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SentenceTransformer based on sentence-transformers/all-mpnet-base-v2

This is a sentence-transformers model finetuned from sentence-transformers/all-mpnet-base-v2. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

The idea is to take in an utterance, and determine which DMRSQ items best fit it.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: sentence-transformers/all-mpnet-base-v2
Maximum Sequence Length: 384 tokens
Output Dimensionality: 768 dimensions
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': 384, 'do_lower_case': False, 'architecture': 'MPNetModel'})
  (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 = [
    'I am really struggling with my studies at the moment, I am so anxious that I keep putting off my essays and worrying all the time.',
    'ITEM 77: When considering an emotionally important decision, the subject explores his or her own motives and limitations to arrive at a more fulfilling decision.',
    'ITEM 84: The subject recites a litany of issues and problems but does not appear to be engaged in solving them, but rather prefers to complain.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000,  0.2863, -0.0609],
#         [ 0.2863,  1.0000, -0.0028],
#         [-0.0609, -0.0028,  1.0000]])

Training Details

Training Dataset

Trained using the PsyDefConv dataset (https://arxiv.org/abs/2512.15601v1).

Unnamed Dataset

Size: 97,930 training samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 3 tokens
mean: 27.14 tokens
max: 206 tokens

min: 14 tokens
mean: 42.05 tokens
max: 81 tokens

min: -1.0
mean: -0.47
max: 0.96

	sentence1	sentence2	score
type	string	string	float
details	min: 3 tokens mean: 27.14 tokens max: 206 tokens	min: 14 tokens mean: 42.05 tokens max: 81 tokens	min: -1.0 mean: -0.47 max: 0.96

Samples:

sentence1	sentence2	score
`I don't know. Do you hav e any ideas?`	`ITEM 36: The subject describes emotional conflictual situations in which some of the feelings or dissatisfaction are channeled into creative or artistic activities. The resulting creative products - such as a poem or painting - give the subject a sense of mastery or relief from the conflicts.`	`-0.1`
`Good you understand me`	`ITEM 108: The subject cannot remember certain facts which would normally not be forgotten, such as a distressing incident, reflecting some uneasy feelings about the topic.`	`-1.0`
`its about me and my friend got fight for misunderstanding`	`ITEM 28: When telling an emotionally meaningful story, the subject states that he or she does not have specific feelings that one would expect, although the subject recognizes that he or she should feel something.`	`-1.0`

Loss: AnglELoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 64
num_train_epochs: 32
learning_rate: 2e-05
lr_scheduler_type: cosine
bf16: True
eval_strategy: steps
warmup_ratio: 0.1

All Hyperparameters

Click to expand

per_device_train_batch_size: 64
num_train_epochs: 32
max_steps: -1
learning_rate: 2e-05
lr_scheduler_type: cosine
lr_scheduler_kwargs: None
warmup_steps: 0.1
optim: adamw_torch_fused
optim_args: None
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
optim_target_modules: None
gradient_accumulation_steps: 1
average_tokens_across_devices: True
max_grad_norm: 1.0
label_smoothing_factor: 0.0
bf16: True
fp16: False
bf16_full_eval: False
fp16_full_eval: False
tf32: None
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
use_liger_kernel: False
liger_kernel_config: None
use_cache: False
neftune_noise_alpha: None
torch_empty_cache_steps: None
auto_find_batch_size: False
log_on_each_node: True
logging_nan_inf_filter: True
include_num_input_tokens_seen: no
log_level: passive
log_level_replica: warning
disable_tqdm: False
project: huggingface
trackio_space_id: trackio
eval_strategy: steps
per_device_eval_batch_size: 8
prediction_loss_only: True
eval_on_start: False
eval_do_concat_batches: True
eval_use_gather_object: False
eval_accumulation_steps: None
include_for_metrics: []
batch_eval_metrics: False
save_only_model: False
save_on_each_node: False
enable_jit_checkpoint: False
push_to_hub: False
hub_private_repo: None
hub_model_id: None
hub_strategy: every_save
hub_always_push: False
hub_revision: None
load_best_model_at_end: False
ignore_data_skip: False
restore_callback_states_from_checkpoint: False
full_determinism: False
seed: 42
data_seed: None
use_cpu: False
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
parallelism_config: None
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_pin_memory: True
dataloader_persistent_workers: False
dataloader_prefetch_factor: None
remove_unused_columns: True
label_names: None
train_sampling_strategy: random
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
ddp_backend: None
ddp_timeout: 1800
fsdp: []
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
deepspeed: None
debug: []
skip_memory_metrics: True
do_predict: False
resume_from_checkpoint: None
warmup_ratio: 0.1
local_rank: -1
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Click to expand

