sobamchan/st5-base-mean-16000

SentenceTransformer based on google-t5/t5-base

This is a sentence-transformers model finetuned from google-t5/t5-base on the all-nli 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.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: google-t5/t5-base
• Maximum Sequence Length: 256 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • all-nli
• Language: en

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': 256, 'do_lower_case': False}) with Transformer model: T5EncoderModel 
  (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})
  (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("sentence_transformers_model_id")
# Run inference
sentences = [
    'A construction worker peeking out of a manhole while his coworker sits on the sidewalk smiling.',
    'A worker is looking out of a manhole.',
    'The workers are both inside the manhole.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

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

Training Details

Training Dataset

all-nli

• Dataset: all-nli at d482672
• Size: 557,850 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 6 tokens mean: 9.96 tokens max: 52 tokens	min: 5 tokens mean: 12.79 tokens max: 44 tokens	min: 4 tokens mean: 14.02 tokens max: 57 tokens

• Samples:

  anchor	positive	negative
  A person on a horse jumps over a broken down airplane.	A person is outdoors, on a horse.	A person is at a diner, ordering an omelette.
  Children smiling and waving at camera	There are children present	The kids are frowning
  A boy is jumping on skateboard in the middle of a red bridge.	The boy does a skateboarding trick.	The boy skates down the sidewalk.

• Loss: MultipleNegativesRankingLoss with these parameters:

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

Evaluation Dataset

all-nli

• Dataset: all-nli at d482672
• Size: 6,584 evaluation samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 5 tokens mean: 19.41 tokens max: 79 tokens	min: 4 tokens mean: 9.69 tokens max: 35 tokens	min: 4 tokens mean: 10.35 tokens max: 30 tokens

• Samples:

  anchor	positive	negative
  Two women are embracing while holding to go packages.	Two woman are holding packages.	The men are fighting outside a deli.
  Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.	Two kids in numbered jerseys wash their hands.	Two kids in jackets walk to school.
  A man selling donuts to a customer during a world exhibition event held in the city of Angeles	A man selling donuts to a customer.	A woman drinks her coffee in a small cafe.

• 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: 64
• per_device_eval_batch_size: 64
• learning_rate: 1e-05
• warmup_ratio: 0.1
• batch_sampler: no_duplicates

