This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2. It maps sentences & paragraphs to a 384-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: sentence-transformers/all-MiniLM-L6-v2
Maximum Sequence Length: 256 tokens
Output Dimensionality: 384 tokens
Similarity Function: Cosine Similarity

Language: en
License: apache-2.0

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: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, '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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'cheese chicken',
    'bread broast meal',
    'la citta mozzarella cheese',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

Training Details

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 128
per_device_eval_batch_size: 128
learning_rate: 2e-05
num_train_epochs: 1
warmup_ratio: 0.1
fp16: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 128
per_device_eval_batch_size: 128
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_steps: None
learning_rate: 2e-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: 1
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: True
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
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss
0.0013	100	11.6158
0.0026	200	11.2267
0.0039	300	10.8062
0.0051	400	10.6318
0.0064	500	10.474
0.0077	600	9.8674
0.0090	700	9.5482
0.0103	800	9.0193
0.0116	900	8.7481
0.0129	1000	8.3555
0.0142	1100	8.0735
0.0154	1200	7.7894
0.0167	1300	7.5994
0.0180	1400	7.3892
0.0193	1500	7.2224
0.0206	1600	7.0969
0.0219	1700	6.9532
0.0232	1800	6.9266
0.0244	1900	6.7354
0.0257	2000	6.5991
0.0270	2100	6.4943
0.0283	2200	6.3841
0.0296	2300	6.3595
0.0309	2400	6.2125
0.0322	2500	6.2191
0.0334	2600	6.1639
0.0347	2700	6.0397
0.0360	2800	5.9348
0.0373	2900	5.864
0.0386	3000	5.7838
0.0399	3100	5.7405
0.0412	3200	5.7592
0.0425	3300	5.5271
0.0437	3400	5.7894
0.0450	3500	5.5435
0.0463	3600	5.1175
0.0476	3700	5.4116
0.0489	3800	5.3039
0.0502	3900	5.2913
0.0515	4000	5.3434
0.0527	4100	5.4459
0.0540	4200	5.0356
0.0553	4300	4.9882
0.0566	4400	4.9577
0.0579	4500	4.8138
0.0592	4600	4.984
0.0605	4700	4.9253
0.0617	4800	5.0222
0.0630	4900	4.795
0.0643	5000	4.9672
0.0656	5100	4.7558
0.0669	5200	4.7199
0.0682	5300	4.5787
0.0695	5400	4.7462
0.0708	5500	4.5001
0.0720	5600	4.5065
0.0733	5700	4.3544
0.0746	5800	4.5702
0.0759	5900	4.3736
0.0772	6000	4.2496
0.0785	6100	4.1923
0.0798	6200	4.2192
0.0810	6300	4.4481
0.0823	6400	4.3003
0.0836	6500	4.1875
0.0849	6600	4.3013
0.0862	6700	4.2941
0.0875	6800	4.2107
0.0888	6900	4.1013
0.0900	7000	4.0385
0.0913	7100	4.3636
0.0926	7200	3.9524
0.0939	7300	3.9388
0.0952	7400	3.9949
0.0965	7500	3.5256
0.0978	7600	3.9582
0.0991	7700	3.8467
0.1003	7800	3.6175
0.1016	7900	3.9818
0.1029	8000	3.7575
0.1042	8100	3.8338
0.1055	8200	3.5245