Epoch	Step	Training Loss
0.0327	50	9.0286
0.0653	100	8.6644
0.0980	150	8.4482
0.1306	200	8.0420
0.1633	250	7.8877
0.1960	300	7.7193
0.2286	350	7.6281
0.2613	400	7.5958
0.2939	450	7.5480
0.3266	500	7.4803
0.3592	550	7.4053
0.3919	600	7.4259
0.4246	650	7.3823
0.4572	700	7.3625
0.4899	750	7.3457
0.5225	800	7.2602
0.5552	850	7.2397
0.5879	900	7.2235
0.6205	950	7.1831
0.6532	1000	7.1283
0.6858	1050	7.0789
0.7185	1100	7.0127
0.7511	1150	7.0097
0.7838	1200	6.9263
0.8165	1250	6.9291
0.8491	1300	6.9226
0.8818	1350	6.9457
0.9144	1400	6.9025
0.9471	1450	6.8800
0.9798	1500	6.9247
1.0124	1550	6.7813
1.0451	1600	6.8561
1.0777	1650	6.8159
1.1104	1700	6.7782
1.1430	1750	6.7164
1.1757	1800	6.6916
1.2084	1850	6.6451
1.2410	1900	6.6419
1.2737	1950	6.6067
1.3063	2000	6.5635
1.3390	2050	6.5494
1.3717	2100	6.4898
1.4043	2150	6.4935
1.4370	2200	6.3574
1.4696	2250	6.3831
1.5023	2300	6.2581
1.5349	2350	6.2736
1.5676	2400	6.2733
1.6003	2450	6.1686
1.6329	2500	6.1762
1.6656	2550	6.1364
1.6982	2600	6.1803
1.7309	2650	6.0832
1.7636	2700	6.1267
1.7962	2750	6.1240
1.8289	2800	6.1396
1.8615	2850	6.0066
1.8942	2900	6.0773
1.9268	2950	5.9386
1.9595	3000	6.0717
1.9922	3050	6.0051
2.0248	3100	5.8921
2.0575	3150	5.8049
2.0901	3200	5.9381
2.1228	3250	5.8070
2.1555	3300	5.8685
2.1881	3350	5.9179
2.2208	3400	6.1413
2.2534	3450	5.8098
2.2861	3500	5.8001
2.3187	3550	5.8884
2.3514	3600	6.0481
2.3841	3650	5.7075
2.4167	3700	5.7332
2.4494	3750	5.7707
2.4820	3800	5.6845
2.5147	3850	5.6999
2.5474	3900	5.7848
2.5800	3950	5.7242
2.6127	4000	5.7651
2.6453	4050	5.8526
2.6780	4100	5.5716
2.7106	4150	5.8108
2.7433	4200	5.7400
2.7760	4250	5.3879
2.8086	4300	5.7105
2.8413	4350	5.5207
2.8739	4400	5.5447
2.9066	4450	5.4288
2.9393	4500	5.5564
2.9719	4550	5.3389
3.0046	4600	4.9946
3.0372	4650	5.5276
3.0699	4700	5.3802
3.1025	4750	5.1807
3.1352	4800	5.3450
3.1679	4850	5.3487
3.2005	4900	5.0817
3.2332	4950	4.9707
3.2658	5000	4.9607
3.2985	5050	4.9008
3.3312	5100	5.0297
3.3638	5150	5.2010
3.3965	5200	4.8707
3.4291	5250	5.2481
3.4618	5300	5.0141
3.4944	5350	4.8756
3.5271	5400	5.0154
3.5598	5450	4.8147
3.5924	5500	5.0341
3.6251	5550	4.9787
3.6577	5600	4.7306
3.6904	5650	4.7687
3.7231	5700	4.6810
3.7557	5750	4.6770
3.7884	5800	4.8442
3.8210	5850	4.9817
3.8537	5900	4.8987
3.8863	5950	4.7971
3.9190	6000	4.7418
3.9517	6050	4.8572
3.9843	6100	4.6620
4.0170	6150	4.4533
4.0496	6200	4.2484
4.0823	6250	4.3388
4.1150	6300	4.5170
4.1476	6350	4.3652
4.1803	6400	4.3752
4.2129	6450	4.5958
4.2456	6500	4.7159
4.2782	6550	4.3793
4.3109	6600	4.3346
4.3436	6650	4.4930
4.3762	6700	4.6090
4.4089	6750	4.3612
4.4415	6800	4.3246
4.4742	6850	4.2361
4.5069	6900	4.5913
4.5395	6950	4.4233
4.5722	7000	4.1146
4.6048	7050	4.2286
4.6375	7100	4.2269
4.6702	7150	4.3150
4.7028	7200	4.1564
4.7355	7250	3.9239
4.7681	7300	4.0376
4.8008	7350	4.2173
4.8334	7400	3.9518
4.8661	7450	4.2801
4.8988	7500	4.2634
4.9314	7550	4.3524
4.9641	7600	3.9455
4.9967	7650	3.9870
5.0294	7700	4.0884
