WpythonW/RUbert-tiny_custom_test_2

SentenceTransformer based on cointegrated/rubert-tiny2

This is a sentence-transformers model finetuned from cointegrated/rubert-tiny2. It maps sentences & paragraphs to a 312-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: cointegrated/rubert-tiny2
• Maximum Sequence Length: 2048 tokens
• Output Dimensionality: 312 tokens
• Similarity Function: Cosine Similarity

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 2048, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 312, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("WpythonW/RUbert-tiny_custom_test_2")
# Run inference
sentences = [
    'когда я получу деньги за отпуск',
    'Отпускные начисляются не позднее чем за три рабочих дня до даты начала отпуска.',
    'Создайте, пожалуйста, обращение в ИТ поддержку на портале support',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 312]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Information Retrieval

• Dataset: test
• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7761
cosine_accuracy@3	0.9337
cosine_accuracy@5	0.9675
cosine_precision@1	0.7761
cosine_precision@3	0.3112
cosine_precision@5	0.1935
cosine_precision@10	0.0985
cosine_recall@1	0.7761
cosine_recall@3	0.9337
cosine_recall@5	0.9675
cosine_recall@10	0.9853
cosine_ndcg@10	0.8899
cosine_mrr@10	0.8582
cosine_map@100	0.859
dot_accuracy@1	0.7761
dot_accuracy@3	0.9337
dot_accuracy@5	0.9675
dot_precision@1	0.7761
dot_precision@3	0.3112
dot_precision@5	0.1935
dot_precision@10	0.0985
dot_recall@1	0.7761
dot_recall@3	0.9337
dot_recall@5	0.9675
dot_recall@10	0.9853
dot_ndcg@10	0.8899
dot_mrr@10	0.8582
dot_map@100	0.859

Information Retrieval

• Dataset: test
• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.9896
cosine_accuracy@3	1.0
cosine_accuracy@5	1.0
cosine_precision@1	0.9896
cosine_precision@3	0.3333
cosine_precision@5	0.2
cosine_precision@10	0.1
cosine_recall@1	0.9896
cosine_recall@3	1.0
cosine_recall@5	1.0
cosine_recall@10	1.0
cosine_ndcg@10	0.9962
cosine_mrr@10	0.9948
cosine_map@100	0.9948
dot_accuracy@1	0.9896
dot_accuracy@3	1.0
dot_accuracy@5	1.0
dot_precision@1	0.9896
dot_precision@3	0.3333
dot_precision@5	0.2
dot_precision@10	0.1
dot_recall@1	0.9896
dot_recall@3	1.0
dot_recall@5	1.0
dot_recall@10	1.0
dot_ndcg@10	0.9962
dot_mrr@10	0.9948
dot_map@100	0.9948

Training Details

Training Dataset

Unnamed Dataset

• Size: 1,630 training samples
• Columns: sentence_0 and sentence_1
• Approximate statistics based on the first 1000 samples:

  	sentence_0	sentence_1
  type	string	string
  details	min: 3 tokens mean: 12.4 tokens max: 74 tokens	min: 7 tokens mean: 61.91 tokens max: 371 tokens

• Samples:

  sentence_0	sentence_1
  Не отображается вкладка премия в ЛК. В консультации написали, что личный кабинет не передан на обслуживание в сервисную функцию HR Поддержку X5	Создайте, пожалуйста, обращение в ИТ поддержку на портале support
  как пересмотреть зарплату?	По данному вопросу Вы можете обратиться в кадровую службу, создав заявку "Консультация по HR вопросам"
  поменять телефон сотруднику	Кнопка "изменить номер" телефона находится в личном разделе в ЛК. Если доступа к ЛК нет, для смены номера телефона, обратитесь в поддержку