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 64
• per_device_eval_batch_size: 64
• 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: 1e-05
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1.0
• num_train_epochs: 3
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.1
• 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: None
• hub_always_push: False
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• include_inputs_for_metrics: False
• include_for_metrics: []
• 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
• use_liger_kernel: False
• eval_use_gather_object: False
• average_tokens_across_devices: False
• prompts: None
• batch_sampler: no_duplicates
• multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss	Validation Loss
0.0011	10	-	1.8733
0.0023	20	-	1.8726
0.0034	30	-	1.8714
0.0046	40	-	1.8697
0.0057	50	-	1.8675
0.0069	60	-	1.8649
0.0080	70	-	1.8619
0.0092	80	-	1.8584
0.0103	90	-	1.8544
0.0115	100	3.1046	1.8499
0.0126	110	-	1.8451
0.0138	120	-	1.8399
0.0149	130	-	1.8343
0.0161	140	-	1.8283
0.0172	150	-	1.8223
0.0184	160	-	1.8159
0.0195	170	-	1.8091
0.0206	180	-	1.8016
0.0218	190	-	1.7938
0.0229	200	3.0303	1.7858
0.0241	210	-	1.7775
0.0252	220	-	1.7693
0.0264	230	-	1.7605
0.0275	240	-	1.7514
0.0287	250	-	1.7417
0.0298	260	-	1.7320
0.0310	270	-	1.7227
0.0321	280	-	1.7134
0.0333	290	-	1.7040
0.0344	300	2.9459	1.6941
0.0356	310	-	1.6833
0.0367	320	-	1.6725
0.0379	330	-	1.6614
0.0390	340	-	1.6510
0.0402	350	-	1.6402
0.0413	360	-	1.6296
0.0424	370	-	1.6187
0.0436	380	-	1.6073
0.0447	390	-	1.5962
0.0459	400	2.7813	1.5848
0.0470	410	-	1.5735
0.0482	420	-	1.5620
0.0493	430	-	1.5495
0.0505	440	-	1.5375
0.0516	450	-	1.5256
0.0528	460	-	1.5133
0.0539	470	-	1.5012
0.0551	480	-	1.4892
0.0562	490	-	1.4769
0.0574	500	2.6308	1.4640
0.0585	510	-	1.4513
0.0597	520	-	1.4391
0.0608	530	-	1.4262
0.0619	540	-	1.4130
0.0631	550	-	1.3998
0.0642	560	-	1.3874
0.0654	570	-	1.3752
0.0665	580	-	1.3620
0.0677	590	-	1.3485
0.0688	600	2.4452	1.3350
0.0700	610	-	1.3213
0.0711	620	-	1.3088
0.0723	630	-	1.2965
0.0734	640	-	1.2839
0.0746	650	-	1.2713
0.0757	660	-	1.2592
0.0769	670	-	1.2466
0.0780	680	-	1.2332
0.0792	690	-	1.2203
0.0803	700	2.2626	1.2077
0.0815	710	-	1.1959
0.0826	720	-	1.1841
0.0837	730	-	1.1725
0.0849	740	-	1.1619
0.0860	750	-	1.1516
0.0872	760	-	1.1416
0.0883	770	-	1.1320
0.0895	780	-	1.1227
0.0906	790	-	1.1138
0.0918	800	2.0044	1.1053
0.0929	810	-	1.0965
0.0941	820	-	1.0879
0.0952	830	-	1.0796
0.0964	840	-	1.0718
0.0975	850	-	1.0644
0.0987	860	-	1.0564
0.0998	870	-	1.0490
0.1010	880	-	1.0417
0.1021	890	-	1.0354
0.1032	900	1.8763	1.0296
0.1044	910	-	1.0239
0.1055	920	-	1.0180
0.1067	930	-	1.0123
0.1078	940	-	1.0065
0.1090	950	-	1.0008
0.1101	960	-	0.9950
0.1113	970	-	0.9894
0.1124	980	-	0.9840
0.1136	990	-	0.9793
0.1147	1000	1.7287	0.9752
0.1159	1010	-	0.9706
0.1170	1020	-	0.9659
0.1182	1030	-	0.9615
0.1193	1040	-	0.9572
0.1205	1050	-	0.9531
0.1216	1060	-	0.9494
0.1227	1070	-	0.9456
0.1239	1080	-	0.9415
0.1250	1090	-	0.9377
0.1262	1100	1.6312	0.9339
0.1273	1110	-	0.9303
0.1285	1120	-	0.9267
0.1296	1130	-	0.9232
0.1308	1140	-	0.9197
0.1319	1150	-	0.9162
0.1331	1160	-	0.9128
0.1342	1170	-	0.9097
0.1354	1180	-	0.9069
0.1365	1190	-	0.9040
0.1377	1200	1.5316	0.9010
0.1388	1210	-	0.8979
0.1400	1220	-	0.8947
0.1411	1230	-	0.8915
0.1423	1240	-	0.8888
0.1434	1250	-	0.8861
0.1445	1260	-	0.8833
0.1457	1270	-	0.8806
0.1468	1280	-	0.8779
0.1480	1290	-	0.8748
0.1491	1300	1.4961	0.8718
0.1503	1310	-	0.8690
0.1514	1320	-	0.8664
0.1526	1330	-	0.8635
0.1537	1340	-	0.8603
0.1549	1350	-	0.8574
0.1560	1360	-	0.8545
0.1572	1370	-	0.8521
0.1583	1380	-	0.8497
0.1595	1390	-	0.8474
0.1606	1400	1.451	0.8453
0.1618	1410	-	0.8429
0.1629	1420	-	0.8404
