This is a sentence-transformers model finetuned from Alibaba-NLP/gte-multilingual-base. 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: Alibaba-NLP/gte-multilingual-base
Maximum Sequence Length: 8192 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': 8192, 'do_lower_case': False}) with Transformer model: NewModel 
  (1): Pooling({'word_embedding_dimension': 768, '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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("lucian-li/my_new_model")
# Run inference
sentences = [
    'Carrier aggregation measurement accuracy',
    'Reference Signal Time Difference (RSTD) Measurement Accuracy\nRequirements for Carrier Aggregation\nA.8\nUE Measurements Procedures\nA.9\nMeasurement Performance Requirements\nNOTE:\nOnly requirements and test cases in this table defined for inter-band carrier aggregation shall apply.\n\n\nETSI\nETSI TS 136 307 V10.17.0 (2016-01)',
    'Operator control\nThree general architectures are candidates to offer energy savings functionalities:\nDistributed, NM-Centralized, EM-Centralized as defined in TS 32.500 [6].\nEnergy savings in cells can be initiated in several different ways. Some of the mechanisms are:\nFor NM-centralized architecture\n-\nIRPManager instructs the cells to move to energySaving state (e.g. according to a schedule determined by\nnetwork statistics) , configures trigger points (e.g. load threshold crossing) when it want',
]
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

Unnamed Dataset

Size: 583,058 training samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 7 tokens
mean: 85.73 tokens
max: 229 tokens
min: 7 tokens
mean: 85.86 tokens
max: 229 tokens

Samples:

anchor	positive
`Triggering Optimization Function (TG_F)[object Object]This functional bloc supports the following functions: [SO2], [SO3].`	`Optimization Fallback Function (O_FB_F)[object Object]This functional bloc supports the following functions: [SO7], [SO9], [SO10].`
`Optimization Fallback Function (O_FB_F)[object Object]This functional bloc supports the following functions: [SO7], [SO9], [SO10].`	`Self-Optimization Progress Update Function (SO_PGS_UF)[object Object]This function updates the self-optimization progress and important events to the operator: [SO11]`
`Self-Optimization Progress Update Function (SO_PGS_UF)[object Object]This function updates the self-optimization progress and important events to the operator: [SO11]`	`NRM IRP Update Function (NRM_UF)[object Object]This function updates the E-UTRAN and EPC NRM IRP with the optimization modification if needed.`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 11
num_train_epochs: 1
warmup_ratio: 0.1

