-retrieval
425
+ name: Information Retrieval
426
+ dataset:
427
+ name: NanoNFCorpus
428
+ type: NanoNFCorpus
429
+ metrics:
430
+ - type: cosine_accuracy@1
431
+ value: 0.32
432
+ name: Cosine Accuracy@1
433
+ - type: cosine_accuracy@3
434
+ value: 0.44
435
+ name: Cosine Accuracy@3
436
+ - type: cosine_accuracy@5
437
+ value: 0.46
438
+ name: Cosine Accuracy@5
439
+ - type: cosine_accuracy@10
440
+ value: 0.5
441
+ name: Cosine Accuracy@10
442
+ - type: cosine_precision@1
443
+ value: 0.32
444
+ name: Cosine Precision@1
445
+ - type: cosine_precision@3
446
+ value: 0.2866666666666666
447
+ name: Cosine Precision@3
448
+ - type: cosine_precision@5
449
+ value: 0.244
450
+ name: Cosine Precision@5
451
+ - type: cosine_precision@10
452
+ value: 0.17800000000000002
453
+ name: Cosine Precision@10
454
+ - type: cosine_recall@1
455
+ value: 0.022867372385014545
456
+ name: Cosine Recall@1
457
+ - type: cosine_recall@3
458
+ value: 0.051610132551984836
459
+ name: Cosine Recall@3
460
+ - type: cosine_recall@5
461
+ value: 0.061993511339545566
462
+ name: Cosine Recall@5
463
+ - type: cosine_recall@10
464
+ value: 0.07344138386002937
465
+ name: Cosine Recall@10
466
+ - type: cosine_ndcg@10
467
+ value: 0.22405550472948219
468
+ name: Cosine Ndcg@10
469
+ - type: cosine_mrr@10
470
+ value: 0.3782222222222222
471
+ name: Cosine Mrr@10
472
+ - type: cosine_map@100
473
+ value: 0.08778657539162772
474
+ name: Cosine Map@100
475
+ - task:
476
+ type: information-retrieval
477
+ name: Information Retrieval
478
+ dataset:
479
+ name: NanoNQ
480
+ type: NanoNQ
481
+ metrics:
482
+ - type: cosine_accuracy@1
483
+ value: 0.4
484
+ name: Cosine Accuracy@1
485
+ - type: cosine_accuracy@3
486
+ value: 0.54
487
+ name: Cosine Accuracy@3
488
+ - type: cosine_accuracy@5
489
+ value: 0.62
490
+ name: Cosine Accuracy@5
491
+ - type: cosine_accuracy@10
492
+ value: 0.7
493
+ name: Cosine Accuracy@10
494
+ - type: cosine_precision@1
495
+ value: 0.4
496
+ name: Cosine Precision@1
497
+ - type: cosine_precision@3
498
+ value: 0.18
499
+ name: Cosine Precision@3
500
+ - type: cosine_precision@5
501
+ value: 0.124
502
+ name: Cosine Precision@5
503
+ - type: cosine_precision@10
504
+ value: 0.07200000000000001
505
+ name: Cosine Precision@10
506
+ - type: cosine_recall@1
507
+ value: 0.4
508
+ name: Cosine Recall@1
509
+ - type: cosine_recall@3
510
+ value: 0.53
511
+ name: Cosine Recall@3
512
+ - type: cosine_recall@5
513
+ value: 0.59
514
+ name: Cosine Recall@5
515
+ - type: cosine_recall@10
516
+ value: 0.67
517
+ name: Cosine Recall@10
518
+ - type: cosine_ndcg@10
519
+ value: 0.5271006159134835
520
+ name: Cosine Ndcg@10
521
+ - type: cosine_mrr@10
522
+ value: 0.4858809523809523
523
+ name: Cosine Mrr@10
524
+ - type: cosine_map@100
525
+ value: 0.4878346435046129
526
+ name: Cosine Map@100
527
+ - task:
528
+ type: information-retrieval
529
+ name: Information Retrieval
530
+ dataset:
531
+ name: NanoQuoraRetrieval
532
+ type: NanoQuoraRetrieval
533
+ metrics:
534
+ - type: cosine_accuracy@1
535
+ value: 0.84
536
+ name: Cosine Accuracy@1
537
+ - type: cosine_accuracy@3
538
+ value: 0.98
539
+ name: Cosine Accuracy@3
540
+ - type: cosine_accuracy@5
541
+ value: 0.98
542
+ name: Cosine Accuracy@5
543
+ - type: cosine_accuracy@10
544
+ value: 1.0
545
+ name: Cosine Accuracy@10
546
+ - type: cosine_precision@1
547
+ value: 0.84
548
+ name: Cosine Precision@1
549
+ - type: cosine_precision@3
550
+ value: 0.38666666666666655
551
+ name: Cosine Precision@3
552
+ - type: cosine_precision@5
553
+ value: 0.23999999999999994
554
+ name: Cosine Precision@5
555
+ - type: cosine_precision@10
556
+ value: 0.12999999999999998
557
+ name: Cosine Precision@10
558
+ - type: cosine_recall@1
559
+ value: 0.7573333333333333
560
+ name: Cosine Recall@1
561
+ - type: cosine_recall@3
562
+ value: 0.9286666666666668
563
+ name: Cosine Recall@3
564
+ - type: cosine_recall@5
565
+ value: 0.9359999999999999
566
+ name: Cosine Recall@5