0.1640	1430	-	0.8380
0.1652	1440	-	0.8357
0.1663	1450	-	0.8336
0.1675	1460	-	0.8312
0.1686	1470	-	0.8289
0.1698	1480	-	0.8262
0.1709	1490	-	0.8236
0.1721	1500	1.4177	0.8213
0.1732	1510	-	0.8189
0.1744	1520	-	0.8168
0.1755	1530	-	0.8147
0.1767	1540	-	0.8127
0.1778	1550	-	0.8107
0.1790	1560	-	0.8082
0.1801	1570	-	0.8059
0.1813	1580	-	0.8036
0.1824	1590	-	0.8015
0.1835	1600	1.3734	0.7993
0.1847	1610	-	0.7970
0.1858	1620	-	0.7948
0.1870	1630	-	0.7922
0.1881	1640	-	0.7900
0.1893	1650	-	0.7877
0.1904	1660	-	0.7852
0.1916	1670	-	0.7829
0.1927	1680	-	0.7804
0.1939	1690	-	0.7779
0.1950	1700	1.3327	0.7757
0.1962	1710	-	0.7738
0.1973	1720	-	0.7719
0.1985	1730	-	0.7700
0.1996	1740	-	0.7679
0.2008	1750	-	0.7658
0.2019	1760	-	0.7641
0.2031	1770	-	0.7621
0.2042	1780	-	0.7601
0.2053	1790	-	0.7580
0.2065	1800	1.2804	0.7558
0.2076	1810	-	0.7536
0.2088	1820	-	0.7514
0.2099	1830	-	0.7493
0.2111	1840	-	0.7473
0.2122	1850	-	0.7451
0.2134	1860	-	0.7429
0.2145	1870	-	0.7408
0.2157	1880	-	0.7389
0.2168	1890	-	0.7368
0.2180	1900	1.2255	0.7349
0.2191	1910	-	0.7328
0.2203	1920	-	0.7310
0.2214	1930	-	0.7293
0.2226	1940	-	0.7277
0.2237	1950	-	0.7259
0.2248	1960	-	0.7240
0.2260	1970	-	0.7221
0.2271	1980	-	0.7203
0.2283	1990	-	0.7184
0.2294	2000	1.2635	0.7165
0.2306	2010	-	0.7150
0.2317	2020	-	0.7135
0.2329	2030	-	0.7117
0.2340	2040	-	0.7099
0.2352	2050	-	0.7084
0.2363	2060	-	0.7068
0.2375	2070	-	0.7054
0.2386	2080	-	0.7037
0.2398	2090	-	0.7023
0.2409	2100	1.1912	0.7009
0.2421	2110	-	0.6991
0.2432	2120	-	0.6974
0.2444	2130	-	0.6962
0.2455	2140	-	0.6950
0.2466	2150	-	0.6938
0.2478	2160	-	0.6922
0.2489	2170	-	0.6909
0.2501	2180	-	0.6897
0.2512	2190	-	0.6884
0.2524	2200	1.2144	0.6868
0.2535	2210	-	0.6856
0.2547	2220	-	0.6843
0.2558	2230	-	0.6829
0.2570	2240	-	0.6817
0.2581	2250	-	0.6804
0.2593	2260	-	0.6789
0.2604	2270	-	0.6775
0.2616	2280	-	0.6763
0.2627	2290	-	0.6751
0.2639	2300	1.1498	0.6739
0.2650	2310	-	0.6725
0.2661	2320	-	0.6711
0.2673	2330	-	0.6698
0.2684	2340	-	0.6684
0.2696	2350	-	0.6666
0.2707	2360	-	0.6653
0.2719	2370	-	0.6638
0.2730	2380	-	0.6621
0.2742	2390	-	0.6609
0.2753	2400	1.1446	0.6596
0.2765	2410	-	0.6582
0.2776	2420	-	0.6568
0.2788	2430	-	0.6553
0.2799	2440	-	0.6541
0.2811	2450	-	0.6527
0.2822	2460	-	0.6513
0.2834	2470	-	0.6496
0.2845	2480	-	0.6483
0.2856	2490	-	0.6475
0.2868	2500	1.1309	0.6465
0.2879	2510	-	0.6455
0.2891	2520	-	0.6447
0.2902	2530	-	0.6437
0.2914	2540	-	0.6428
0.2925	2550	-	0.6415
0.2937	2560	-	0.6403
0.2948	2570	-	0.6392
0.2960	2580	-	0.6381
0.2971	2590	-	0.6371
0.2983	2600	1.1006	0.6358
0.2994	2610	-	0.6348
0.3006	2620	-	0.6340
0.3017	2630	-	0.6330
0.3029	2640	-	0.6319
0.3040	2650	-	0.6308
0.3052	2660	-	0.6300
0.3063	2670	-	0.6291
0.3074	2680	-	0.6280
0.3086	2690	-	0.6268
0.3097	2700	1.0772	0.6254
0.3109	2710	-	0.6243
0.3120	2720	-	0.6232
0.3132	2730	-	0.6224
0.3143	2740	-	0.6215
0.3155	2750	-	0.6205
0.3166	2760	-	0.6194
0.3178	2770	-	0.6183
0.3189	2780	-	0.6171
0.3201	2790	-	0.6160
0.3212	2800	1.0648	0.6153
0.3224	2810	-	0.6141
0.3235	2820	-	0.6129
0.3247	2830	-	0.6119
0.3258	2840	-	0.6109
0.3269	2850	-	0.6099
0.3281	2860	-	0.6088
0.3292	2870	-	0.6079
0.3304	2880	-	0.6073
0.3315	2890	-	0.6063
0.3327	2900	1.0398	0.6054
0.3338	2910	-	0.6044
0.3350	2920	-	0.6033
0.3361	2930	-	0.6022
0.3373	2940	-	0.6012
0.3384	2950	-	0.6003
0.3396	2960	-	0.5993
0.3407	2970	-	0.5986
0.3419	2980	-	0.5978
0.3430	2990	-	0.5967
0.3442	3000	1.0256	0.5959
0.3453	3010	-	0.5947
0.3464	3020	-	0.5937
0.3476	3030	-	0.5929
0.3487	3040	-	0.5920
0.3499	3050	-	0.5908
0.3510	3060	-	0.5897
0.3522	3070	-	0.5888
0.3533	3080	-	0.5882
0.3545	3090	-	0.5874
0.3556	3100	1.0489	0.5868
0.3568	3110	-	0.5860
0.3579	3120	-	0.5854
0.3591	3130	-	0.5839
0.3602	3140	-	0.5830