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: no
prediction_loss_only: True
per_device_train_batch_size: 11
per_device_eval_batch_size: 8
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: 1.0
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: 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}
tp_size: 0
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
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: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss
0.0019	100	0.8198
0.0038	200	0.7651
0.0057	300	0.6659
0.0075	400	0.6404
0.0094	500	0.5638
0.0113	600	0.5184
0.0132	700	0.448
0.0151	800	0.4464
0.0170	900	0.3461
0.0189	1000	0.3731
0.0208	1100	0.343
0.0226	1200	0.3557
0.0245	1300	0.3623
0.0264	1400	0.2941
0.0283	1500	0.3153
0.0302	1600	0.2724
0.0321	1700	0.2702
0.0340	1800	0.2934
0.0358	1900	0.2255
0.0377	2000	0.2519
0.0396	2100	0.2424
0.0415	2200	0.1883
0.0434	2300	0.2428
0.0453	2400	0.2212
0.0472	2500	0.1862
0.0491	2600	0.2451
0.0509	2700	0.2336
0.0528	2800	0.225
0.0547	2900	0.2154
0.0566	3000	0.1907
0.0585	3100	0.2514
0.0604	3200	0.2082
0.0623	3300	0.2076
0.0641	3400	0.1818
0.0660	3500	0.1688
0.0679	3600	0.2261
0.0698	3700	0.2108
0.0717	3800	0.1732
0.0736	3900	0.1764
0.0755	4000	0.1481
0.0773	4100	0.1687
0.0792	4200	0.1897
0.0811	4300	0.1685
0.0830	4400	0.1915
0.0849	4500	0.2013
0.0868	4600	0.1701
0.0887	4700	0.2006
0.0906	4800	0.2006
0.0924	4900	0.1617
0.0943	5000	0.1406
0.0962	5100	0.1456
0.0981	5200	0.1703
0.1000	5300	0.1464
0.1019	5400	0.1803
0.1038	5500	0.1346
0.1056	5600	0.134
0.1075	5700	0.1567
0.1094	5800	0.163
0.1113	5900	0.1544
0.1132	6000	0.1648
0.1151	6100	0.1505
0.1170	6200	0.1231
0.1189	6300	0.1591
0.1207	6400	0.1533
0.1226	6500	0.1376
0.1245	6600	0.1473
0.1264	6700	0.1405
0.1283	6800	0.141
0.1302	6900	0.1105
0.1321	7000	0.1712
0.1339	7100	0.1534
0.1358	7200	0.1578
0.1377	7300	0.1101
0.1396	7400	0.128
0.1415	7500	0.1679
0.1434	7600	0.1592
0.1453	7700	0.1383
0.1472	7800	0.1274
0.1490	7900	0.1616
0.1509	8000	0.1617
0.1528	8100	0.1361
0.1547	8200	0.1268
0.1566	8300	0.1286
0.1585	8400	0.1253
0.1604	8500	0.1157
0.1622	8600	0.1499
0.1641	8700	0.1398
0.1660	8800	0.1188
0.1679	8900	0.1103
0.1698	9000	0.1217
0.1717	9100	0.1144
0.1736	9200	0.1203
0.1755	9300	0.1074
0.1773	9400	0.1145
0.1792	9500	0.1035
0.1811	9600	0.1406
0.1830	9700	0.1465
0.1849	9800	0.1169
0.1868	9900	0.1115
0.1887	10000	0.1207
0.1905	10100	0.1191
0.1924	10200	0.1099
0.1943	10300	0.1309
0.1962	10400	0.1092
0.1981	10500	0.1075
0.2000	10600	0.1174
0.2019	10700	0.1103
0.2038	10800	0.1077
0.2056	10900	0.0844
0.2075	11000	0.1093
0.2094	11100	0.1428
0.2113	11200	0.0928
0.2132	11300	0.1039
0.2151	11400	0.1436
0.2170	11500	0.1197
0.2188	11600	0.1249
0.2207	11700	0.0856
0.2226	11800	0.1126
0.2245	11900	0.1028
0.2264	12000	0.0988
0.2283	12100	0.1031
0.2302	12200	0.101
0.2320	12300	0.1188
0.2339	12400	0.0908
0.2358	12500	0.069
0.2377	12600	0.1099
0.2396	12700	0.1227
0.2415	12800	0.0794
0.2434	12900	0.0969
0.2453	13000	0.0864
0.2471	13100	0.1193
0.2490	13200	0.0824
0.2509	13300	0.12
0.2528	13400	0.0928
0.2547	13500	0.1126
0.2566	13600	0.0912
0.2585	13700	0.1126
0.2603	13800	0.078
0.2622	13900	0.0715
0.2641	14000	0.1095
0.2660	14100	0.089
0.2679	14200	0.0926
0.2698	14300	0.086
0.2717	14400	0.1115
0.2736	14500	0.0996
0.2754	14600	0.1014
0.2773	14700	0.1033
0.2792	14800	0.0732
0.2811	14900	0.0994
0.2830	15000	0.0872
0.2849	15100	0.0923
0.2868	15200	0.111
0.2886	15300	0.0891
0.2905	15400	0.0868
0.2924	15500	0.0773
0.2943	15600	0.0918
0.2962	15700	0.0726