567
+ - type: cosine_recall@10
568
+ value: 0.9793333333333334
569
+ name: Cosine Recall@10
570
+ - type: cosine_ndcg@10
571
+ value: 0.9154478750600358
572
+ name: Cosine Ndcg@10
573
+ - type: cosine_mrr@10
574
+ value: 0.9053333333333333
575
+ name: Cosine Mrr@10
576
+ - type: cosine_map@100
577
+ value: 0.8889771382049948
578
+ name: Cosine Map@100
579
+ - task:
580
+ type: information-retrieval
581
+ name: Information Retrieval
582
+ dataset:
583
+ name: NanoSCIDOCS
584
+ type: NanoSCIDOCS
585
+ metrics:
586
+ - type: cosine_accuracy@1
587
+ value: 0.3
588
+ name: Cosine Accuracy@1
589
+ - type: cosine_accuracy@3
590
+ value: 0.36
591
+ name: Cosine Accuracy@3
592
+ - type: cosine_accuracy@5
593
+ value: 0.54
594
+ name: Cosine Accuracy@5
595
+ - type: cosine_accuracy@10
596
+ value: 0.68
597
+ name: Cosine Accuracy@10
598
+ - type: cosine_precision@1
599
+ value: 0.3
600
+ name: Cosine Precision@1
601
+ - type: cosine_precision@3
602
+ value: 0.2
603
+ name: Cosine Precision@3
604
+ - type: cosine_precision@5
605
+ value: 0.19200000000000003
606
+ name: Cosine Precision@5
607
+ - type: cosine_precision@10
608
+ value: 0.142
609
+ name: Cosine Precision@10
610
+ - type: cosine_recall@1
611
+ value: 0.06466666666666666
612
+ name: Cosine Recall@1
613
+ - type: cosine_recall@3
614
+ value: 0.12466666666666669
615
+ name: Cosine Recall@3
616
+ - type: cosine_recall@5
617
+ value: 0.19666666666666666
618
+ name: Cosine Recall@5
619
+ - type: cosine_recall@10
620
+ value: 0.2906666666666667
621
+ name: Cosine Recall@10
622
+ - type: cosine_ndcg@10
623
+ value: 0.2646043570275534
624
+ name: Cosine Ndcg@10
625
+ - type: cosine_mrr@10
626
+ value: 0.3836031746031746
627
+ name: Cosine Mrr@10
628
+ - type: cosine_map@100
629
+ value: 0.20582501612453505
630
+ name: Cosine Map@100
631
+ - task:
632
+ type: information-retrieval
633
+ name: Information Retrieval
634
+ dataset:
635
+ name: NanoArguAna
636
+ type: NanoArguAna
637
+ metrics:
638
+ - type: cosine_accuracy@1
639
+ value: 0.16
640
+ name: Cosine Accuracy@1
641
+ - type: cosine_accuracy@3
642
+ value: 0.52
643
+ name: Cosine Accuracy@3
644
+ - type: cosine_accuracy@5
645
+ value: 0.72
646
+ name: Cosine Accuracy@5
647
+ - type: cosine_accuracy@10
648
+ value: 0.8
649
+ name: Cosine Accuracy@10
650
+ - type: cosine_precision@1
651
+ value: 0.16
652
+ name: Cosine Precision@1
653
+ - type: cosine_precision@3
654
+ value: 0.17333333333333337
655
+ name: Cosine Precision@3
656
+ - type: cosine_precision@5
657
+ value: 0.14400000000000002
658
+ name: Cosine Precision@5
659
+ - type: cosine_precision@10
660
+ value: 0.08
661
+ name: Cosine Precision@10
662
+ - type: cosine_recall@1
663
+ value: 0.16
664
+ name: Cosine Recall@1
665
+ - type: cosine_recall@3
666
+ value: 0.52
667
+ name: Cosine Recall@3
668
+ - type: cosine_recall@5
669
+ value: 0.72
670
+ name: Cosine Recall@5
671
+ - type: cosine_recall@10
672
+ value: 0.8
673
+ name: Cosine Recall@10
674
+ - type: cosine_ndcg@10
675
+ value: 0.47137188069353025
676
+ name: Cosine Ndcg@10
677
+ - type: cosine_mrr@10
678
+ value: 0.36633333333333323
679
+ name: Cosine Mrr@10
680
+ - type: cosine_map@100
681
+ value: 0.3750999024240443
682
+ name: Cosine Map@100
683
+ - task:
684
+ type: information-retrieval
685
+ name: Information Retrieval
686
+ dataset:
687
+ name: NanoSciFact
688
+ type: NanoSciFact
689
+ metrics:
690
+ - type: cosine_accuracy@1
691
+ value: 0.38
692
+ name: Cosine Accuracy@1
693
+ - type: cosine_accuracy@3
694
+ value: 0.56
695
+ name: Cosine Accuracy@3
696
+ - type: cosine_accuracy@5
697
+ value: 0.64
698
+ name: Cosine Accuracy@5
699
+ - type: cosine_accuracy@10
700
+ value: 0.7
701
+ name: Cosine Accuracy@10
702
+ - type: cosine_precision@1
703
+ value: 0.38
704
+ name: Cosine Precision@1
705
+ - type: cosine_precision@3
706
+ value: 0.2
707
+ name: Cosine Precision@3
708
+ - type: cosine_precision@5
709
+ value: 0.14
710
+ name: Cosine Precision@5