0.3614	3150	-	0.5822
0.3625	3160	-	0.5814
0.3637	3170	-	0.5808
0.3648	3180	-	0.5802
0.3660	3190	-	0.5794
0.3671	3200	1.038	0.5788
0.3682	3210	-	0.5778
0.3694	3220	-	0.5770
0.3705	3230	-	0.5763
0.3717	3240	-	0.5752
0.3728	3250	-	0.5745
0.3740	3260	-	0.5737
0.3751	3270	-	0.5728
0.3763	3280	-	0.5720
0.3774	3290	-	0.5713
0.3786	3300	1.0058	0.5707
0.3797	3310	-	0.5700
0.3809	3320	-	0.5690
0.3820	3330	-	0.5681
0.3832	3340	-	0.5673
0.3843	3350	-	0.5669
0.3855	3360	-	0.5667
0.3866	3370	-	0.5665
0.3877	3380	-	0.5659
0.3889	3390	-	0.5650
0.3900	3400	1.0413	0.5645
0.3912	3410	-	0.5641
0.3923	3420	-	0.5635
0.3935	3430	-	0.5629
0.3946	3440	-	0.5622
0.3958	3450	-	0.5617
0.3969	3460	-	0.5614
0.3981	3470	-	0.5607
0.3992	3480	-	0.5603
0.4004	3490	-	0.5598
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0.8925	7780	-	0.4324
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0.9177	8000	0.7828	0.4289
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0.9705	8460	-	0.4237
0.9717	8470	-	0.4236
0.9728	8480	-	0.4232
0.9740	8490	-	0.4229
0.9751	8500	0.7416	0.4227
0.9763	8510	-	0.4226
0.9774	8520	-	0.4220
0.9785	8530	-	0.4218
0.9797	8540	-	0.4217
0.9808	8550	-	0.4217
0.9820	8560	-	0.4215
0.9831	8570	-	0.4216
0.9843	8580	-	0.4217
0.9854	8590	-	0.4216
0.9866	8600	0.748	0.4217
0.9877	8610	-	0.4215
0.9889	8620	-	0.4216
0.9900	8630	-	0.4218
0.9912	8640	-	0.4218
0.9923	8650	-	0.4219
0.9935	8660	-	0.4217
0.9946	8670	-	0.4217
0.9958	8680	-	0.4214
0.9969	8690	-	0.4210
0.9980	8700	0.7553	0.4205
0.9992	8710	-	0.4200
1.0003	8720	-	0.4199
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1.0038	8750	-	0.4198
1.0049	8760	-	0.4200
1.0061	8770	-	0.4198
1.0072	8780	-	0.4195
1.0084	8790	-	0.4194
1.0095	8800	0.7202	0.4191
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1.0118	8820	-	0.4188
1.0130	8830	-	0.4188
1.0141	8840	-	0.4192
1.0153	8850	-	0.4190
1.0164	8860	-	0.4191
1.0176	8870	-	0.4190
1.0187	8880	-	0.4192
1.0198	8890	-	0.4190
1.0210	8900	0.7567	0.4189
1.0221	8910	-	0.4188
1.0233	8920	-	0.4189
1.0244	8930	-	0.4188
1.0256	8940	-	0.4187
1.0267	8950	-	0.4183
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1.0290	8970	-	0.4182
1.0302	8980	-	0.4184
1.0313	8990	-	0.4181
1.0325	9000	0.7345	0.4177
1.0336	9010	-	0.4173
1.0348	9020	-	0.4171
1.0359	9030	-	0.4172
1.0371	9040	-	0.4171
1.0382	9050	-	0.4172
1.0393	9060	-	0.4172
1.0405	9070	-	0.4170
1.0416	9080	-	0.4165
1.0428	9090	-	0.4162
1.0439	9100	0.7344	0.4162
1.0451	9110	-	0.4160
1.0462	9120	-	0.4158
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1.0485	9140	-	0.4157
1.0497	9150	-	0.4156
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1.0520	9170	-	0.4153
1.0531	9180	-	0.4154
1.0543	9190	-	0.4154
1.0554	9200	0.7233	0.4157
1.0566	9210	-	0.4157
1.0577	9220	-	0.4156
1.0589	9230	-	0.4155
1.0600	9240	-	0.4153
1.0611	9250	-	0.4154
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1.0634	9270	-	0.4154
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1.0669	9300	0.7442	0.4148
1.0680	9310	-	0.4144
1.0692	9320	-	0.4143
1.0703	9330	-	0.4141
1.0715	9340	-	0.4140
1.0726	9350	-	0.4138
1.0738	9360	-	0.4136
1.0749	9370	-	0.4133
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1.0772	9390	-	0.4130
1.0784	9400	0.722	0.4129
1.0795	9410	-	0.4131
1.0806	9420	-	0.4132
1.0818	9430	-	0.4133
1.0829	9440	-	0.4134
1.0841	9450	-	0.4134
1.0852	9460	-	0.4133
1.0864	9470	-	0.4132
1.0875	9480	-	0.4132
1.0887	9490	-	0.4134
1.0898	9500	0.7433	0.4133
1.0910	9510	-	0.4133
1.0921	9520	-	0.4133
1.0933	9530	-	0.4132
1.0944	9540	-	0.4131
1.0956	9550	-	0.4130
1.0967	9560	-	0.4130
1.0979	9570	-	0.4126
1.0990	9580	-	0.4125
1.1001	9590	-	0.4121
1.1013	9600	0.746	0.4119
1.1024	9610	-	0.4117
1.1036	9620	-	0.4112
1.1047	9630	-	0.4109
1.1059	9640	-	0.4106