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 512
• per_device_eval_batch_size: 512
• num_train_epochs: 1200
• multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 512
• per_device_eval_batch_size: 512
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 1
• eval_accumulation_steps: None
• torch_empty_cache_steps: None
• learning_rate: 5e-05
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1
• num_train_epochs: 1200
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.0
• warmup_steps: 0
• log_level: passive
• log_level_replica: warning
• log_on_each_node: True
• logging_nan_inf_filter: True
• save_safetensors: True
• save_on_each_node: False
• save_only_model: False
• restore_callback_states_from_checkpoint: False
• no_cuda: False
• use_cpu: False
• use_mps_device: False
• seed: 42
• data_seed: None
• jit_mode_eval: False
• use_ipex: False
• bf16: False
• fp16: False
• fp16_opt_level: O1
• half_precision_backend: auto
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• local_rank: 0
• ddp_backend: None
• tpu_num_cores: None
• tpu_metrics_debug: False
• debug: []
• dataloader_drop_last: False
• dataloader_num_workers: 0
• dataloader_prefetch_factor: None
• past_index: -1
• disable_tqdm: False
• remove_unused_columns: True
• label_names: None
• load_best_model_at_end: False
• ignore_data_skip: False
• fsdp: []
• fsdp_min_num_params: 0
• fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
• fsdp_transformer_layer_cls_to_wrap: None
• accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
• deepspeed: None
• label_smoothing_factor: 0.0
• optim: adamw_torch
• optim_args: None
• adafactor: False
• group_by_length: False
• length_column_name: length
• ddp_find_unused_parameters: None
• ddp_bucket_cap_mb: None
• ddp_broadcast_buffers: False
• dataloader_pin_memory: True
• dataloader_persistent_workers: False
• skip_memory_metrics: True
• use_legacy_prediction_loop: False
• push_to_hub: False
• resume_from_checkpoint: None
• hub_model_id: None
• hub_strategy: every_save
• hub_private_repo: False
• hub_always_push: False
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• include_inputs_for_metrics: False
• eval_do_concat_batches: True
• fp16_backend: auto
• push_to_hub_model_id: None
• push_to_hub_organization: None
• mp_parameters:
• auto_find_batch_size: False
• full_determinism: False
• torchdynamo: None
• ray_scope: last
• ddp_timeout: 1800
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• dispatch_batches: None
• split_batches: None
• include_tokens_per_second: False
• include_num_input_tokens_seen: False
• neftune_noise_alpha: None
• optim_target_modules: None
• batch_eval_metrics: False
• eval_on_start: False
• eval_use_gather_object: False
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: round_robin