5.0621	7750	3.7905
5.0947	7800	4.1414
5.1274	7850	4.0200
5.1600	7900	3.9873
5.1927	7950	4.0775
5.2253	8000	3.7879
5.2580	8050	3.8040
5.2907	8100	4.1354
5.3233	8150	3.6614
5.3560	8200	3.8756
5.3886	8250	3.6846
5.4213	8300	3.7679
5.4540	8350	3.6936
5.4866	8400	3.4698
5.5193	8450	3.7179
5.5519	8500	4.0484
5.5846	8550	3.9596
5.6172	8600	3.7064
5.6499	8650	3.8859
5.6826	8700	3.9497
5.7152	8750	3.8719
5.7479	8800	3.9762
5.7805	8850	3.6275
5.8132	8900	3.5444
5.8459	8950	3.9623
5.8785	9000	3.7250
5.9112	9050	3.4245
5.9438	9100	3.8240
5.9765	9150	3.5984
6.0091	9200	3.6267
6.0418	9250	3.5863
6.0745	9300	3.4977
6.1071	9350	3.3482
6.1398	9400	3.3381
6.1724	9450	3.7424
6.2051	9500	3.9227
6.2378	9550	3.3812
6.2704	9600	3.4736
6.3031	9650	3.3770
6.3357	9700	3.7019
6.3684	9750	3.4811
6.4010	9800	3.6467
6.4337	9850	4.2202
6.4664	9900	3.5151
6.4990	9950	3.4188
6.5317	10000	3.5108
6.5643	10050	3.3860
6.5970	10100	3.5434
6.6297	10150	3.1989
6.6623	10200	3.9570
6.6950	10250	3.3240
6.7276	10300	3.5816
6.7603	10350	3.4212
6.7929	10400	3.8962
6.8256	10450	3.7376
6.8583	10500	3.2337
6.8909	10550	3.9117
6.9236	10600	3.4673
6.9562	10650	3.2873
6.9889	10700	3.4152
7.0216	10750	3.0089
7.0542	10800	3.2337
7.0869	10850	3.1476
7.1195	10900	3.2843
7.1522	10950	3.1003
7.1848	11000	3.1259
7.2175	11050	3.6063
7.2502	11100	3.2024
7.2828	11150	3.0674
7.3155	11200	3.0912
7.3481	11250	3.2739
7.3808	11300	3.1656
7.4135	11350	3.2524
7.4461	11400	3.6492
7.4788	11450	3.0780
7.5114	11500	3.8293
7.5441	11550	3.3781
7.5767	11600	3.1099
7.6094	11650	3.3728
7.6421	11700	3.1408
7.6747	11750	3.5507
7.7074	11800	3.0614
7.7400	11850	3.5971
7.7727	11900	3.3297
7.8054	11950	3.3595
7.8380	12000	3.2790
7.8707	12050	3.8074
7.9033	12100	2.9731
7.9360	12150	2.9645
7.9686	12200	3.0973
8.0013	12250	3.2855
8.0340	12300	3.0362
8.0666	12350	2.8836
8.0993	12400	3.2710
8.1319	12450	3.2857
8.1646	12500	3.5436
8.1973	12550	3.0281
8.2299	12600	3.2262
8.2626	12650	3.0665
8.2952	12700	3.1259
8.3279	12750	2.7805
8.3605	12800	3.3477
8.3932	12850	3.1317
8.4259	12900	3.1003
8.4585	12950	2.8282
8.4912	13000	2.8609
8.5238	13050	2.8478
8.5565	13100	3.2137
8.5892	13150	3.3437
8.6218	13200	2.9976
8.6545	13250	3.0456
8.6871	13300	2.9759
8.7198	13350	3.0762
8.7524	13400	3.2567
8.7851	13450	2.8913
8.8178	13500	2.8575
8.8504	13550	2.9115
8.8831	13600	2.8438
8.9157	13650	3.4465
8.9484	13700	3.2220
8.9811	13750	3.3776
9.0137	13800	2.9947
9.0464	13850	2.6348
9.0790	13900	2.7870
9.1117	13950	3.1431
9.1444	14000	2.7580
9.1770	14050	3.0923
9.2097	14100	2.7125
9.2423	14150	3.0405
9.2750	14200	2.8070
9.3076	14250	2.8188
9.3403	14300	2.9717
9.3730	14350	3.3031
9.4056	14400	3.4510
9.4383	14450	2.8420
9.4709	14500	2.7343
9.5036	14550	3.1293
9.5363	14600	3.1140
9.5689	14650	3.1765
9.6016	14700	2.9057
9.6342	14750	2.7985
9.6669	14800	2.8251
9.6995	14850	2.9817
9.7322	14900	2.8598
9.7649	14950	2.7754
9.7975	15000	3.0566
9.8302	15050	2.9721
9.8628	15100	2.5517
9.8955	15150	2.8098
9.9282	15200	2.9832