1.1070	9650	-	0.4101
1.1082	9660	-	0.4101
1.1093	9670	-	0.4102
1.1105	9680	-	0.4102
1.1116	9690	-	0.4101
1.1128	9700	0.7447	0.4099
1.1139	9710	-	0.4100
1.1151	9720	-	0.4098
1.1162	9730	-	0.4097
1.1174	9740	-	0.4094
1.1185	9750	-	0.4097
1.1197	9760	-	0.4096
1.1208	9770	-	0.4096
1.1219	9780	-	0.4097
1.1231	9790	-	0.4097
1.1242	9800	0.7234	0.4094
1.1254	9810	-	0.4090
1.1265	9820	-	0.4090
1.1277	9830	-	0.4091
1.1288	9840	-	0.4091
1.1300	9850	-	0.4090
1.1311	9860	-	0.4088
1.1323	9870	-	0.4088
1.1334	9880	-	0.4085
1.1346	9890	-	0.4085
1.1357	9900	0.7054	0.4084
1.1369	9910	-	0.4087
1.1380	9920	-	0.4089
1.1392	9930	-	0.4089
1.1403	9940	-	0.4088
1.1414	9950	-	0.4091
1.1426	9960	-	0.4088
1.1437	9970	-	0.4086
1.1449	9980	-	0.4084
1.1460	9990	-	0.4089
1.1472	10000	0.7071	0.4088
1.1483	10010	-	0.4086
1.1495	10020	-	0.4081
1.1506	10030	-	0.4079
1.1518	10040	-	0.4079
1.1529	10050	-	0.4081
1.1541	10060	-	0.4081
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1.1564	10080	-	0.4079
1.1575	10090	-	0.4078
1.1587	10100	0.7289	0.4075
1.1598	10110	-	0.4072
1.1609	10120	-	0.4070
1.1621	10130	-	0.4070
1.1632	10140	-	0.4074
1.1644	10150	-	0.4074
1.1655	10160	-	0.4073
1.1667	10170	-	0.4073
1.1678	10180	-	0.4072
1.1690	10190	-	0.4073
1.1701	10200	0.758	0.4071
1.1713	10210	-	0.4071
1.1724	10220	-	0.4071
1.1736	10230	-	0.4068
1.1747	10240	-	0.4063
1.1759	10250	-	0.4062
1.1770	10260	-	0.4064
1.1782	10270	-	0.4065
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1.1805	10290	-	0.4065
1.1816	10300	0.7322	0.4066
1.1827	10310	-	0.4065
1.1839	10320	-	0.4065
1.1850	10330	-	0.4061
1.1862	10340	-	0.4060
1.1873	10350	-	0.4057
1.1885	10360	-	0.4056
1.1896	10370	-	0.4056
1.1908	10380	-	0.4059
1.1919	10390	-	0.4061
1.1931	10400	0.6948	0.4059
1.1942	10410	-	0.4059
1.1954	10420	-	0.4060
1.1965	10430	-	0.4058
1.1977	10440	-	0.4057
1.1988	10450	-	0.4056
1.2000	10460	-	0.4056
1.2011	10470	-	0.4056
1.2022	10480	-	0.4057
1.2034	10490	-	0.4056
1.2045	10500	0.7185	0.4055
1.2057	10510	-	0.4056
1.2068	10520	-	0.4054
1.2080	10530	-	0.4053
1.2091	10540	-	0.4051
1.2103	10550	-	0.4050
1.2114	10560	-	0.4051
1.2126	10570	-	0.4052
1.2137	10580	-	0.4053
1.2149	10590	-	0.4053
1.2160	10600	0.7039	0.4053
1.2172	10610	-	0.4054
1.2183	10620	-	0.4051
1.2195	10630	-	0.4050
1.2206	10640	-	0.4048
1.2218	10650	-	0.4044
1.2229	10660	-	0.4046
1.2240	10670	-	0.4044
1.2252	10680	-	0.4041
1.2263	10690	-	0.4039
1.2275	10700	0.6969	0.4037
1.2286	10710	-	0.4037
1.2298	10720	-	0.4035
1.2309	10730	-	0.4036
1.2321	10740	-	0.4035
1.2332	10750	-	0.4038
1.2344	10760	-	0.4038
1.2355	10770	-	0.4037
1.2367	10780	-	0.4037
1.2378	10790	-	0.4037
1.2390	10800	0.6921	0.4038
1.2401	10810	-	0.4039
1.2413	10820	-	0.4038
1.2424	10830	-	0.4037
1.2435	10840	-	0.4040
1.2447	10850	-	0.4042
1.2458	10860	-	0.4044
1.2470	10870	-	0.4043
1.2481	10880	-	0.4043
1.2493	10890	-	0.4044
1.2504	10900	0.728	0.4042
1.2516	10910	-	0.4044
1.2527	10920	-	0.4043
1.2539	10930	-	0.4039
1.2550	10940	-	0.4038
1.2562	10950	-	0.4037
1.2573	10960	-	0.4035
1.2585	10970	-	0.4032
1.2596	10980	-	0.4024
1.2608	10990	-	0.4019
1.2619	11000	0.713	0.4018
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1.2642	11020	-	0.4015
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1.2665	11040	-	0.4015
1.2676	11050	-	0.4014
1.2688	11060	-	0.4013
1.2699	11070	-	0.4015
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1.2722	11090	-	0.4017
1.2734	11100	0.668	0.4017
1.2745	11110	-	0.4016
1.2757	11120	-	0.4016
1.2768	11130	-	0.4019
1.2780	11140	-	0.4021
1.2791	11150	-	0.4019
1.2803	11160	-	0.4017
1.2814	11170	-	0.4017
1.2826	11180	-	0.4018
1.2837	11190	-	0.4013
1.2848	11200	0.7101	0.4011
1.2860	11210	-	0.4011
1.2871	11220	-	0.4014
1.2883	11230	-	0.4015