0.1068	8300	3.639
0.1081	8400	3.419
0.1093	8500	3.6876
0.1106	8600	3.5471
0.1119	8700	3.5832
0.1132	8800	3.488
0.1145	8900	3.2942
0.1158	9000	3.4713
0.1171	9100	3.5447
0.1184	9200	3.5261
0.1196	9300	3.4973
0.1209	9400	3.247
0.1222	9500	3.3903
0.1235	9600	3.3588
0.1248	9700	3.329
0.1261	9800	3.1164
0.1274	9900	3.2872
0.1286	10000	3.3814
0.1299	10100	3.5534
0.1312	10200	3.5506
0.1325	10300	3.0623
0.1338	10400	3.0977
0.1351	10500	3.075
0.1364	10600	3.1087
0.1376	10700	3.2012
0.1389	10800	3.0618
0.1402	10900	3.111
0.1415	11000	3.0688
0.1428	11100	3.0018
0.1441	11200	2.8335
0.1454	11300	2.9481
0.1467	11400	3.0893
0.1479	11500	3.2911
0.1492	11600	2.7548
0.1505	11700	3.1455
0.1518	11800	3.0931
0.1531	11900	2.9134
0.1544	12000	2.9433
0.1557	12100	3.2991
0.1569	12200	2.6546
0.1582	12300	2.565
0.1595	12400	2.9691
0.1608	12500	3.0197
0.1621	12600	2.6063
0.1634	12700	2.833
0.1647	12800	2.6514
0.1659	12900	2.4589
0.1672	13000	3.0107
0.1685	13100	2.484
0.1698	13200	2.6073
0.1711	13300	2.5912
0.1724	13400	2.742
0.1737	13500	2.6607
0.1750	13600	2.8503
0.1762	13700	2.4531
0.1775	13800	2.77
0.1788	13900	2.5851
0.1801	14000	2.6802
0.1814	14100	2.6594
0.1827	14200	2.589
0.1840	14300	2.4014
0.1852	14400	2.6958
0.1865	14500	2.7391
0.1878	14600	2.2975
0.1891	14700	2.3556
0.1904	14800	2.7232
0.1917	14900	2.8015
0.1930	15000	2.3563
0.1942	15100	2.7878
0.1955	15200	2.4123
0.1968	15300	2.3573
0.1981	15400	2.4662
0.1994	15500	2.5693
0.2007	15600	2.4088
0.2020	15700	2.0045
0.2033	15800	2.2505
0.2045	15900	2.4698
0.2058	16000	2.5727
0.2071	16100	2.5563
0.2084	16200	2.2802
0.2097	16300	2.3056
0.2110	16400	2.6633
0.2123	16500	2.7207
0.2135	16600	1.7251
0.2148	16700	2.2006
0.2161	16800	2.3847
0.2174	16900	2.2348
0.2187	17000	2.1206
0.2200	17100	2.3809
0.2213	17200	2.3109
0.2226	17300	2.6169
0.2238	17400	2.2302
0.2251	17500	1.9437
0.2264	17600	1.9098
0.2277	17700	2.3483
0.2290	17800	2.0433
0.2303	17900	2.1697
0.2316	18000	2.0189
0.2328	18100	2.4019
0.2341	18200	2.199
0.2354	18300	2.2687
0.2367	18400	2.0452
0.2380	18500	1.8169
0.2393	18600	2.1265
0.2406	18700	2.1813
0.2418	18800	1.8364
0.2431	18900	2.0735
0.2444	19000	1.8744
0.2457	19100	2.3403
0.2470	19200	1.7847
0.2483	19300	1.9472
0.2496	19400	2.0642
0.2509	19500	2.3265
0.2521	19600	1.9527
0.2534	19700	2.153
0.2547	19800	2.3579
0.2560	19900	1.7197
0.2573	20000	1.768
0.2586	20100	2.3296
0.2599	20200	2.1719
0.2611	20300	1.9499
0.2624	20400	1.9302
0.2637	20500	2.2728
0.2650	20600	1.753
0.2663	20700	2.044
0.2676	20800	1.7346
0.2689	20900	1.9699
0.2701	21000	1.7828
0.2714	21100	2.1586
0.2727	21200	1.8986
0.2740	21300	2.1792
0.2753	21400	1.8915
0.2766	21500	1.8125
0.2779	21600	1.7926
0.2792	21700	1.6121
0.2804	21800	1.7718
0.2817	21900	1.7177
0.2830	22000	1.6762
0.2843	22100	1.8338
0.2856	22200	1.7119
0.2869	22300	2.0222
0.2882	22400	1.9919
0.2894	22500	1.831