9.9608	15250	3.0638
9.9935	15300	3.1261
10.0261	15350	2.7489
10.0588	15400	2.9901
10.0914	15450	2.7472
10.1241	15500	2.7541
10.1568	15550	2.7685
10.1894	15600	2.5877
10.2221	15650	2.7921
10.2547	15700	2.8734
10.2874	15750	3.1077
10.3201	15800	2.8413
10.3527	15850	2.7016
10.3854	15900	2.7277
10.4180	15950	2.6911
10.4507	16000	2.4181
10.4833	16050	2.7372
10.5160	16100	3.3660
10.5487	16150	2.7253
10.5813	16200	3.0624
10.6140	16250	2.6498
10.6466	16300	3.2688
10.6793	16350	2.6105
10.7120	16400	2.6413
10.7446	16450	2.7669
10.7773	16500	2.6641
10.8099	16550	3.2430
10.8426	16600	2.6195
10.8752	16650	2.6909
10.9079	16700	2.5267
10.9406	16750	2.7383
10.9732	16800	2.8003
11.0059	16850	2.7182
11.0385	16900	2.3434
11.0712	16950	2.4962
11.1039	17000	2.3787
11.1365	17050	2.4857
11.1692	17100	2.4772
11.2018	17150	2.6334
11.2345	17200	2.6987
11.2671	17250	2.9109
11.2998	17300	2.4420
11.3325	17350	2.8495
11.3651	17400	2.8956
11.3978	17450	2.3482
11.4304	17500	2.4971
11.4631	17550	2.4384
11.4958	17600	2.5417
11.5284	17650	2.7535
11.5611	17700	2.3815
11.5937	17750	2.8631
11.6264	17800	2.3544
11.6590	17850	2.8596
11.6917	17900	2.7100
11.7244	17950	2.5971
11.7570	18000	2.5054
11.7897	18050	2.6495
11.8223	18100	2.9272
11.8550	18150	2.6058
11.8877	18200	2.9791
11.9203	18250	2.7122
11.9530	18300	2.7005
11.9856	18350	2.7432
12.0183	18400	2.5222
12.0509	18450	2.4689
12.0836	18500	2.2014
12.1163	18550	2.4980
12.1489	18600	2.6785
12.1816	18650	2.6154
12.2142	18700	2.3773
12.2469	18750	2.4081
12.2796	18800	2.6466
12.3122	18850	2.5145
12.3449	18900	2.5929
12.3775	18950	2.3862
12.4102	19000	2.4002
12.4428	19050	2.5694
12.4755	19100	2.4865
12.5082	19150	2.5911
12.5408	19200	2.4595
12.5735	19250	2.4827
12.6061	19300	2.4510
12.6388	19350	2.3470
12.6715	19400	2.5575
12.7041	19450	2.7434
12.7368	19500	2.2912
12.7694	19550	2.5389
12.8021	19600	2.3614
12.8347	19650	2.7248
12.8674	19700	2.5501
12.9001	19750	2.6211
12.9327	19800	2.4423
12.9654	19850	2.9040
12.9980	19900	2.2045
13.0307	19950	2.2205
13.0634	20000	2.4343
13.0960	20050	2.0991
13.1287	20100	2.2938
13.1613	20150	2.4924
13.1940	20200	2.2811
13.2266	20250	2.1915
13.2593	20300	2.4335
13.2920	20350	2.3268
13.3246	20400	2.4420
13.3573	20450	2.3000
13.3899	20500	2.3566
13.4226	20550	2.2889
13.4553	20600	2.5217
13.4879	20650	2.4200
13.5206	20700	2.3375
13.5532	20750	2.8099
13.5859	20800	2.3955
13.6185	20850	2.3459
13.6512	20900	2.3745
13.6839	20950	2.4857
13.7165	21000	2.6480
13.7492	21050	2.5800
13.7818	21100	2.7418
13.8145	21150	2.4374
13.8472	21200	2.4302
13.8798	21250	2.3211
13.9125	21300	2.3181
13.9451	21350	2.4256
13.9778	21400	2.3024
14.0105	21450	2.2549
14.0431	21500	2.3100
14.0758	21550	2.0652
14.1084	21600	2.1677
14.1411	21650	2.4389
14.1737	21700	2.2917
14.2064	21750	2.0271
14.2391	21800	2.2715
14.2717	21850	2.3162
14.3044	21900	2.4586
14.3370	21950	2.2283
14.3697	22000	2.1602
14.4024	22050	2.0790
14.4350	22100	2.5350
14.4677	22150	2.1997
14.5003	22200	2.2950