1.2894	11240	-	0.4010
1.2906	11250	-	0.4012
1.2917	11260	-	0.4013
1.2929	11270	-	0.4010
1.2940	11280	-	0.4006
1.2952	11290	-	0.4005
1.2963	11300	0.6963	0.4004
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1.2998	11330	-	0.4003
1.3009	11340	-	0.3999
1.3021	11350	-	0.3997
1.3032	11360	-	0.3996
1.3043	11370	-	0.3997
1.3055	11380	-	0.3996
1.3066	11390	-	0.3994
1.3078	11400	0.6706	0.3993
1.3089	11410	-	0.3991
1.3101	11420	-	0.3990
1.3112	11430	-	0.3990
1.3124	11440	-	0.3987
1.3135	11450	-	0.3981
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1.3158	11470	-	0.3975
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1.3193	11500	0.6962	0.3974
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1.3250	11550	-	0.3975
1.3261	11560	-	0.3974
1.3273	11570	-	0.3973
1.3284	11580	-	0.3971
1.3296	11590	-	0.3969
1.3307	11600	0.7083	0.3970
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1.3330	11620	-	0.3971
1.3342	11630	-	0.3973
1.3353	11640	-	0.3975
1.3365	11650	-	0.3973
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1.3388	11670	-	0.3973
1.3399	11680	-	0.3976
1.3411	11690	-	0.3976
1.3422	11700	0.6757	0.3976
1.3434	11710	-	0.3975
1.3445	11720	-	0.3973
1.3456	11730	-	0.3971
1.3468	11740	-	0.3963
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1.3491	11760	-	0.3965
1.3502	11770	-	0.3967
1.3514	11780	-	0.3966
1.3525	11790	-	0.3964
1.3537	11800	0.7091	0.3965
1.3548	11810	-	0.3964
1.3560	11820	-	0.3964
1.3571	11830	-	0.3963
1.3583	11840	-	0.3962
1.3594	11850	-	0.3961
1.3606	11860	-	0.3956
1.3617	11870	-	0.3956
1.3629	11880	-	0.3961
1.3640	11890	-	0.3963
1.3651	11900	0.6977	0.3962
1.3663	11910	-	0.3958
1.3674	11920	-	0.3960
1.3686	11930	-	0.3963
1.3697	11940	-	0.3964
1.3709	11950	-	0.3961
1.3720	11960	-	0.3960
1.3732	11970	-	0.3958
1.3743	11980	-	0.3954
1.3755	11990	-	0.3948
1.3766	12000	0.7003	0.3944
1.3778	12010	-	0.3940
1.3789	12020	-	0.3940
1.3801	12030	-	0.3938
1.3812	12040	-	0.3939
1.3824	12050	-	0.3943
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1.3847	12070	-	0.3947
1.3858	12080	-	0.3943
1.3869	12090	-	0.3940
1.3881	12100	0.679	0.3943
1.3892	12110	-	0.3945
1.3904	12120	-	0.3946
1.3915	12130	-	0.3944
1.3927	12140	-	0.3941
1.3938	12150	-	0.3941
1.3950	12160	-	0.3941
1.3961	12170	-	0.3939
1.3973	12180	-	0.3939
1.3984	12190	-	0.3939
1.3996	12200	0.692	0.3938
1.4007	12210	-	0.3937
1.4019	12220	-	0.3932
1.4030	12230	-	0.3928
1.4042	12240	-	0.3925
1.4053	12250	-	0.3922
1.4064	12260	-	0.3924
1.4076	12270	-	0.3923
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1.4099	12290	-	0.3924
1.4110	12300	0.6677	0.3925
1.4122	12310	-	0.3926
1.4133	12320	-	0.3927
1.4145	12330	-	0.3928
1.4156	12340	-	0.3928
1.4168	12350	-	0.3929
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1.4202	12380	-	0.3933
1.4214	12390	-	0.3933
1.4225	12400	0.6892	0.3930
1.4237	12410	-	0.3928
1.4248	12420	-	0.3928
1.4259	12430	-	0.3927
1.4271	12440	-	0.3927
1.4282	12450	-	0.3924
1.4294	12460	-	0.3924
1.4305	12470	-	0.3922
1.4317	12480	-	0.3920
1.4328	12490	-	0.3919
1.4340	12500	0.7016	0.3917
1.4351	12510	-	0.3914
1.4363	12520	-	0.3912
1.4374	12530	-	0.3914
1.4386	12540	-	0.3915
1.4397	12550	-	0.3915
1.4409	12560	-	0.3915
1.4420	12570	-	0.3912
1.4432	12580	-	0.3910
1.4443	12590	-	0.3910
1.4455	12600	0.68	0.3907
1.4466	12610	-	0.3906
1.4477	12620	-	0.3904
1.4489	12630	-	0.3903
1.4500	12640	-	0.3902
1.4512	12650	-	0.3899
1.4523	12660	-	0.3898
1.4535	12670	-	0.3898
1.4546	12680	-	0.3897
1.4558	12690	-	0.3896
1.4569	12700	0.681	0.3894
1.4581	12710	-	0.3892
1.4592	12720	-	0.3892
1.4604	12730	-	0.3893
1.4615	12740	-	0.3896
1.4627	12750	-	0.3898
1.4638	12760	-	0.3900
1.4650	12770	-	0.3899
1.4661	12780	-	0.3898
1.4672	12790	-	0.3899
1.4684	12800	0.6816	0.3901