0.2907	22600	1.6744
0.2920	22700	1.8928
0.2933	22800	1.9069
0.2946	22900	1.7777
0.2959	23000	1.9121
0.2972	23100	2.1006
0.2984	23200	1.4984
0.2997	23300	2.0935
0.3010	23400	1.5027
0.3023	23500	1.4537
0.3036	23600	1.8411
0.3049	23700	2.266
0.3062	23800	1.9203
0.3075	23900	1.8136
0.3087	24000	1.5086
0.3100	24100	1.8985
0.3113	24200	1.5029
0.3126	24300	1.9142
0.3139	24400	2.0408
0.3152	24500	1.5039
0.3165	24600	1.7824
0.3177	24700	1.9319
0.3190	24800	1.6635
0.3203	24900	1.6966
0.3216	25000	1.5839
0.3229	25100	1.6846
0.3242	25200	1.9096
0.3255	25300	1.6172
0.3268	25400	1.7866
0.3280	25500	2.0234
0.3293	25600	1.4119
0.3306	25700	1.5748
0.3319	25800	2.238
0.3332	25900	1.7175
0.3345	26000	1.5788
0.3358	26100	1.8376
0.3370	26200	1.4764
0.3383	26300	1.7255
0.3396	26400	1.4659
0.3409	26500	1.8362
0.3422	26600	1.867
0.3435	26700	1.9681
0.3448	26800	1.6144
0.3460	26900	1.5201
0.3473	27000	1.4705
0.3486	27100	1.6603
0.3499	27200	1.7817
0.3512	27300	1.6453
0.3525	27400	1.5808
0.3538	27500	1.3525
0.3551	27600	2.1304
0.3563	27700	1.5801
0.3576	27800	1.6616
0.3589	27900	1.7675
0.3602	28000	1.8379
0.3615	28100	1.4156
0.3628	28200	1.7096
0.3641	28300	1.3778
0.3653	28400	1.7096
0.3666	28500	1.4561
0.3679	28600	1.8914
0.3692	28700	1.3892
0.3705	28800	1.5533
0.3718	28900	1.7548
0.3731	29000	1.5122
0.3743	29100	1.5438
0.3756	29200	1.9568
0.3769	29300	1.4259
0.3782	29400	1.263
0.3795	29500	1.5855
0.3808	29600	1.4765
0.3821	29700	1.5449
0.3834	29800	1.5552
0.3846	29900	1.257
0.3859	30000	1.4308
0.3872	30100	2.1056
0.3885	30200	1.7317
0.3898	30300	1.1246
0.3911	30400	1.4522
0.3924	30500	1.653
0.3936	30600	1.3639
0.3949	30700	1.8574
0.3962	30800	1.8059
0.3975	30900	1.7438
0.3988	31000	1.5563
0.4001	31100	1.8731
0.4014	31200	2.0423
0.4027	31300	1.3474
0.4039	31400	1.2572
0.4052	31500	1.4148
0.4065	31600	1.8187
0.4078	31700	1.4591
0.4091	31800	1.2913
0.4104	31900	1.8988
0.4117	32000	1.3661
0.4129	32100	1.3375
0.4142	32200	1.5449
0.4155	32300	1.8418
0.4168	32400	1.2645
0.4181	32500	1.5022
0.4194	32600	1.2937
0.4207	32700	1.5676
0.4219	32800	1.1308
0.4232	32900	1.4277
0.4245	33000	1.538
0.4258	33100	1.6725
0.4271	33200	1.1943
0.4284	33300	1.1987
0.4297	33400	1.8072
0.4310	33500	1.7707
0.4322	33600	1.5222
0.4335	33700	1.2665
0.4348	33800	1.474
0.4361	33900	1.4759
0.4374	34000	1.2904
0.4387	34100	1.6051
0.4400	34200	1.2015
0.4412	34300	1.6001
0.4425	34400	1.308
0.4438	34500	1.3701
0.4451	34600	1.2928
0.4464	34700	1.6035
0.4477	34800	1.839
0.4490	34900	1.0386
0.4502	35000	1.5857
0.4515	35100	1.2452
0.4528	35200	1.2076
0.4541	35300	1.4913
0.4554	35400	1.3709
0.4567	35500	1.4901
0.4580	35600	1.4157
0.4593	35700	1.5487
0.4605	35800	1.553
0.4618	35900	1.4667
0.4631	36000	1.7429
0.4644	36100	1.2109
0.4657	36200	1.4929
0.4670	36300	1.5324
0.4683	36400	1.7022
0.4695	36500	1.3955
0.4708	36600	1.5456