14.5330	22250	2.2393
14.5656	22300	2.2792
14.5983	22350	2.2812
14.6310	22400	2.3468
14.6636	22450	2.4226
14.6963	22500	2.2243
14.7289	22550	2.2009
14.7616	22600	2.4433
14.7943	22650	2.3105
14.8269	22700	2.1217
14.8596	22750	2.3712
14.8922	22800	2.3294
14.9249	22850	2.6271
14.9575	22900	2.0722
14.9902	22950	2.2202
15.0229	23000	1.9338
15.0555	23050	2.3543
15.0882	23100	1.9807
15.1208	23150	1.8918
15.1535	23200	2.2189
15.1862	23250	2.3504
15.2188	23300	2.2202
15.2515	23350	2.0904
15.2841	23400	1.9969
15.3168	23450	2.3871
15.3494	23500	2.1765
15.3821	23550	2.3617
15.4148	23600	2.2702
15.4474	23650	2.1637
15.4801	23700	2.2266
15.5127	23750	2.2683
15.5454	23800	2.2319
15.5781	23850	2.1214
15.6107	23900	2.2746
15.6434	23950	2.0026
15.6760	24000	1.9858
15.7087	24050	2.3871
15.7413	24100	2.1308
15.7740	24150	2.2342
15.8067	24200	2.0566
15.8393	24250	2.2963
15.8720	24300	2.2808
15.9046	24350	2.0946
15.9373	24400	2.2421
15.9700	24450	2.0743
16.0026	24500	2.1031
16.0353	24550	1.9651
16.0679	24600	1.8037
16.1006	24650	2.0392
16.1332	24700	2.1911
16.1659	24750	2.2293
16.1986	24800	2.1284
16.2312	24850	2.0906
16.2639	24900	1.9106
16.2965	24950	1.9963
16.3292	25000	2.1065
16.3619	25050	2.1713
16.3945	25100	1.9761
16.4272	25150	2.2876
16.4598	25200	2.2098
16.4925	25250	2.2109
16.5251	25300	1.9847
16.5578	25350	2.1614
16.5905	25400	2.1328
16.6231	25450	2.1855
16.6558	25500	2.0902
16.6884	25550	2.1599
16.7211	25600	1.9535
16.7538	25650	2.0449
16.7864	25700	2.2589
16.8191	25750	2.1057
16.8517	25800	2.1730
16.8844	25850	2.0112
16.9170	25900	2.2679
16.9497	25950	2.2358
16.9824	26000	2.0218
17.0150	26050	2.0626
17.0477	26100	2.2664
17.0803	26150	2.0680
17.1130	26200	1.7651
17.1457	26250	1.9711
17.1783	26300	2.1895
17.2110	26350	1.7760
17.2436	26400	2.1259
17.2763	26450	1.9074
17.3089	26500	2.4010
17.3416	26550	1.9644
17.3743	26600	1.7472
17.4069	26650	2.1776
17.4396	26700	1.7590
17.4722	26750	2.1171
17.5049	26800	1.9798
17.5376	26850	1.9788
17.5702	26900	2.1596
17.6029	26950	2.1677
17.6355	27000	2.0866
17.6682	27050	1.8937
17.7008	27100	2.1016
17.7335	27150	2.1834
17.7662	27200	2.1638
17.7988	27250	2.0891
17.8315	27300	1.8167
17.8641	27350	2.2649
17.8968	27400	1.8734
17.9295	27450	1.8578
17.9621	27500	1.9935
17.9948	27550	2.1817
18.0274	27600	1.8428
18.0601	27650	1.8994
18.0927	27700	2.1134
18.1254	27750	1.7124
18.1581	27800	1.9190
18.1907	27850	2.0476
18.2234	27900	1.9766
18.2560	27950	1.9530
18.2887	28000	2.0143
18.3214	28050	2.0919
18.3540	28100	1.8905
18.3867	28150	1.9557
18.4193	28200	2.0128
18.4520	28250	1.8608
18.4847	28300	1.7412
18.5173	28350	1.6939
18.5500	28400	2.0228
18.5826	28450	1.9211
18.6153	28500	2.0674
18.6479	28550	1.8358
18.6806	28600	2.1006
18.7133	28650	1.9139
18.7459	28700	1.8472
18.7786	28750	1.9808
18.8112	28800	1.8494
18.8439	28850	1.9636
18.8766	28900	1.8708
18.9092	28950	1.7714
18.9419	29000	1.9487
18.9745	29050	2.1460
19.0072	29100	1.9752
19.0398	29150	2.0633
19.0725	29200	2.1856