Training Logs

Click to expand

Epoch	Step	Training Loss	test_cosine_map@100
1.0	4	-	0.1922
2.0	8	-	0.1922
3.0	12	-	0.1925
4.0	16	-	0.1927
5.0	20	-	0.1929
6.0	24	-	0.1931
7.0	28	-	0.1934
8.0	32	-	0.1942
9.0	36	-	0.1951
10.0	40	-	0.1960
11.0	44	-	0.1977
12.0	48	-	0.1993
13.0	52	-	0.2011
14.0	56	-	0.2024
15.0	60	-	0.2042
16.0	64	-	0.2047
17.0	68	-	0.2064
18.0	72	-	0.2081
19.0	76	-	0.2106
20.0	80	-	0.2123
21.0	84	-	0.2132
22.0	88	-	0.2148
23.0	92	-	0.2173
24.0	96	-	0.2200
25.0	100	-	0.2216
26.0	104	-	0.2241
27.0	108	-	0.2262
28.0	112	-	0.2286
29.0	116	-	0.2317
30.0	120	-	0.2339
31.0	124	-	0.2353
32.0	128	-	0.2392
33.0	132	-	0.2421
34.0	136	-	0.2442
35.0	140	-	0.2469
36.0	144	-	0.2501
37.0	148	-	0.2543
38.0	152	-	0.2565
39.0	156	-	0.2603
40.0	160	-	0.2643
41.0	164	-	0.2668
42.0	168	-	0.2688
43.0	172	-	0.2711
44.0	176	-	0.2743
45.0	180	-	0.2767
46.0	184	-	0.2810
47.0	188	-	0.2838
48.0	192	-	0.2869
49.0	196	-	0.2896
50.0	200	-	0.2940
51.0	204	-	0.2972
52.0	208	-	0.3012
53.0	212	-	0.3053
54.0	216	-	0.3072
55.0	220	-	0.3097
56.0	224	-	0.3133
57.0	228	-	0.3171
58.0	232	-	0.3220
59.0	236	-	0.3249
60.0	240	-	0.3274
61.0	244	-	0.3304
62.0	248	-	0.3336
63.0	252	-	0.3357
64.0	256	-	0.3398
65.0	260	-	0.3438
66.0	264	-	0.3463
67.0	268	-	0.3498
68.0	272	-	0.3535
69.0	276	-	0.3580
70.0	280	-	0.3606
71.0	284	-	0.3634
72.0	288	-	0.3654
73.0	292	-	0.3679
74.0	296	-	0.3723
75.0	300	-	0.3750
76.0	304	-	0.3781
77.0	308	-	0.3810
78.0	312	-	0.3840
79.0	316	-	0.3871
80.0	320	-	0.3914
81.0	324	-	0.3958
82.0	328	-	0.3991
83.0	332	-	0.4025
84.0	336	-	0.4053
85.0	340	-	0.4091
86.0	344	-	0.4125
87.0	348	-	0.4148
88.0	352	-	0.4176
89.0	356	-	0.4212
90.0	360	-	0.4240
91.0	364	-	0.4277
92.0	368	-	0.4315
93.0	372	-	0.4338
94.0	376	-	0.4363
95.0	380	-	0.4392
96.0	384	-	0.4423
97.0	388	-	0.4460
98.0	392	-	0.4487
99.0	396	-	0.4526
100.0	400	-	0.4563
101.0	404	-	0.4597
102.0	408	-	0.4644
103.0	412	-	0.4678
104.0	416	-	0.4707
105.0	420	-	0.4750
106.0	424	-	0.4791
107.0	428	-	0.4820
108.0	432	-	0.4847
109.0	436	-	0.4895
110.0	440	-	0.4916
111.0	444	-	0.4961
112.0	448	-	0.4990
113.0	452	-	0.5032
114.0	456	-	0.5065
115.0	460	-	0.5093
116.0	464	-	0.5135
117.0	468	-	0.5175
118.0	472	-	0.5199
119.0	476	-	0.5243
120.0	480	-	0.5266
121.0	484	-	0.5297
122.0	488	-	0.5324
123.0	492	-	0.5353
124.0	496	-	0.5378
125.0	500	4.6316	0.5404
126.0	504	-	0.5449
127.0	508	-	0.5473
128.0	512	-	0.5500
129.0	516	-	0.5535
130.0	520	-	0.5553
131.0	524	-	0.5570
132.0	528	-	0.5597
133.0	532	-	0.5625
134.0	536	-	0.5660
135.0	540	-	0.5693
136.0	544	-	0.5713
137.0	548	-	0.5745
138.0	552	-	0.5767
139.0	556	-	0.5798
140.0	560	-	0.5835
141.0	564	-	0.5853
142.0	568	-	0.5863
143.0	572	-	0.5909
144.0	576	-	0.5933
145.0	580	-	0.5970
146.0	584	-	0.5995
147.0	588	-	0.6008
148.0	592	-	0.6040
149.0	596	-	0.6073
150.0	600	-	0.6097
151.0	604	-	0.6121