711
+ - type: cosine_precision@10
712
+ value: 0.07800000000000001
713
+ name: Cosine Precision@10
714
+ - type: cosine_recall@1
715
+ value: 0.345
716
+ name: Cosine Recall@1
717
+ - type: cosine_recall@3
718
+ value: 0.525
719
+ name: Cosine Recall@3
720
+ - type: cosine_recall@5
721
+ value: 0.615
722
+ name: Cosine Recall@5
723
+ - type: cosine_recall@10
724
+ value: 0.68
725
+ name: Cosine Recall@10
726
+ - type: cosine_ndcg@10
727
+ value: 0.521095291928473
728
+ name: Cosine Ndcg@10
729
+ - type: cosine_mrr@10
730
+ value: 0.4848333333333332
731
+ name: Cosine Mrr@10
732
+ - type: cosine_map@100
733
+ value: 0.4707221516167083
734
+ name: Cosine Map@100
735
+ - task:
736
+ type: information-retrieval
737
+ name: Information Retrieval
738
+ dataset:
739
+ name: NanoTouche2020
740
+ type: NanoTouche2020
741
+ metrics:
742
+ - type: cosine_accuracy@1
743
+ value: 0.3673469387755102
744
+ name: Cosine Accuracy@1
745
+ - type: cosine_accuracy@3
746
+ value: 0.8571428571428571
747
+ name: Cosine Accuracy@3
748
+ - type: cosine_accuracy@5
749
+ value: 0.9387755102040817
750
+ name: Cosine Accuracy@5
751
+ - type: cosine_accuracy@10
752
+ value: 1.0
753
+ name: Cosine Accuracy@10
754
+ - type: cosine_precision@1
755
+ value: 0.3673469387755102
756
+ name: Cosine Precision@1
757
+ - type: cosine_precision@3
758
+ value: 0.4965986394557823
759
+ name: Cosine Precision@3
760
+ - type: cosine_precision@5
761
+ value: 0.4489795918367347
762
+ name: Cosine Precision@5
763
+ - type: cosine_precision@10
764
+ value: 0.39387755102040817
765
+ name: Cosine Precision@10
766
+ - type: cosine_recall@1
767
+ value: 0.03066633506656198
768
+ name: Cosine Recall@1
769
+ - type: cosine_recall@3
770
+ value: 0.1123508290418132
771
+ name: Cosine Recall@3
772
+ - type: cosine_recall@5
773
+ value: 0.1616156991422983
774
+ name: Cosine Recall@5
775
+ - type: cosine_recall@10
776
+ value: 0.2674040762687923
777
+ name: Cosine Recall@10
778
+ - type: cosine_ndcg@10
779
+ value: 0.42905651691216934
780
+ name: Cosine Ndcg@10
781
+ - type: cosine_mrr@10
782
+ value: 0.6237204405571752
783
+ name: Cosine Mrr@10
784
+ - type: cosine_map@100
785
+ value: 0.32876348596122706
786
+ name: Cosine Map@100
787
+ - task:
788
+ type: nano-beir
789
+ name: Nano BEIR
790
+ dataset:
791
+ name: NanoBEIR mean
792
+ type: NanoBEIR_mean
793
+ metrics:
794
+ - type: cosine_accuracy@1
795
+ value: 0.40210361067503925
796
+ name: Cosine Accuracy@1
797
+ - type: cosine_accuracy@3
798
+ value: 0.6074725274725276
799
+ name: Cosine Accuracy@3
800
+ - type: cosine_accuracy@5
801
+ value: 0.6891365777080062
802
+ name: Cosine Accuracy@5
803
+ - type: cosine_accuracy@10
804
+ value: 0.7630769230769231
805
+ name: Cosine Accuracy@10
806
+ - type: cosine_precision@1
807
+ value: 0.40210361067503925
808
+ name: Cosine Precision@1
809
+ - type: cosine_precision@3
810
+ value: 0.26691784406070124
811
+ name: Cosine Precision@3
812
+ - type: cosine_precision@5
813
+ value: 0.2093061224489796
814
+ name: Cosine Precision@5
815
+ - type: cosine_precision@10
816
+ value: 0.14706750392464676
817
+ name: Cosine Precision@10
818
+ - type: cosine_recall@1
819
+ value: 0.247517129041094
820
+ name: Cosine Recall@1
821
+ - type: cosine_recall@3
822
+ value: 0.38926520351898297
823
+ name: Cosine Recall@3
824
+ - type: cosine_recall@5
825
+ value: 0.4577308064048442
826
+ name: Cosine Recall@5
827
+ - type: cosine_recall@10
828
+ value: 0.5286777529906109
829
+ name: Cosine Recall@10
830
+ - type: cosine_ndcg@10
831
+ value: 0.47051450989545496
832
+ name: Cosine Ndcg@10
833
+ - type: cosine_mrr@10
834
+ value: 0.519487042436022
835
+ name: Cosine Mrr@10
836
+ - type: cosine_map@100
837
+ value: 0.399348617561261
838
+ name: Cosine Map@100
839
+ ---
840
+
841
+ # bert-base-uncased adapter finetuned on GooAQ pairs
842
+
843
+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) dataset. 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.