19.1052	29250	1.9137
19.1378	29300	1.8796
19.1705	29350	1.7385
19.2031	29400	1.7861
19.2358	29450	1.7297
19.2685	29500	1.9150
19.3011	29550	1.9047
19.3338	29600	2.0195
19.3664	29650	1.8167
19.3991	29700	1.9342
19.4317	29750	1.8338
19.4644	29800	1.7466
19.4971	29850	1.8973
19.5297	29900	1.7393
19.5624	29950	1.9313
19.5950	30000	1.8565
19.6277	30050	2.0979
19.6604	30100	1.9298
19.6930	30150	1.8885
19.7257	30200	1.7616
19.7583	30250	1.7516
19.7910	30300	1.9562
19.8236	30350	1.9194
19.8563	30400	2.0395
19.8890	30450	1.7662
19.9216	30500	1.9583
19.9543	30550	2.0188
19.9869	30600	1.8229
20.0196	30650	1.6464
20.0523	30700	1.9959
20.0849	30750	1.6206
20.1176	30800	1.7450
20.1502	30850	1.7764
20.1829	30900	1.6936
20.2155	30950	1.6909
20.2482	31000	1.9737
20.2809	31050	1.8326
20.3135	31100	1.7103
20.3462	31150	1.7439
20.3788	31200	1.8374
20.4115	31250	1.6792
20.4442	31300	1.9449
20.4768	31350	1.8734
20.5095	31400	1.6430
20.5421	31450	2.0952
20.5748	31500	1.9891
20.6074	31550	1.7573
20.6401	31600	1.9143
20.6728	31650	1.6937
20.7054	31700	1.9446
20.7381	31750	1.9838
20.7707	31800	1.7570
20.8034	31850	1.8097
20.8361	31900	1.9721
20.8687	31950	1.8207
20.9014	32000	1.7857
20.9340	32050	1.7605
20.9667	32100	1.8348
20.9993	32150	1.6633
21.0320	32200	1.4270
21.0647	32250	1.8491
21.0973	32300	1.6216
21.1300	32350	1.9579
21.1626	32400	1.6637
21.1953	32450	1.5366
21.2280	32500	1.7831
21.2606	32550	1.9055
21.2933	32600	1.8531
21.3259	32650	1.3591
21.3586	32700	1.8334
21.3912	32750	1.8504
21.4239	32800	1.9743
21.4566	32850	1.8119
21.4892	32900	1.8622
21.5219	32950	1.7152
21.5545	33000	2.0479
21.5872	33050	1.5677
21.6199	33100	1.7661
21.6525	33150	1.9125
21.6852	33200	1.9057
21.7178	33250	1.3903
21.7505	33300	1.5802
21.7831	33350	1.9131
21.8158	33400	1.7475
21.8485	33450	1.8216
21.8811	33500	1.9810
21.9138	33550	1.9163
21.9464	33600	1.7334
21.9791	33650	1.7990
22.0118	33700	1.7664
22.0444	33750	1.6453
22.0771	33800	1.7921
22.1097	33850	1.6310
22.1424	33900	1.6774
22.1750	33950	1.9685
22.2077	34000	1.8472
22.2404	34050	1.6497
22.2730	34100	1.6521
22.3057	34150	1.4748
22.3383	34200	1.6577
22.3710	34250	1.6079
22.4037	34300	1.4378
22.4363	34350	1.7309
22.4690	34400	1.8067
22.5016	34450	1.7367
22.5343	34500	1.6197
22.5669	34550	1.3781
22.5996	34600	1.7569
22.6323	34650	1.7471
22.6649	34700	1.7978
22.6976	34750	1.6486
22.7302	34800	1.8566
22.7629	34850	1.7889
22.7956	34900	1.7175
22.8282	34950	1.7887
22.8609	35000	1.8908
22.8935	35050	1.7223
22.9262	35100	1.6751
22.9589	35150	1.8001
22.9915	35200	1.8165
23.0242	35250	1.3228
23.0568	35300	1.8181
23.0895	35350	1.6723
23.1221	35400	1.5197
23.1548	35450	1.4939
23.1875	35500	1.6044
23.2201	35550	1.4903
23.2528	35600	1.7873
23.2854	35650	1.9744
23.3181	35700	1.7997
23.3508	35750	1.5434
23.3834	35800	1.5244
23.4161	35850	1.7727
23.4487	35900	1.8397
23.4814	35950	1.7058
23.5140	36000	1.5808
23.5467	36050	1.7354
23.5794	36100	1.5446
23.6120	36150	1.5880
23.6447	36200	1.8711