152.0	608	-	0.6163
153.0	612	-	0.6178
154.0	616	-	0.6205
155.0	620	-	0.6223
156.0	624	-	0.6242
157.0	628	-	0.6265
158.0	632	-	0.6290
159.0	636	-	0.6326
160.0	640	-	0.6347
161.0	644	-	0.6370
162.0	648	-	0.6400
163.0	652	-	0.6422
164.0	656	-	0.6436
165.0	660	-	0.6460
166.0	664	-	0.6473
167.0	668	-	0.6497
168.0	672	-	0.6515
169.0	676	-	0.6545
170.0	680	-	0.6574
171.0	684	-	0.6595
172.0	688	-	0.6616
173.0	692	-	0.6639
174.0	696	-	0.6658
175.0	700	-	0.6676
176.0	704	-	0.6697
177.0	708	-	0.6713
178.0	712	-	0.6746
179.0	716	-	0.6765
180.0	720	-	0.6784
181.0	724	-	0.6806
182.0	728	-	0.6820
183.0	732	-	0.6838
184.0	736	-	0.6867
185.0	740	-	0.6882
186.0	744	-	0.6913
187.0	748	-	0.6930
188.0	752	-	0.6943
189.0	756	-	0.6979
190.0	760	-	0.6982
191.0	764	-	0.7008
192.0	768	-	0.7038
193.0	772	-	0.7059
194.0	776	-	0.7062
195.0	780	-	0.7083
196.0	784	-	0.7112
197.0	788	-	0.7139
198.0	792	-	0.7163
199.0	796	-	0.7181
200.0	800	-	0.7188
201.0	804	-	0.7208
202.0	808	-	0.7223
203.0	812	-	0.7245
204.0	816	-	0.7264
205.0	820	-	0.7296
206.0	824	-	0.7325
207.0	828	-	0.7340
208.0	832	-	0.7362
209.0	836	-	0.7373
210.0	840	-	0.7394
211.0	844	-	0.7416
212.0	848	-	0.7420
213.0	852	-	0.7434
214.0	856	-	0.7444
215.0	860	-	0.7466
216.0	864	-	0.7479
217.0	868	-	0.7523
218.0	872	-	0.7540
219.0	876	-	0.7553
220.0	880	-	0.7558
221.0	884	-	0.7586
222.0	888	-	0.7596
223.0	892	-	0.7613
224.0	896	-	0.7637
225.0	900	-	0.7652
226.0	904	-	0.7667
227.0	908	-	0.7682
228.0	912	-	0.7698
229.0	916	-	0.7715
230.0	920	-	0.7729
231.0	924	-	0.7757
232.0	928	-	0.7767
233.0	932	-	0.7783
234.0	936	-	0.7802
235.0	940	-	0.7814
236.0	944	-	0.7835
237.0	948	-	0.7859
238.0	952	-	0.7874
239.0	956	-	0.7887
240.0	960	-	0.7903
241.0	964	-	0.7927
242.0	968	-	0.7940
243.0	972	-	0.7958
244.0	976	-	0.7973
245.0	980	-	0.7991
246.0	984	-	0.8009
247.0	988	-	0.8021
248.0	992	-	0.8035
249.0	996	-	0.8043
250.0	1000	3.2323	0.8057
251.0	1004	-	0.8071
252.0	1008	-	0.8088
253.0	1012	-	0.8104
254.0	1016	-	0.8112
255.0	1020	-	0.8123
256.0	1024	-	0.8135
257.0	1028	-	0.8154
258.0	1032	-	0.8167
259.0	1036	-	0.8174
260.0	1040	-	0.8187
261.0	1044	-	0.8187
262.0	1048	-	0.8210
263.0	1052	-	0.8216
264.0	1056	-	0.8242
265.0	1060	-	0.8260
266.0	1064	-	0.8267
267.0	1068	-	0.8278
268.0	1072	-	0.8294
269.0	1076	-	0.8309
270.0	1080	-	0.8319
271.0	1084	-	0.8325
272.0	1088	-	0.8346
273.0	1092	-	0.8353
274.0	1096	-	0.8362
275.0	1100	-	0.8373
276.0	1104	-	0.8385
277.0	1108	-	0.8392
278.0	1112	-	0.8405
279.0	1116	-	0.8431
280.0	1120	-	0.8453
281.0	1124	-	0.8464
282.0	1128	-	0.8480
283.0	1132	-	0.8476
284.0	1136	-	0.8491
285.0	1140	-	0.8509
286.0	1144	-	0.8508
287.0	1148	-	0.8513
288.0	1152	-	0.8525
289.0	1156	-	0.8534
290.0	1160	-	0.8543
291.0	1164	-	0.8554
292.0	1168	-	0.8572
293.0	1172	-	0.8590
1.0	4	-	0.8591
2.0	8	-	0.8591
3.0	12	-	0.8591
4.0	16	-	0.8591
5.0	20	-	0.8591