844
+
845
+ ## Model Details
846
+
847
+ ### Model Description
848
+ - **Model Type:** Sentence Transformer
849
+ - **Base model:** [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) <!-- at revision 86b5e0934494bd15c9632b12f734a8a67f723594 -->
850
+ - **Maximum Sequence Length:** 512 tokens
851
+ - **Output Dimensionality:** 768 dimensions
852
+ - **Similarity Function:** Cosine Similarity
853
+ - **Training Dataset:**
854
+ - [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq)
855
+ - **Language:** en
856
+ - **License:** apache-2.0
857
+
858
+ ### Model Sources
859
+
860
+ - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
861
+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
862
+ - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
863
+
864
+ ### Full Model Architecture
865
+
866
+ ```
867
+ SentenceTransformer(
868
+ (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
869
+ (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})
870
+ )
871
+ ```
872
+
873
+ ## Usage
874
+
875
+ ### Direct Usage (Sentence Transformers)
876
+
877
+ First install the Sentence Transformers library:
878
+
879
+ ```bash
880
+ pip install -U sentence-transformers
881
+ ```
882
+
883
+ Then you can load this model and run inference.
884
+ ```python
885
+ from sentence_transformers import SentenceTransformer
886
+
887
+ # Download from the 🤗 Hub
888
+ model = SentenceTransformer("tomaarsen/bert-base-uncased-gooaq-peft")
889
+ # Run inference
890
+ sentences = [
891
+ 'how can i download youtube videos with internet download manager?',
892
+ "['Go to settings and then click on extensions (top left side in chrome).', 'Minimise your browser and open the location (folder) where IDM is installed. ... ', 'Find the file “IDMGCExt. ... ', 'Drag this file to your chrome browser and drop to install the IDM extension.']",
893
+ "Coca-Cola might rot your teeth and load your body with sugar and calories, but it's actually an effective and safe first line of treatment for some stomach blockages, researchers say.",
894
+ ]
895
+ embeddings = model.encode(sentences)
896
+ print(embeddings.shape)
897
+ # [3, 768]
898
+
899
+ # Get the similarity scores for the embeddings
900
+ similarities = model.similarity(embeddings, embeddings)
901
+ print(similarities.shape)
902
+ # [3, 3]
903
+ ```
904
+
905
+ <!--
906
+ ### Direct Usage (Transformers)
907
+
908
+ <details><summary>Click to see the direct usage in Transformers</summary>
909
+
910
+ </details>
911
+ -->
912
+
913
+ <!--
914
+ ### Downstream Usage (Sentence Transformers)
915
+
916
+ You can finetune this model on your own dataset.
917
+
918
+ <details><summary>Click to expand</summary>
919
+
920
+ </details>
921
+ -->
922
+
923
+ <!--
924
+ ### Out-of-Scope Use
925
+
926
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
927
+ -->
928
+
929
+ ## Evaluation
930
+
931
+ ### Metrics
932
+
933
+ #### Information Retrieval
934
+
935
+ * Datasets: `NanoClimateFEVER`, `NanoDBPedia`, `NanoFEVER`, `NanoFiQA2018`, `NanoHotpotQA`, `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoQuoraRetrieval`, `NanoSCIDOCS`, `NanoArguAna`, `NanoSciFact` and `NanoTouche2020`
936
+ * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
937
+
938
+ | Metric | NanoClimateFEVER | NanoDBPedia | NanoFEVER | NanoFiQA2018 | NanoHotpotQA | NanoMSMARCO | NanoNFCorpus | NanoNQ | NanoQuoraRetrieval | NanoSCIDOCS | NanoArguAna | NanoSciFact | NanoTouche2020 |
939
+ |:--------------------|:-----------------|:------------|:-----------|:-------------|:-------------|:------------|:-------------|:-----------|:-------------------|:------------|:------------|:------------|:---------------|
940
+ | cosine_accuracy@1 | 0.3 | 0.48 | 0.6 | 0.22 | 0.64 | 0.22 | 0.32 | 0.4 | 0.84 | 0.3 | 0.16 | 0.38 | 0.3673 |
941
+ | cosine_accuracy@3 | 0.42 | 0.78 | 0.84 | 0.4 | 0.74 | 0.46 | 0.44 | 0.54 | 0.98 | 0.36 | 0.52 | 0.56 | 0.8571 |
942
+ | cosine_accuracy@5 | 0.48 | 0.82 | 0.9 | 0.5 | 0.82 | 0.54 | 0.46 | 0.62 | 0.98 | 0.54 | 0.72 | 0.64 | 0.9388 |
943
+ | cosine_accuracy@10 | 0.54 | 0.92 | 0.96 | 0.6 | 0.84 | 0.68 | 0.5 | 0.7 | 1.0 | 0.68 | 0.8 | 0.7 | 1.0 |
944
+ | cosine_precision@1 | 0.3 | 0.48 | 0.6 | 0.22 | 0.64 | 0.22 | 0.32 | 0.4 | 0.84 | 0.3 | 0.16 | 0.38 | 0.3673 |
945
+ | cosine_precision@3 | 0.16 | 0.46 | 0.28 | 0.18 | 0.3133 | 0.1533 | 0.2867 | 0.18 | 0.3867 | 0.2 | 0.1733 | 0.2 | 0.4966 |
946
+ | cosine_precision@5 | 0.116 | 0.416 | 0.184 | 0.14 | 0.224 | 0.108 | 0.244 | 0.124 | 0.24 | 0.192 | 0.144 | 0.14 | 0.449 |
947
+ | cosine_precision@10 | 0.066 | 0.39 | 0.098 | 0.098 | 0.118 | 0.068 | 0.178 | 0.072 | 0.13 | 0.142 | 0.08 | 0.078 | 0.3939 |