1.4695	12810	-	0.3901
1.4707	12820	-	0.3901
1.4718	12830	-	0.3898
1.4730	12840	-	0.3897
1.4741	12850	-	0.3897
1.4753	12860	-	0.3895
1.4764	12870	-	0.3896
1.4776	12880	-	0.3895
1.4787	12890	-	0.3896
1.4799	12900	0.6635	0.3897
1.4810	12910	-	0.3897
1.4822	12920	-	0.3899
1.4833	12930	-	0.3900
1.4845	12940	-	0.3897
1.4856	12950	-	0.3898
1.4868	12960	-	0.3899
1.4879	12970	-	0.3898
1.4890	12980	-	0.3898
1.4902	12990	-	0.3894
1.4913	13000	0.6698	0.3892
1.4925	13010	-	0.3892
1.4936	13020	-	0.3894
1.4948	13030	-	0.3893
1.4959	13040	-	0.3894
1.4971	13050	-	0.3893
1.4982	13060	-	0.3894
1.4994	13070	-	0.3893
1.5005	13080	-	0.3895
1.5017	13090	-	0.3895
1.5028	13100	0.6757	0.3898
1.5040	13110	-	0.3898
1.5051	13120	-	0.3897
1.5063	13130	-	0.3897
1.5074	13140	-	0.3897
1.5085	13150	-	0.3899
1.5097	13160	-	0.3901
1.5108	13170	-	0.3901
1.5120	13180	-	0.3903
1.5131	13190	-	0.3901
1.5143	13200	0.6483	0.3901
1.5154	13210	-	0.3904
1.5166	13220	-	0.3904
1.5177	13230	-	0.3903
1.5189	13240	-	0.3900
1.5200	13250	-	0.3898
1.5212	13260	-	0.3894
1.5223	13270	-	0.3892
1.5235	13280	-	0.3891
1.5246	13290	-	0.3890
1.5258	13300	0.686	0.3892
1.5269	13310	-	0.3892
1.5280	13320	-	0.3892
1.5292	13330	-	0.3891
1.5303	13340	-	0.3890
1.5315	13350	-	0.3894
1.5326	13360	-	0.3895
1.5338	13370	-	0.3895
1.5349	13380	-	0.3894
1.5361	13390	-	0.3895
1.5372	13400	0.6901	0.3896
1.5384	13410	-	0.3895
1.5395	13420	-	0.3891
1.5407	13430	-	0.3891
1.5418	13440	-	0.3890
1.5430	13450	-	0.3889
1.5441	13460	-	0.3887
1.5453	13470	-	0.3885
1.5464	13480	-	0.3885
1.5476	13490	-	0.3886
1.5487	13500	0.6568	0.3887
1.5498	13510	-	0.3884
1.5510	13520	-	0.3879
1.5521	13530	-	0.3874
1.5533	13540	-	0.3870
1.5544	13550	-	0.3868
1.5556	13560	-	0.3869
1.5567	13570	-	0.3872
1.5579	13580	-	0.3873
1.5590	13590	-	0.3874
1.5602	13600	0.6665	0.3875
1.5613	13610	-	0.3876
1.5625	13620	-	0.3875
1.5636	13630	-	0.3872
1.5648	13640	-	0.3873
1.5659	13650	-	0.3872
1.5671	13660	-	0.3869
1.5682	13670	-	0.3867
1.5693	13680	-	0.3864
1.5705	13690	-	0.3861
1.5716	13700	0.6795	0.3860
1.5728	13710	-	0.3858
1.5739	13720	-	0.3858
1.5751	13730	-	0.3854
1.5762	13740	-	0.3851
1.5774	13750	-	0.3850
1.5785	13760	-	0.3849
1.5797	13770	-	0.3849
1.5808	13780	-	0.3849
1.5820	13790	-	0.3848
1.5831	13800	0.6894	0.3848
1.5843	13810	-	0.3846
1.5854	13820	-	0.3845
1.5866	13830	-	0.3847
1.5877	13840	-	0.3848
1.5888	13850	-	0.3849
1.5900	13860	-	0.3848
1.5911	13870	-	0.3846
1.5923	13880	-	0.3845
1.5934	13890	-	0.3844
1.5946	13900	0.6483	0.3845
1.5957	13910	-	0.3841
1.5969	13920	-	0.3841
1.5980	13930	-	0.3842
1.5992	13940	-	0.3842
1.6003	13950	-	0.3843
1.6015	13960	-	0.3843
1.6026	13970	-	0.3843
1.6038	13980	-	0.3844
1.6049	13990	-	0.3845
1.6061	14000	0.6856	0.3846
1.6072	14010	-	0.3845
1.6084	14020	-	0.3846
1.6095	14030	-	0.3845
1.6106	14040	-	0.3843
1.6118	14050	-	0.3842
1.6129	14060	-	0.3841
1.6141	14070	-	0.3842
1.6152	14080	-	0.3843
1.6164	14090	-	0.3845
1.6175	14100	0.6797	0.3845
1.6187	14110	-	0.3845
1.6198	14120	-	0.3844
1.6210	14130	-	0.3842
1.6221	14140	-	0.3841
1.6233	14150	-	0.3838
1.6244	14160	-	0.3836
1.6256	14170	-	0.3835
1.6267	14180	-	0.3834
1.6279	14190	-	0.3831
1.6290	14200	0.7057	0.3828
1.6301	14210	-	0.3825
1.6313	14220	-	0.3822
1.6324	14230	-	0.3821
1.6336	14240	-	0.3820
1.6347	14250	-	0.3822
1.6359	14260	-	0.3822
1.6370	14270	-	0.3822
1.6382	14280	-	0.3821
1.6393	14290	-	0.3822
1.6405	14300	0.6699	0.3827
1.6416	14310	-	0.3828
1.6428	14320	-	0.3827
1.6439	14330	-	0.3823
1.6451	14340	-	0.3822
1.6462	14350	-	0.3824
1.6474	14360	-	0.3826
1.6485	14370	-	0.3826
1.6497	14380	-	0.3827
1.6508	14390	-	0.3827
1.6519	14400	0.6615	0.3827
1.6531	14410	-	0.3828
1.6542	14420	-	0.3826
1.6554	14430	-	0.3825
1.6565	14440	-	0.3826
1.6577	14450	-	0.3830