6.0	24	-	0.8591
7.0	28	-	0.8592
8.0	32	-	0.8592
9.0	36	-	0.8589
10.0	40	-	0.8593
11.0	44	-	0.8587
12.0	48	-	0.8590
13.0	52	-	0.8591
14.0	56	-	0.8591
15.0	60	-	0.8593
16.0	64	-	0.8593
17.0	68	-	0.8595
18.0	72	-	0.8598
19.0	76	-	0.8602
20.0	80	-	0.8606
21.0	84	-	0.8614
22.0	88	-	0.8617
23.0	92	-	0.8617
24.0	96	-	0.8621
25.0	100	-	0.8622
26.0	104	-	0.8626
27.0	108	-	0.8626
28.0	112	-	0.8626
29.0	116	-	0.8629
30.0	120	-	0.8629
31.0	124	-	0.8629
32.0	128	-	0.8629
33.0	132	-	0.8628
34.0	136	-	0.8625
35.0	140	-	0.8626
36.0	144	-	0.8628
37.0	148	-	0.8628
38.0	152	-	0.8630
39.0	156	-	0.8635
40.0	160	-	0.8635
41.0	164	-	0.8642
42.0	168	-	0.8646
43.0	172	-	0.8649
44.0	176	-	0.8654
45.0	180	-	0.8658
46.0	184	-	0.8662
47.0	188	-	0.8666
48.0	192	-	0.8676
49.0	196	-	0.8676
50.0	200	-	0.8677
51.0	204	-	0.8681
52.0	208	-	0.8680
53.0	212	-	0.8677
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125.0	500	2.6383	0.8909
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243.0	972	-	0.9429
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249.0	996	-	0.9452
250.0	1000	2.507	0.9461
251.0	1004	-	0.9464
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375.0	1500	2.391	0.9755
376.0	1504	-	0.9757
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395.0	1580	-	0.9783
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404.0	1616	-	0.9788
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416.0	1664	-	0.9797
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418.0	1672	-	0.9800
419.0	1676	-	0.9803
420.0	1680	-	0.9809
421.0	1684	-	0.9806
422.0	1688	-	0.9809
423.0	1692	-	0.9812
424.0	1696	-	0.9810
425.0	1700	-	0.9806
426.0	1704	-	0.9806
427.0	1708	-	0.9799
428.0	1712	-	0.9796
429.0	1716	-	0.9802
430.0	1720	-	0.9802
431.0	1724	-	0.9810
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433.0	1732	-	0.9807
434.0	1736	-	0.9810
435.0	1740	-	0.9813
436.0	1744	-	0.9816
437.0	1748	-	0.9820
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440.0	1760	-	0.9813
441.0	1764	-	0.9820
442.0	1768	-	0.9823
443.0	1772	-	0.9820
444.0	1776	-	0.9823
445.0	1780	-	0.9826
446.0	1784	-	0.9823
447.0	1788	-	0.9832
448.0	1792	-	0.9832
449.0	1796	-	0.9832
450.0	1800	-	0.9835
451.0	1804	-	0.9835
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453.0	1812	-	0.9835
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456.0	1824	-	0.9838
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458.0	1832	-	0.9841
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464.0	1856	-	0.9844
465.0	1860	-	0.9841
466.0	1864	-	0.9844
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468.0	1872	-	0.9853
469.0	1876	-	0.9850
470.0	1880	-	0.9856
471.0	1884	-	0.9856
472.0	1888	-	0.9856
473.0	1892	-	0.9853
474.0	1896	-	0.9856
475.0	1900	-	0.9850
476.0	1904	-	0.9850
477.0	1908	-	0.9850
478.0	1912	-	0.9850
479.0	1916	-	0.9853