948
+ | cosine_recall@1 | 0.1483 | 0.0444 | 0.59 | 0.1144 | 0.32 | 0.22 | 0.0229 | 0.4 | 0.7573 | 0.0647 | 0.16 | 0.345 | 0.0307 |
949
+ | cosine_recall@3 | 0.21 | 0.1092 | 0.8 | 0.2189 | 0.47 | 0.46 | 0.0516 | 0.53 | 0.9287 | 0.1247 | 0.52 | 0.525 | 0.1124 |
950
+ | cosine_recall@5 | 0.2567 | 0.145 | 0.8567 | 0.3109 | 0.56 | 0.54 | 0.062 | 0.59 | 0.936 | 0.1967 | 0.72 | 0.615 | 0.1616 |
951
+ | cosine_recall@10 | 0.2867 | 0.2407 | 0.9067 | 0.4079 | 0.59 | 0.68 | 0.0734 | 0.67 | 0.9793 | 0.2907 | 0.8 | 0.68 | 0.2674 |
952
+ | **cosine_ndcg@10** | **0.2613** | **0.4507** | **0.7556** | **0.2964** | **0.5584** | **0.4416** | **0.2241** | **0.5271** | **0.9154** | **0.2646** | **0.4714** | **0.5211** | **0.4291** |
953
+ | cosine_mrr@10 | 0.3718 | 0.6355 | 0.7192 | 0.3307 | 0.7015 | 0.3667 | 0.3782 | 0.4859 | 0.9053 | 0.3836 | 0.3663 | 0.4848 | 0.6237 |
954
+ | cosine_map@100 | 0.2163 | 0.3183 | 0.7017 | 0.2334 | 0.4954 | 0.3814 | 0.0878 | 0.4878 | 0.889 | 0.2058 | 0.3751 | 0.4707 | 0.3288 |
955
+
956
+ #### Nano BEIR
957
+
958
+ * Dataset: `NanoBEIR_mean`
959
+ * Evaluated with [<code>NanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.NanoBEIREvaluator)
960
+
961
+ | Metric | Value |
962
+ |:--------------------|:-----------|
963
+ | cosine_accuracy@1 | 0.4021 |
964
+ | cosine_accuracy@3 | 0.6075 |
965
+ | cosine_accuracy@5 | 0.6891 |
966
+ | cosine_accuracy@10 | 0.7631 |
967
+ | cosine_precision@1 | 0.4021 |
968
+ | cosine_precision@3 | 0.2669 |
969
+ | cosine_precision@5 | 0.2093 |
970
+ | cosine_precision@10 | 0.1471 |
971
+ | cosine_recall@1 | 0.2475 |
972
+ | cosine_recall@3 | 0.3893 |
973
+ | cosine_recall@5 | 0.4577 |
974
+ | cosine_recall@10 | 0.5287 |
975
+ | **cosine_ndcg@10** | **0.4705** |
976
+ | cosine_mrr@10 | 0.5195 |
977
+ | cosine_map@100 | 0.3993 |
978
+
979
+ <!--
980
+ ## Bias, Risks and Limitations
981
+
982
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
983
+ -->
984
+
985
+ <!--
986
+ ### Recommendations
987
+
988
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
989
+ -->
990
+
991
+ ## Training Details
992
+
993
+ ### Training Dataset
994
+
995
+ #### gooaq
996
+
997
+ * Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c)
998
+ * Size: 3,012,496 training samples
999
+ * Columns: <code>question</code> and <code>answer</code>
1000
+ * Approximate statistics based on the first 1000 samples:
1001
+ | | question | answer |
1002
+ |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
1003
+ | type | string | string |
1004
+ | details | <ul><li>min: 8 tokens</li><li>mean: 11.86 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 60.48 tokens</li><li>max: 138 tokens</li></ul> |
1005
+ * Samples:
1006
+ | question | answer |
1007
+ |:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
1008
+ | <code>what is the difference between broilers and layers?</code> | <code>An egg laying poultry is called egger or layer whereas broilers are reared for obtaining meat. So a layer should be able to produce more number of large sized eggs, without growing too much. On the other hand, a broiler should yield more meat and hence should be able to grow well.</code> |
1009
+ | <code>what is the difference between chronological order and spatial order?</code> | <code>As a writer, you should always remember that unlike chronological order and the other organizational methods for data, spatial order does not take into account the time. Spatial order is primarily focused on the location. All it does is take into account the location of objects and not the time.</code> |
1010
+ | <code>is kamagra same as viagra?</code> | <code>Kamagra is thought to contain the same active ingredient as Viagra, sildenafil citrate. In theory, it should work in much the same way as Viagra, taking about 45 minutes to take effect, and lasting for around 4-6 hours. However, this will vary from person to person.</code> |
1011
+ * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
1012
+ ```json
1013
+ {
1014
+ "loss": "CachedMultipleNegativesRankingLoss",
1015
+ "matryoshka_dims": [
1016
+ 768,
1017
+ 512,
1018
+ 256,
1019
+ 128,
1020
+ 64,
1021
+ 32
1022
+ ],
1023
+ "matryoshka_weights": [
1024
+ 1,
1025
+ 1,
1026
+ 1,
1027
+ 1,
1028
+ 1,
1029
+ 1
1030
+ ],
1031
+ "n_dims_per_step": -1
1032
+ }
1033
+ ```
1034
+
1035
+ ### Evaluation Dataset
1036
+
1037
+ #### gooaq
1038
+
1039
+ * Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c)
1040
+ * Size: 3,012,496 evaluation samples
1041
+ * Columns: <code>question</code> and <code>answer</code>
1042
+ * Approximate statistics based on the first 1000 samples:
1043
+ | | question | answer |
1044
+ |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
1045
+ | type | string | string |
1046
+ | details | <ul><li>min: 8 tokens</li><li>mean: 11.88 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 61.03 tokens</li><li>max: 127 tokens</li></ul> |
1047
+ * Samples:
1048
+ | question | answer |
1049
+ |:-----------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
1050
+ | <code>how do i program my directv remote with my tv?</code> | <code>['Press MENU on your remote.', 'Select Settings & Help > Settings > Remote Control > Program Remote.', 'Choose the device (TV, audio, DVD) you wish to program. ... ', 'Follow the on-screen prompts to complete programming.']</code> |
1051
+ | <code>are rodrigues fruit bats nocturnal?</code> | <code>Before its numbers were threatened by habitat destruction, storms, and hunting, some of those groups could number 500 or more members. Sunrise, sunset. Rodrigues fruit bats are most active at dawn, at dusk, and at night.</code> |
1052
+ | <code>why does your heart rate increase during exercise bbc bitesize?</code> | <code>During exercise there is an increase in physical activity and muscle cells respire more than they do when the body is at rest. The heart rate increases during exercise. The rate and depth of breathing increases - this makes sure that more oxygen is absorbed into the blood, and more carbon dioxide is removed from it.</code> |
1053
+ * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
1054
+ ```json
1055
+ {
1056
+ "loss": "CachedMultipleNegativesRankingLoss",
1057
+ "matryoshka_dims": [
1058
+ 768,
1059
+ 512,
1060
+ 256,
1061
+ 128,
1062
+ 64,
1063
+ 32
1064
+ ],
1065
+ "matryoshka_weights": [
1066
+ 1,
1067
+ 1,
1068
+ 1,
1069
+ 1,
1070
+ 1,
1071
+ 1
1072
+ ],
1073
+ "n_dims_per_step": -1
1074
+ }
1075
+ ```
1076
+
1077
+ ### Training Hyperparameters
1078
+ #### Non-Default Hyperparameters
1079
+
1080
+ - `eval_strategy`: steps
1081
+ - `per_device_train_batch_size`: 1024
1082
+ - `per_device_eval_batch_size`: 1024
1083
+ - `learning_rate`: 2e-05
1084
+ - `num_train_epochs`: 1
1085
+ - `warmup_ratio`: 0.1
1086
+ - `seed`: 12
1087
+ - `bf16`: True
1088
+ - `batch_sampler`: no_duplicates
1089
+
1090
+ #### All Hyperparameters
1091
+ <details><summary>Click to expand</summary>
1092
+
1093
+ - `overwrite_output_dir`: False
1094
+ - `do_predict`: False
1095
+ - `eval_strategy`: steps
1096
+ - `prediction_loss_only`: True
1097
+ - `per_device_train_batch_size`: 1024
1098
+ - `per_device_eval_batch_size`: 1024
1099
+ - `per_gpu_train_batch_size`: None
1100
+ - `per_gpu_eval_batch_size`: None
1101
+ - `gradient_accumulation_steps`: 1
1102
+ - `eval_accumulation_steps`: None
1103
+ - `torch_empty_cache_steps`: None
1104
+ - `learning_rate`: 2e-05
1105
+ - `weight_decay`: 0.0
1106
+ - `adam_beta1`: 0.9
1107
+ - `adam_beta2`: 0.999
1108
+ - `adam_epsilon`: 1e-08
1109
+ - `max_grad_norm`: 1.0
1110
+ - `num_train_epochs`: 1
1111
+ - `max_steps`: -1
1112
+ - `lr_scheduler_type`: linear
1113
+ - `lr_scheduler_kwargs`: {}
1114
+ - `warmup_ratio`: 0.1
1115
+ - `warmup_steps`: 0
1116
+ - `log_level`: passive
1117
+ - `log_level_replica`: warning
1118
+ - `log_on_each_node`: True
1119
+ - `logging_nan_inf_filter`: True
1120
+ - `save_safetensors`: True
1121
+ - `save_on_each_node`: False
1122
+ - `save_only_model`: False
1123
+ - `restore_callback_states_from_checkpoint`: False
1124
+ - `no_cuda`: False
1125
+ - `use_cpu`: False
1126
+ - `use_mps_device`: False
1127
+ - `seed`: 12
1128
+ - `data_seed`: None
1129
+ - `jit_mode_eval`: False
1130
+ - `use_ipex`: False
1131
+ - `bf16`: True
1132
+ - `fp16`: False
1133
+ - `fp16_opt_level`: O1
1134
+ - `half_precision_backend`: auto
1135
+ - `bf16_full_eval`: False
1136
+ - `fp16_full_eval`: False
1137
+ - `tf32`: None
1138
+ - `local_rank`: 0
1139
+ - `ddp_backend`: None
1140
+ - `tpu_num_cores`: None
1141
+ - `tpu_metrics_debug`: False
1142
+ - `debug`: []
1143
+ - `dataloader_drop_last`: False
1144
+ - `dataloader_num_workers`: 0
1145
+ - `dataloader_prefetch_factor`: None
1146
+ - `past_index`: -1
1147
+ - `disable_tqdm`: False
1148
+ - `remove_unused_columns`: True
1149
+ - `label_names`: None
1150
+ - `load_best_model_at_end`: False
1151
+ - `ignore_data_skip`: False
1152
+ - `fsdp`: []
1153
+ - `fsdp_min_num_params`: 0
1154
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
1155
+ - `fsdp_transformer_layer_cls_to_wrap`: None
1156
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
1157
+ - `deepspeed`: None
1158
+ - `label_smoothing_factor`: 0.0
1159
+ - `optim`: adamw_torch