23.6773	36250	1.7160
23.7100	36300	1.7900
23.7427	36350	1.7464
23.7753	36400	1.4821
23.8080	36450	1.5822
23.8406	36500	1.6595
23.8733	36550	1.8593
23.9059	36600	1.6593
23.9386	36650	1.8218
23.9713	36700	1.5263
24.0039	36750	1.6455
24.0366	36800	1.4105
24.0692	36850	1.5919
24.1019	36900	1.7372
24.1346	36950	1.6470
24.1672	37000	1.5463
24.1999	37050	1.8665
24.2325	37100	1.7140
24.2652	37150	1.4222
24.2978	37200	1.5772
24.3305	37250	1.4491
24.3632	37300	1.7550
24.3958	37350	1.6556
24.4285	37400	1.6815
24.4611	37450	1.4938
24.4938	37500	1.6417
24.5265	37550	1.6379
24.5591	37600	1.7183
24.5918	37650	1.6381
24.6244	37700	1.7205
24.6571	37750	1.6668
24.6897	37800	1.6097
24.7224	37850	1.5974
24.7551	37900	1.4577
24.7877	37950	1.7353
24.8204	38000	1.8409
24.8530	38050	1.6529
24.8857	38100	1.7042
24.9184	38150	1.5920
24.9510	38200	1.5260
24.9837	38250	1.6843
25.0163	38300	1.5489
25.0490	38350	1.4266
25.0816	38400	1.6260
25.1143	38450	1.5667
25.1470	38500	1.5914
25.1796	38550	1.6381
25.2123	38600	1.4450
25.2449	38650	1.4836
25.2776	38700	1.4296
25.3103	38750	1.4547
25.3429	38800	1.6745
25.3756	38850	1.6315
25.4082	38900	1.6128
25.4409	38950	1.6090
25.4735	39000	1.6080
25.5062	39050	1.4883
25.5389	39100	1.4759
25.5715	39150	1.7895
25.6042	39200	1.6290
25.6368	39250	1.5896
25.6695	39300	1.3669
25.7022	39350	1.7570
25.7348	39400	1.7096
25.7675	39450	1.6464
25.8001	39500	1.5836
25.8328	39550	1.4649
25.8654	39600	1.5321
25.8981	39650	1.7041
25.9308	39700	1.6562
25.9634	39750	1.8695
25.9961	39800	1.5636
26.0287	39850	1.6339
26.0614	39900	1.5447
26.0941	39950	1.6000
26.1267	40000	1.6995
26.1594	40050	1.7142
26.1920	40100	1.3842
26.2247	40150	1.4875
26.2573	40200	1.5789
26.2900	40250	1.6715
26.3227	40300	1.6125
26.3553	40350	1.5673
26.3880	40400	1.6396
26.4206	40450	1.5057
26.4533	40500	1.6584
26.4860	40550	1.6758
26.5186	40600	1.4290
26.5513	40650	1.7511
26.5839	40700	1.5793
26.6166	40750	1.4984
26.6492	40800	1.4702
26.6819	40850	1.8344
26.7146	40900	1.5882
26.7472	40950	1.4245
26.7799	41000	1.5706
26.8125	41050	1.6478
26.8452	41100	1.4552
26.8779	41150	1.6468
26.9105	41200	1.6006
26.9432	41250	1.6027
26.9758	41300	1.4874
27.0085	41350	1.3157
27.0411	41400	1.4175
27.0738	41450	1.4619
27.1065	41500	1.5909
27.1391	41550	1.4197
27.1718	41600	1.5115
27.2044	41650	1.4334
27.2371	41700	1.5533
27.2698	41750	1.5201
27.3024	41800	1.5953
27.3351	41850	1.4537
27.3677	41900	1.6232
27.4004	41950	1.5933
27.4331	42000	1.6955
27.4657	42050	1.4612
27.4984	42100	1.6584
27.5310	42150	1.7636
27.5637	42200	1.6297
27.5963	42250	1.5764
27.6290	42300	1.5077
27.6617	42350	1.5008
27.6943	42400	1.3797
27.7270	42450	1.8068
27.7596	42500	1.6229
27.7923	42550	1.7154
27.8250	42600	1.4650
27.8576	42650	1.5567
27.8903	42700	1.5959
27.9229	42750	1.3661
27.9556	42800	1.5697
27.9882	42850	1.2631
28.0209	42900	1.3554
28.0536	42950	1.5743
28.0862	43000	1.6630
28.1189	43050	1.5044
28.1515	43100	1.5048
28.1842	43150	1.5329
28.2169	43200	1.6174