1.6588	14460	-	0.3830
1.6600	14470	-	0.3830
1.6611	14480	-	0.3830
1.6623	14490	-	0.3830
1.6634	14500	0.6628	0.3829
1.6646	14510	-	0.3829
1.6657	14520	-	0.3829
1.6669	14530	-	0.3829
1.6680	14540	-	0.3829
1.6692	14550	-	0.3829
1.6703	14560	-	0.3830
1.6714	14570	-	0.3828
1.6726	14580	-	0.3825
1.6737	14590	-	0.3822
1.6749	14600	0.6728	0.3819
1.6760	14610	-	0.3817
1.6772	14620	-	0.3817
1.6783	14630	-	0.3815
1.6795	14640	-	0.3813
1.6806	14650	-	0.3815
1.6818	14660	-	0.3814
1.6829	14670	-	0.3814
1.6841	14680	-	0.3812
1.6852	14690	-	0.3809
1.6864	14700	0.6852	0.3808
1.6875	14710	-	0.3807
1.6887	14720	-	0.3804
1.6898	14730	-	0.3802
1.6909	14740	-	0.3799
1.6921	14750	-	0.3798
1.6932	14760	-	0.3797
1.6944	14770	-	0.3795
1.6955	14780	-	0.3797
1.6967	14790	-	0.3797
1.6978	14800	0.6585	0.3797
1.6990	14810	-	0.3797
1.7001	14820	-	0.3798
1.7013	14830	-	0.3800
1.7024	14840	-	0.3800
1.7036	14850	-	0.3798
1.7047	14860	-	0.3799
1.7059	14870	-	0.3799
1.7070	14880	-	0.3798
1.7082	14890	-	0.3801
1.7093	14900	0.6691	0.3801
1.7105	14910	-	0.3800
1.7116	14920	-	0.3798
1.7127	14930	-	0.3795
1.7139	14940	-	0.3792
1.7150	14950	-	0.3791
1.7162	14960	-	0.3790
1.7173	14970	-	0.3790
1.7185	14980	-	0.3793
1.7196	14990	-	0.3794
1.7208	15000	0.6676	0.3794
1.7219	15010	-	0.3794
1.7231	15020	-	0.3794
1.7242	15030	-	0.3793
1.7254	15040	-	0.3791
1.7265	15050	-	0.3790
1.7277	15060	-	0.3788
1.7288	15070	-	0.3787
1.7300	15080	-	0.3787
1.7311	15090	-	0.3787
1.7322	15100	0.6945	0.3785
1.7334	15110	-	0.3782
1.7345	15120	-	0.3781
1.7357	15130	-	0.3780
1.7368	15140	-	0.3782
1.7380	15150	-	0.3782
1.7391	15160	-	0.3781
1.7403	15170	-	0.3781
1.7414	15180	-	0.3781
1.7426	15190	-	0.3784
1.7437	15200	0.6697	0.3787
1.7449	15210	-	0.3790
1.7460	15220	-	0.3792
1.7472	15230	-	0.3792
1.7483	15240	-	0.3791
1.7495	15250	-	0.3791
1.7506	15260	-	0.3788
1.7517	15270	-	0.3789
1.7529	15280	-	0.3788
1.7540	15290	-	0.3788
1.7552	15300	0.6557	0.3784
1.7563	15310	-	0.3784
1.7575	15320	-	0.3784
1.7586	15330	-	0.3785
1.7598	15340	-	0.3789
1.7609	15350	-	0.3791
1.7621	15360	-	0.3791
1.7632	15370	-	0.3791
1.7644	15380	-	0.3789
1.7655	15390	-	0.3788
1.7667	15400	0.6837	0.3788
1.7678	15410	-	0.3788
1.7690	15420	-	0.3788
1.7701	15430	-	0.3787
1.7713	15440	-	0.3786
1.7724	15450	-	0.3785
1.7735	15460	-	0.3784
1.7747	15470	-	0.3780
1.7758	15480	-	0.3778
1.7770	15490	-	0.3778
1.7781	15500	0.6685	0.3779
1.7793	15510	-	0.3781
1.7804	15520	-	0.3783
1.7816	15530	-	0.3784
1.7827	15540	-	0.3782
1.7839	15550	-	0.3779
1.7850	15560	-	0.3779
1.7862	15570	-	0.3782
1.7873	15580	-	0.3786
1.7885	15590	-	0.3785
1.7896	15600	0.6521	0.3783
1.7908	15610	-	0.3785
1.7919	15620	-	0.3783
1.7930	15630	-	0.3783
1.7942	15640	-	0.3784
1.7953	15650	-	0.3783
1.7965	15660	-	0.3782
1.7976	15670	-	0.3780
1.7988	15680	-	0.3779
1.7999	15690	-	0.3779
1.8011	15700	0.649	0.3779
1.8022	15710	-	0.3781
1.8034	15720	-	0.3781
1.8045	15730	-	0.3782
1.8057	15740	-	0.3780
1.8068	15750	-	0.3780
1.8080	15760	-	0.3780
1.8091	15770	-	0.3780
1.8103	15780	-	0.3780
1.8114	15790	-	0.3781
1.8126	15800	0.6673	0.3783
1.8137	15810	-	0.3781
1.8148	15820	-	0.3781
1.8160	15830	-	0.3785
1.8171	15840	-	0.3788
1.8183	15850	-	0.3789
1.8194	15860	-	0.3788
1.8206	15870	-	0.3786
1.8217	15880	-	0.3783
1.8229	15890	-	0.3782
1.8240	15900	0.6902	0.3783
1.8252	15910	-	0.3781
1.8263	15920	-	0.3779
1.8275	15930	-	0.3777
1.8286	15940	-	0.3778
1.8298	15950	-	0.3780
1.8309	15960	-	0.3780
1.8321	15970	-	0.3781
1.8332	15980	-	0.3780
1.8343	15990	-	0.3779
1.8355	16000	0.6568	0.3778

Framework Versions

• Python: 3.12.8
• Sentence Transformers: 3.4.1
• Transformers: 4.49.0
• PyTorch: 2.2.0+cu121
• Accelerate: 1.4.0
• Datasets: 3.3.2
• Tokenizers: 0.21.0

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}
}