1160
+ - `optim_args`: None
1161
+ - `adafactor`: False
1162
+ - `group_by_length`: False
1163
+ - `length_column_name`: length
1164
+ - `ddp_find_unused_parameters`: None
1165
+ - `ddp_bucket_cap_mb`: None
1166
+ - `ddp_broadcast_buffers`: False
1167
+ - `dataloader_pin_memory`: True
1168
+ - `dataloader_persistent_workers`: False
1169
+ - `skip_memory_metrics`: True
1170
+ - `use_legacy_prediction_loop`: False
1171
+ - `push_to_hub`: False
1172
+ - `resume_from_checkpoint`: None
1173
+ - `hub_model_id`: None
1174
+ - `hub_strategy`: every_save
1175
+ - `hub_private_repo`: False
1176
+ - `hub_always_push`: False
1177
+ - `gradient_checkpointing`: False
1178
+ - `gradient_checkpointing_kwargs`: None
1179
+ - `include_inputs_for_metrics`: False
1180
+ - `include_for_metrics`: []
1181
+ - `eval_do_concat_batches`: True
1182
+ - `fp16_backend`: auto
1183
+ - `push_to_hub_model_id`: None
1184
+ - `push_to_hub_organization`: None
1185
+ - `mp_parameters`:
1186
+ - `auto_find_batch_size`: False
1187
+ - `full_determinism`: False
1188
+ - `torchdynamo`: None
1189
+ - `ray_scope`: last
1190
+ - `ddp_timeout`: 1800
1191
+ - `torch_compile`: False
1192
+ - `torch_compile_backend`: None
1193
+ - `torch_compile_mode`: None
1194
+ - `dispatch_batches`: None
1195
+ - `split_batches`: None
1196
+ - `include_tokens_per_second`: False
1197
+ - `include_num_input_tokens_seen`: False
1198
+ - `neftune_noise_alpha`: None
1199
+ - `optim_target_modules`: None
1200
+ - `batch_eval_metrics`: False
1201
+ - `eval_on_start`: False
1202
+ - `use_liger_kernel`: False
1203
+ - `eval_use_gather_object`: False
1204
+ - `average_tokens_across_devices`: False
1205
+ - `prompts`: None
1206
+ - `batch_sampler`: no_duplicates
1207
+ - `multi_dataset_batch_sampler`: proportional
1208
+
1209
+ </details>
1210
+
1211
+ ### Training Logs
1212
+ | Epoch | Step | Training Loss | Validation Loss | NanoClimateFEVER_cosine_ndcg@10 | NanoDBPedia_cosine_ndcg@10 | NanoFEVER_cosine_ndcg@10 | NanoFiQA2018_cosine_ndcg@10 | NanoHotpotQA_cosine_ndcg@10 | NanoMSMARCO_cosine_ndcg@10 | NanoNFCorpus_cosine_ndcg@10 | NanoNQ_cosine_ndcg@10 | NanoQuoraRetrieval_cosine_ndcg@10 | NanoSCIDOCS_cosine_ndcg@10 | NanoArguAna_cosine_ndcg@10 | NanoSciFact_cosine_ndcg@10 | NanoTouche2020_cosine_ndcg@10 | NanoBEIR_mean_cosine_ndcg@10 |
1213
+ |:------:|:----:|:-------------:|:---------------:|:-------------------------------:|:--------------------------:|:------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:---------------------:|:---------------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:-----------------------------:|:----------------------------:|
1214
+ | 0 | 0 | - | - | 0.1046 | 0.2182 | 0.1573 | 0.0575 | 0.2597 | 0.1602 | 0.0521 | 0.0493 | 0.7310 | 0.1320 | 0.2309 | 0.1240 | 0.0970 | 0.1826 |
1215
+ | 0.0010 | 1 | 28.4479 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1216
+ | 0.0256 | 25 | 27.0904 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1217
+ | 0.0512 | 50 | 19.016 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1218
+ | 0.0768 | 75 | 12.2306 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1219
+ | 0.1024 | 100 | 9.0613 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1220
+ | 0.1279 | 125 | 7.393 | 3.7497 | 0.2787 | 0.4840 | 0.7029 | 0.2589 | 0.5208 | 0.4094 | 0.2117 | 0.4526 | 0.9042 | 0.2503 | 0.5280 | 0.4922 | 0.4132 | 0.4544 |
1221
+ | 0.1535 | 150 | 6.6613 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1222
+ | 0.1791 | 175 | 6.1911 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1223
+ | 0.2047 | 200 | 5.9305 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1224
+ | 0.2303 | 225 | 5.6825 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1225
+ | 0.2559 | 250 | 5.5326 | 2.8771 | 0.2867 | 0.4619 | 0.7333 | 0.2835 | 0.5549 | 0.4056 | 0.2281 | 0.4883 | 0.9137 | 0.2555 | 0.5114 | 0.5220 | 0.4298 | 0.4673 |
1226
+ | 0.2815 | 275 | 5.1671 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1227
+ | 0.3071 | 300 | 5.2006 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1228
+ | 0.3327 | 325 | 5.0447 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1229
+ | 0.3582 | 350 | 4.9647 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1230
+ | 0.3838 | 375 | 4.8521 | 2.5709 | 0.2881 | 0.4577 | 0.7438 | 0.2909 | 0.5712 | 0.4093 | 0.2273 | 0.5141 | 0.9008 | 0.2668 | 0.5117 | 0.5253 | 0.4331 | 0.4723 |
1231
+ | 0.4094 | 400 | 4.8423 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1232
+ | 0.4350 | 425 | 4.7472 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1233