28.2495	43250	1.4120
28.2822	43300	1.4637
28.3148	43350	1.3274
28.3475	43400	1.4406
28.3801	43450	1.7386
28.4128	43500	1.3819
28.4455	43550	1.3553
28.4781	43600	1.3395
28.5108	43650	1.5442
28.5434	43700	1.4133
28.5761	43750	1.5770
28.6088	43800	1.3720
28.6414	43850	1.6503
28.6741	43900	1.5052
28.7067	43950	1.5829
28.7394	44000	1.4876
28.7720	44050	1.7159
28.8047	44100	1.4177
28.8374	44150	1.6509
28.8700	44200	1.6379
28.9027	44250	1.6268
28.9353	44300	1.5226
28.9680	44350	1.4577
29.0007	44400	1.5708
29.0333	44450	1.4214
29.0660	44500	1.5191
29.0986	44550	1.4286
29.1313	44600	1.5108
29.1639	44650	1.3233
29.1966	44700	1.5269
29.2293	44750	1.6637
29.2619	44800	1.3354
29.2946	44850	1.4442
29.3272	44900	1.6092
29.3599	44950	1.5458
29.3926	45000	1.6602
29.4252	45050	1.5026
29.4579	45100	1.5239
29.4905	45150	1.8195
29.5232	45200	1.4508
29.5558	45250	1.5922
29.5885	45300	1.4541
29.6212	45350	1.6059
29.6538	45400	1.5429
29.6865	45450	1.4189
29.7191	45500	1.3880
29.7518	45550	1.3489
29.7845	45600	1.4347
29.8171	45650	1.6756
29.8498	45700	1.6004
29.8824	45750	1.4929
29.9151	45800	1.5354
29.9477	45850	1.5486
29.9804	45900	1.5539
30.0131	45950	1.5889
30.0457	46000	1.5380
30.0784	46050	1.4323
30.1110	46100	1.4169
30.1437	46150	1.6017
30.1764	46200	1.5644
30.2090	46250	1.4206
30.2417	46300	1.5065
30.2743	46350	1.5961
30.3070	46400	1.4240
30.3396	46450	1.5114
30.3723	46500	1.5302
30.4050	46550	1.6155
30.4376	46600	1.4247
30.4703	46650	1.6018
30.5029	46700	1.4195
30.5356	46750	1.4429
30.5683	46800	1.6439
30.6009	46850	1.4760
30.6336	46900	1.5512
30.6662	46950	1.4387
30.6989	47000	1.5118
30.7315	47050	1.4090
30.7642	47100	1.4199
30.7969	47150	1.3875
30.8295	47200	1.5236
30.8622	47250	1.2795
30.8948	47300	1.2937
30.9275	47350	1.5257
30.9602	47400	1.6123
30.9928	47450	1.6460
31.0255	47500	1.3051
31.0581	47550	1.4466
31.0908	47600	1.5853
31.1234	47650	1.5679
31.1561	47700	1.4663
31.1888	47750	1.4773
31.2214	47800	1.5283
31.2541	47850	1.3459
31.2867	47900	1.7697
31.3194	47950	1.5063
31.3521	48000	1.5190
31.3847	48050	1.5872
31.4174	48100	1.5069
31.4500	48150	1.4471
31.4827	48200	1.5093
31.5153	48250	1.3827
31.5480	48300	1.6809
31.5807	48350	1.4950
31.6133	48400	1.4291
31.6460	48450	1.7661
31.6786	48500	1.4973
31.7113	48550	1.5540
31.7440	48600	1.5741
31.7766	48650	1.4359
31.8093	48700	1.5934
31.8419	48750	1.3078
31.8746	48800	1.4380
31.9073	48850	1.3816
31.9399	48900	1.3888
31.9726	48950	1.6013

Framework Versions

Python: 3.13.1
Sentence Transformers: 5.2.3
Transformers: 5.2.0
PyTorch: 2.10.0+cu126
Accelerate: 1.13.0
Datasets: 4.8.3
Tokenizers: 0.22.2

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",
}

AnglELoss

@inproceedings{li-li-2024-aoe,
    title = "{A}o{E}: Angle-optimized Embeddings for Semantic Textual Similarity",
    author = "Li, Xianming and Li, Jing",
    year = "2024",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.acl-long.101/",
    doi = "10.18653/v1/2024.acl-long.101"
}