0.4721	36700	1.5259
0.4734	36800	1.2546
0.4747	36900	1.7708
0.4760	37000	1.1857
0.4773	37100	1.4834
0.4785	37200	1.5092
0.4798	37300	1.5533
0.4811	37400	1.374
0.4824	37500	1.4281
0.4837	37600	1.4387
0.4850	37700	1.2226
0.4863	37800	1.443
0.4876	37900	1.6551
0.4888	38000	1.908
0.4901	38100	1.1089
0.4914	38200	1.333
0.4927	38300	1.5341
0.4940	38400	1.4073
0.4953	38500	1.5042
0.4966	38600	1.1962
0.4978	38700	1.1076
0.4991	38800	1.1288
0.5004	38900	1.2185
0.5017	39000	1.0422
0.5030	39100	1.4606
0.5043	39200	1.4474
0.5056	39300	1.3795
0.5069	39400	1.3372
0.5081	39500	1.1919
0.5094	39600	1.3421
0.5107	39700	1.0574
0.5120	39800	1.3915
0.5133	39900	1.1913
0.5146	40000	1.5687
0.5159	40100	1.6509
0.5171	40200	1.3056
0.5184	40300	1.0561
0.5197	40400	1.492
0.5210	40500	1.5173
0.5223	40600	1.322
0.5236	40700	1.3093
0.5249	40800	1.5409
0.5261	40900	1.1885
0.5274	41000	1.2244
0.5287	41100	1.0398
0.5300	41200	0.9035
0.5313	41300	1.5522
0.5326	41400	1.299
0.5339	41500	1.2729
0.5352	41600	1.2057
0.5364	41700	1.0893
0.5377	41800	1.6176
0.5390	41900	1.2485
0.5403	42000	1.2309
0.5416	42100	1.2515
0.5429	42200	1.1518
0.5442	42300	1.2846
0.5454	42400	1.4233
0.5467	42500	1.5888
0.5480	42600	1.0648
0.5493	42700	1.43
0.5506	42800	1.4847
0.5519	42900	1.0948
0.5532	43000	1.0787
0.5544	43100	1.3424
0.5557	43200	1.2119
0.5570	43300	1.1852
0.5583	43400	1.1024
0.5596	43500	1.0098
0.5609	43600	1.1845
0.5622	43700	1.377
0.5635	43800	0.9749
0.5647	43900	1.1367
0.5660	44000	1.251
0.5673	44100	1.3596
0.5686	44200	1.4161
0.5699	44300	1.0059
0.5712	44400	1.3807
0.5725	44500	0.9023
0.5737	44600	1.763
0.5750	44700	1.1855
0.5763	44800	1.1133
0.5776	44900	1.2322
0.5789	45000	1.2915
0.5802	45100	1.07
0.5815	45200	1.0674
0.5827	45300	0.7163
0.5840	45400	1.2879
0.5853	45500	1.1536
0.5866	45600	1.0275
0.5879	45700	1.3062
0.5892	45800	1.0344
0.5905	45900	1.2083
0.5918	46000	1.1431
0.5930	46100	1.214
0.5943	46200	1.1516
0.5956	46300	0.8538
0.5969	46400	1.353
0.5982	46500	1.5735
0.5995	46600	1.113
0.6008	46700	1.6966
0.6020	46800	1.2462
0.6033	46900	0.9537
0.6046	47000	1.5226
0.6059	47100	1.0129
0.6072	47200	1.271
0.6085	47300	1.2789
0.6098	47400	1.1382
0.6111	47500	1.1972
0.6123	47600	1.7806
0.6136	47700	1.1526
0.6149	47800	1.2149
0.6162	47900	1.3888
0.6175	48000	0.8663
0.6188	48100	1.1928
0.6201	48200	1.4148
0.6213	48300	1.4242
0.6226	48400	1.2628
0.6239	48500	1.3511
0.6252	48600	0.9856
0.6265	48700	1.4032
0.6278	48800	1.0183
0.6291	48900	1.015
0.6303	49000	0.9017
0.6316	49100	0.8986
0.6329	49200	1.1282
0.6342	49300	1.1541
0.6355	49400	1.241
0.6368	49500	1.1044
0.6381	49600	1.4654
0.6394	49700	1.1307
0.6406	49800	1.3193
0.6419	49900	1.1389
0.6432	50000	1.0622
0.6445	50100	1.2068
0.6458	50200	1.1104
0.6471	50300	1.7188
0.6484	50400	0.9865
0.6496	50500	0.9151
0.6509	50600	0.8961
0.6522	50700	0.984
0.6535	50800	0.8511