+ | 0.4606 | 450 | 4.6527 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1234
+ | 0.4862 | 475 | 4.61 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1235
+ | 0.5118 | 500 | 4.5451 | 2.4136 | 0.2786 | 0.4464 | 0.7485 | 0.2961 | 0.5638 | 0.4368 | 0.2269 | 0.5125 | 0.8998 | 0.2680 | 0.4938 | 0.5341 | 0.4383 | 0.4726 |
1236
+ | 0.5374 | 525 | 4.5357 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1237
+ | 0.5629 | 550 | 4.481 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1238
+ | 0.5885 | 575 | 4.4669 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1239
+ | 0.6141 | 600 | 4.3886 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1240
+ | 0.6397 | 625 | 4.2929 | 2.3091 | 0.2639 | 0.4475 | 0.7521 | 0.3095 | 0.5619 | 0.4448 | 0.2244 | 0.5178 | 0.9102 | 0.2655 | 0.4809 | 0.5253 | 0.4351 | 0.4722 |
1241
+ | 0.6653 | 650 | 4.2558 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1242
+ | 0.6909 | 675 | 4.3228 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1243
+ | 0.7165 | 700 | 4.2496 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1244
+ | 0.7421 | 725 | 4.2304 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1245
+ | 0.7677 | 750 | 4.224 | 2.2440 | 0.2628 | 0.4514 | 0.7387 | 0.3028 | 0.5522 | 0.4313 | 0.2253 | 0.5266 | 0.9211 | 0.2675 | 0.4929 | 0.5232 | 0.4351 | 0.4716 |
1246
+ | 0.7932 | 775 | 4.2821 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1247
+ | 0.8188 | 800 | 4.2686 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1248
+ | 0.8444 | 825 | 4.1657 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1249
+ | 0.8700 | 850 | 4.2297 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1250
+ | 0.8956 | 875 | 4.1709 | 2.2142 | 0.2685 | 0.4520 | 0.7569 | 0.2930 | 0.5625 | 0.4486 | 0.2229 | 0.5280 | 0.9153 | 0.2601 | 0.4862 | 0.5199 | 0.4334 | 0.4729 |
1251
+ | 0.9212 | 900 | 4.0771 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1252
+ | 0.9468 | 925 | 4.1492 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1253
+ | 0.9724 | 950 | 4.2074 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1254
+ | 0.9980 | 975 | 4.0993 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1255
+ | 1.0 | 977 | - | - | 0.2613 | 0.4507 | 0.7556 | 0.2964 | 0.5584 | 0.4416 | 0.2241 | 0.5271 | 0.9154 | 0.2646 | 0.4714 | 0.5211 | 0.4291 | 0.4705 |
1256
+
1257
+
1258
+ ### Environmental Impact
1259
+ Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
1260
+ - **Energy Consumed**: 0.643 kWh
1261
+ - **Carbon Emitted**: 0.250 kg of CO2
1262
+ - **Hours Used**: 1.727 hours
1263
+
1264
+ ### Training Hardware
1265
+ - **On Cloud**: No
1266
+ - **GPU Model**: 1 x NVIDIA GeForce RTX 3090
1267
+ - **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
1268
+ - **RAM Size**: 31.78 GB
1269
+
1270
+ ### Framework Versions
1271
+ - Python: 3.11.6
1272
+ - Sentence Transformers: 3.4.0.dev0
1273
+ - Transformers: 4.46.2
1274
+ - PyTorch: 2.5.0+cu121
1275
+ - Accelerate: 0.35.0.dev0
1276
+ - Datasets: 2.20.0
1277
+ - Tokenizers: 0.20.3
1278
+
1279
+ ## Citation
1280
+
1281
+ ### BibTeX
1282
+
1283
+ #### Sentence Transformers
1284
+ ```bibtex
1285
+ @inproceedings{reimers-2019-sentence-bert,
1286
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
1287
+ author = "Reimers, Nils and Gurevych, Iryna",
1288
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
1289
+ month = "11",
1290
+ year = "2019",
1291
+ publisher = "Association for Computational Linguistics",
1292
+ url = "https://arxiv.org/abs/1908.10084",
1293
+ }
1294
+ ```
1295
+
1296
+ #### MatryoshkaLoss
1297
+ ```bibtex
1298
+ @misc{kusupati2024matryoshka,
1299
+ title={Matryoshka Representation Learning},
1300
+ 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},
1301
+ year={2024},
1302
+ eprint={2205.13147},
1303
+ archivePrefix={arXiv},
1304
+ primaryClass={cs.LG}
1305
+ }
1306
+ ```
1307
+
1308
+ #### CachedMultipleNegativesRankingLoss
1309
+ ```bibtex
1310
+ @misc{gao2021scaling,
1311
+ title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
1312
+ author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
1313
+ year={2021},
1314
+ eprint={2101.06983},
1315
+ archivePrefix={arXiv},
1316
+ primaryClass={cs.LG}
1317
+ }
1318
+ ```
1319
+
1320
+ <!--
1321
+ ## Glossary
1322
+
1323
+ *Clearly define terms in order to be accessible across audiences.*
1324
+ -->
1325
+
1326
+ <!--
1327
+ ## Model Card Authors
1328
+
1329
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
1330
+ -->
1331
+
1332
+ <!--
1333
+ ## Model Card Contact
1334
+
1335
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
1336
+ -->
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