480.0	1920	-	0.9856
481.0	1924	-	0.9859
482.0	1928	-	0.9862
483.0	1932	-	0.9862
484.0	1936	-	0.9862
485.0	1940	-	0.9862
486.0	1944	-	0.9859
487.0	1948	-	0.9859
488.0	1952	-	0.9856
489.0	1956	-	0.9859
490.0	1960	-	0.9859
491.0	1964	-	0.9859
492.0	1968	-	0.9856
493.0	1972	-	0.9856
494.0	1976	-	0.9856
495.0	1980	-	0.9856
496.0	1984	-	0.9862
497.0	1988	-	0.9862
498.0	1992	-	0.9856
499.0	1996	-	0.9856
500.0	2000	2.3269	0.9856
501.0	2004	-	0.9856
502.0	2008	-	0.9856
503.0	2012	-	0.9859
504.0	2016	-	0.9862
505.0	2020	-	0.9866
506.0	2024	-	0.9866
507.0	2028	-	0.9869
508.0	2032	-	0.9869
509.0	2036	-	0.9869
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517.0	2068	-	0.9866
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537.0	2148	-	0.9878
538.0	2152	-	0.9872
539.0	2156	-	0.9869
540.0	2160	-	0.9872
541.0	2164	-	0.9875
542.0	2168	-	0.9878
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544.0	2176	-	0.9881
545.0	2180	-	0.9888
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579.0	2316	-	0.9897
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1137.0	4548	-	0.9948
1138.0	4552	-	0.9948
1139.0	4556	-	0.9948
1140.0	4560	-	0.9948
1141.0	4564	-	0.9948
1142.0	4568	-	0.9948
1143.0	4572	-	0.9948
1144.0	4576	-	0.9948
1145.0	4580	-	0.9948
1146.0	4584	-	0.9948
1147.0	4588	-	0.9948
1148.0	4592	-	0.9948
1149.0	4596	-	0.9948
1150.0	4600	-	0.9948
1151.0	4604	-	0.9948
1152.0	4608	-	0.9948
1153.0	4612	-	0.9948
1154.0	4616	-	0.9948
1155.0	4620	-	0.9948
1156.0	4624	-	0.9948
1157.0	4628	-	0.9948
1158.0	4632	-	0.9948
1159.0	4636	-	0.9948
1160.0	4640	-	0.9948
1161.0	4644	-	0.9948
1162.0	4648	-	0.9948
1163.0	4652	-	0.9948
1164.0	4656	-	0.9948
1165.0	4660	-	0.9948
1166.0	4664	-	0.9948
1167.0	4668	-	0.9948
1168.0	4672	-	0.9948
1169.0	4676	-	0.9948
1170.0	4680	-	0.9948
1171.0	4684	-	0.9948
1172.0	4688	-	0.9948
1173.0	4692	-	0.9948
1174.0	4696	-	0.9948
1175.0	4700	-	0.9948
1176.0	4704	-	0.9948
1177.0	4708	-	0.9948
1178.0	4712	-	0.9948
1179.0	4716	-	0.9948
1180.0	4720	-	0.9948
1181.0	4724	-	0.9948
1182.0	4728	-	0.9948
1183.0	4732	-	0.9948
1184.0	4736	-	0.9948
1185.0	4740	-	0.9948
1186.0	4744	-	0.9948
1187.0	4748	-	0.9948
1188.0	4752	-	0.9948
1189.0	4756	-	0.9948
1190.0	4760	-	0.9948
1191.0	4764	-	0.9948
1192.0	4768	-	0.9948
1193.0	4772	-	0.9948
1194.0	4776	-	0.9948
1195.0	4780	-	0.9948
1196.0	4784	-	0.9948
1197.0	4788	-	0.9948
1198.0	4792	-	0.9948
1199.0	4796	-	0.9948
1200.0	4800	-	0.9948

Framework Versions

• Python: 3.10.14
• Sentence Transformers: 3.0.1
• Transformers: 4.44.0
• PyTorch: 2.4.0
• Accelerate: 0.34.2
• Datasets: 2.21.0
• Tokenizers: 0.19.1

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

MultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply}, 
    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},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}