0.6548	50900	1.1632
0.6561	51000	1.1841
0.6574	51100	1.2734
0.6586	51200	0.9312
0.6599	51300	0.6785
0.6612	51400	1.1188
0.6625	51500	0.7932
0.6638	51600	0.9683
0.6651	51700	1.0488
0.6664	51800	0.8825
0.6677	51900	1.3566
0.6689	52000	1.2847
0.6702	52100	1.2242
0.6715	52200	1.2293
0.6728	52300	1.1863
0.6741	52400	1.7708
0.6754	52500	1.1689
0.6767	52600	1.013
0.6779	52700	1.168
0.6792	52800	1.3889
0.6805	52900	1.1024
0.6818	53000	0.9713
0.6831	53100	1.5676
0.6844	53200	0.9941
0.6857	53300	1.277
0.6869	53400	1.1613
0.6882	53500	1.2286
0.6895	53600	1.384
0.6908	53700	1.2483
0.6921	53800	1.3603
0.6934	53900	1.0268
0.6947	54000	0.8918
0.6960	54100	1.3338
0.6972	54200	1.0215
0.6985	54300	1.17
0.6998	54400	1.296
0.7011	54500	1.0897
0.7024	54600	1.1549
0.7037	54700	0.9949
0.7050	54800	0.8423
0.7062	54900	1.3039
0.7075	55000	1.2349
0.7088	55100	0.979
0.7101	55200	0.7874
0.7114	55300	0.9681
0.7127	55400	0.8862
0.7140	55500	0.9906
0.7153	55600	1.0126
0.7165	55700	1.0305
0.7178	55800	0.9663
0.7191	55900	1.0466
0.7204	56000	1.0965
0.7217	56100	1.1585
0.7230	56200	1.2356
0.7243	56300	1.0146
0.7255	56400	1.2187
0.7268	56500	0.9852
0.7281	56600	1.1142
0.7294	56700	1.1876
0.7307	56800	1.1076
0.7320	56900	0.902
0.7333	57000	1.0966
0.7345	57100	1.0078
0.7358	57200	1.0964
0.7371	57300	1.3122
0.7384	57400	1.2366
0.7397	57500	1.1743
0.7410	57600	1.2079
0.7423	57700	1.1459
0.7436	57800	0.8328
0.7448	57900	1.3736
0.7461	58000	1.2092
0.7474	58100	0.8868
0.7487	58200	0.9862
0.7500	58300	1.0948
0.7513	58400	1.0208
0.7526	58500	1.0877
0.7538	58600	0.9753
0.7551	58700	1.3271
0.7564	58800	0.8259
0.7577	58900	1.0397
0.7590	59000	1.204
0.7603	59100	0.72
0.7616	59200	0.943
0.7628	59300	1.1914
0.7641	59400	1.2826
0.7654	59500	1.1414
0.7667	59600	1.396
0.7680	59700	1.449
0.7693	59800	1.2457
0.7706	59900	1.3631
0.7719	60000	0.8453
0.7731	60100	1.2109
0.7744	60200	0.9854
0.7757	60300	0.7796
0.7770	60400	1.4481
0.7783	60500	1.5467
0.7796	60600	0.8859
0.7809	60700	1.1519
0.7821	60800	1.0033
0.7834	60900	1.2377
0.7847	61000	1.1725
0.7860	61100	0.8958
0.7873	61200	1.5263
0.7886	61300	0.8942
0.7899	61400	1.3225
0.7911	61500	0.9094
0.7924	61600	1.0698
0.7937	61700	0.7564
0.7950	61800	1.1038
0.7963	61900	1.2544
0.7976	62000	1.2492
0.7989	62100	0.9651
0.8002	62200	1.2426
0.8014	62300	0.9196
0.8027	62400	1.1564
0.8040	62500	0.8972
0.8053	62600	0.8804
0.8066	62700	0.99
0.8079	62800	1.0681
0.8092	62900	0.7984
0.8104	63000	1.0707
0.8117	63100	1.1273
0.8130	63200	0.8897
0.8143	63300	1.1728
0.8156	63400	1.1743
0.8169	63500	1.1298
0.8182	63600	1.2757
0.8195	63700	0.9797
0.8207	63800	0.979
0.8220	63900	0.8001
0.8233	64000	1.2516
0.8246	64100	1.0874
0.8259	64200	1.2233
0.8272	64300	1.0861
0.8285	64400	0.9833
0.8297	64500	0.7748
0.8310	64600	1.2968
0.8323	64700	0.9834
0.8336	64800	1.0883
0.8349	64900	0.8187
0.8362	65000	1.0442
0.8375	65100	0.8298
0.8387	65200	1.1203
0.8400	65300	1.3857
0.8413	65400	1.0832
0.8426	65500	0.9154
0.8439	65600	1.0595
0.8452	65700	0.9521
0.8465	65800	1.0768
0.8478	65900	1.1611
0.8490	66000	1.2215
0.8503	66100	1.3333
0.8516	66200	0.8955
0.8529	66300	1.1285
0.8542	66400	0.8445
0.8555	66500	1.2692
0.8568	66600	1.3413
0.8580	66700	0.9388
0.8593	66800	1.0097
0.8606	66900	1.1725
0.8619	67000	0.868
0.8632	67100	0.9591
0.8645	67200	1.051
0.8658	67300	1.2206
0.8670	67400	1.1151
0.8683	67500	1.0081
0.8696	67600	1.238
0.8709	67700	0.9283
0.8722	67800	0.8127
0.8735	67900	1.3822
0.8748	68000	0.801
0.8761	68100	1.2545
0.8773	68200	1.0514
0.8786	68300	0.7905
0.8799	68400	1.2604
0.8812	68500	1.0444
0.8825	68600	0.7341
0.8838	68700	1.0117
0.8851	68800	0.8016
0.8863	68900	1.0506
0.8876	69000	1.0956
0.8889	69100	0.9481
0.8902	69200	1.2625
0.8915	69300	1.1086
0.8928	69400	1.0572
0.8941	69500	0.8213
0.8953	69600	1.0622
0.8966	69700	1.1873
0.8979	69800	1.1238
0.8992	69900	1.2476
0.9005	70000	1.1117
0.9018	70100	1.2289
0.9031	70200	0.765
0.9044	70300	0.6267
0.9056	70400	1.2199
0.9069	70500	1.1491
0.9082	70600	1.3097
0.9095	70700	1.2007
0.9108	70800	0.6837
0.9121	70900	0.9657
0.9134	71000	0.9577
0.9146	71100	0.9892
0.9159	71200	1.1105
0.9172	71300	1.0915
0.9185	71400	0.7967
0.9198	71500	0.9847
0.9211	71600	1.1467
0.9224	71700	0.9292
0.9237	71800	1.1285
0.9249	71900	1.1171
0.9262	72000	0.7025
0.9275	72100	0.9062
0.9288	72200	0.9001
0.9301	72300	0.8685
0.9314	72400	0.9424
0.9327	72500	0.7868
0.9339	72600	1.1204
0.9352	72700	1.0016
0.9365	72800	1.3201
0.9378	72900	1.3654
0.9391	73000	0.9326
0.9404	73100	0.942
0.9417	73200	1.1605
0.9429	73300	0.8674
0.9442	73400	1.5539
0.9455	73500	1.6419
0.9468	73600	0.8481
0.9481	73700	0.7011
0.9494	73800	0.9495
0.9507	73900	1.0381
0.9520	74000	1.0113
0.9532	74100	1.1041
0.9545	74200	1.1624
0.9558	74300	1.2322
0.9571	74400	0.99
0.9584	74500	1.3082
0.9597	74600	0.7011
0.9610	74700	1.5987
0.9622	74800	0.7559
0.9635	74900	1.3304
0.9648	75000	1.0202
0.9661	75100	0.8978
0.9674	75200	1.1115
0.9687	75300	0.8045
0.9700	75400	0.9921
0.9712	75500	0.7944
0.9725	75600	0.8674
0.9738	75700	0.8214
0.9751	75800	1.0144
0.9764	75900	0.9535
0.9777	76000	0.9878
0.9790	76100	1.1341
0.9803	76200	0.8596
0.9815	76300	1.2648
0.9828	76400	0.7322
0.9841	76500	1.0803
0.9854	76600	0.8237
0.9867	76700	0.8101
0.9880	76800	0.618
0.9893	76900	0.973
0.9905	77000	1.2514
0.9918	77100	1.179
0.9931	77200	1.1761
0.9944	77300	0.9844
0.9957	77400	0.7929
0.9970	77500	0.7858
0.9983	77600	1.0505
0.9995	77700	0.8098

Framework Versions

Python: 3.8.20
Sentence Transformers: 3.2.1
Transformers: 4.41.0
PyTorch: 2.4.1+cu121
Accelerate: 1.0.1
Datasets: 3.0.1
Tokenizers: 0.19.1

Citation

BibTeX

Sentence Transformers

bibtex

@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",
}

CoSENTLoss