0.2981	15800	0.0951
0.3000	15900	0.0835
0.3019	16000	0.083
0.3037	16100	0.095
0.3056	16200	0.0722
0.3075	16300	0.1061
0.3094	16400	0.0902
0.3113	16500	0.0978
0.3132	16600	0.0983
0.3151	16700	0.0808
0.3169	16800	0.0758
0.3188	16900	0.071
0.3207	17000	0.0918
0.3226	17100	0.1011
0.3245	17200	0.079
0.3264	17300	0.0992
0.3283	17400	0.1089
0.3302	17500	0.0904
0.3320	17600	0.0956
0.3339	17700	0.0747
0.3358	17800	0.0961
0.3377	17900	0.0923
0.3396	18000	0.1114
0.3415	18100	0.0689
0.3434	18200	0.1308
0.3452	18300	0.0923
0.3471	18400	0.0756
0.3490	18500	0.0842
0.3509	18600	0.0859
0.3528	18700	0.0903
0.3547	18800	0.084
0.3566	18900	0.0923
0.3584	19000	0.0848
0.3603	19100	0.0812
0.3622	19200	0.0872
0.3641	19300	0.083
0.3660	19400	0.0826
0.3679	19500	0.101
0.3698	19600	0.0804
0.3717	19700	0.0676
0.3735	19800	0.0836
0.3754	19900	0.0711
0.3773	20000	0.0825
0.3792	20100	0.0835
0.3811	20200	0.0816
0.3830	20300	0.0812
0.3849	20400	0.0689
0.3867	20500	0.0627
0.3886	20600	0.0965
0.3905	20700	0.0632
0.3924	20800	0.0945
0.3943	20900	0.0923
0.3962	21000	0.0833
0.3981	21100	0.0537
0.4000	21200	0.0822
0.4018	21300	0.0684
0.4037	21400	0.0807
0.4056	21500	0.0945
0.4075	21600	0.0981
0.4094	21700	0.0748
0.4113	21800	0.0943
0.4132	21900	0.0709
0.4150	22000	0.0551
0.4169	22100	0.0679
0.4188	22200	0.0666
0.4207	22300	0.0976
0.4226	22400	0.0666
0.4245	22500	0.0651
0.4264	22600	0.0803
0.4283	22700	0.068
0.4301	22800	0.0541
0.4320	22900	0.0487
0.4339	23000	0.091
0.4358	23100	0.074
0.4377	23200	0.0733
0.4396	23300	0.0845
0.4415	23400	0.0823
0.4433	23500	0.0561
0.4452	23600	0.0508
0.4471	23700	0.074
0.4490	23800	0.0683
0.4509	23900	0.0797
0.4528	24000	0.0561
0.4547	24100	0.0744
0.4566	24200	0.0638
0.4584	24300	0.0633
0.4603	24400	0.062
0.4622	24500	0.0887
0.4641	24600	0.0908
0.4660	24700	0.0654
0.4679	24800	0.0522
0.4698	24900	0.0851
0.4716	25000	0.0763
0.4735	25100	0.0623
0.4754	25200	0.0712
0.4773	25300	0.0866
0.4792	25400	0.0812
0.4811	25500	0.0706
0.4830	25600	0.0734
0.4849	25700	0.068
0.4867	25800	0.111
0.4886	25900	0.0627
0.4905	26000	0.0459
0.4924	26100	0.0794
0.4943	26200	0.0547
0.4962	26300	0.0779
0.4981	26400	0.0609
0.4999	26500	0.0785
0.5018	26600	0.0722
0.5037	26700	0.0585
0.5056	26800	0.0572
0.5075	26900	0.0636
0.5094	27000	0.0642
0.5113	27100	0.0606
0.5131	27200	0.0725
0.5150	27300	0.0664
0.5169	27400	0.0933
0.5188	27500	0.0486
0.5207	27600	0.0514
0.5226	27700	0.0779
0.5245	27800	0.0614
0.5264	27900	0.0646
0.5282	28000	0.0606
0.5301	28100	0.0453
0.5320	28200	0.0749
0.5339	28300	0.0695
0.5358	28400	0.0897
0.5377	28500	0.0612
0.5396	28600	0.0542
0.5414	28700	0.0504
0.5433	28800	0.0539
0.5452	28900	0.0584
0.5471	29000	0.0552
0.5490	29100	0.076
0.5509	29200	0.0861
0.5528	29300	0.067
0.5547	29400	0.0887
0.5565	29500	0.059
0.5584	29600	0.0484
0.5603	29700	0.0703
0.5622	29800	0.0802
0.5641	29900	0.0805
0.5660	30000	0.0737
0.5679	30100	0.0518
0.5697	30200	0.0517
0.5716	30300	0.0806
0.5735	30400	0.0586
0.5754	30500	0.0491
0.5773	30600	0.0591
0.5792	30700	0.066
0.5811	30800	0.0419
0.5830	30900	0.0517
0.5848	31000	0.0539
0.5867	31100	0.0845
0.5886	31200	0.044
0.5905	31300	0.0597
0.5924	31400	0.0556
0.5943	31500	0.0724
0.5962	31600	0.0465
0.5980	31700	0.0585
0.5999	31800	0.0978
0.6018	31900	0.0657
0.6037	32000	0.0438
0.6056	32100	0.0429
0.6075	32200	0.0629
0.6094	32300	0.0591
0.6113	32400	0.0543
0.6131	32500	0.0502
0.6150	32600	0.0733
0.6169	32700	0.0426
0.6188	32800	0.0626
0.6207	32900	0.0406
0.6226	33000	0.0524
0.6245	33100	0.0619
0.6263	33200	0.0633
0.6282	33300	0.0582
0.6301	33400	0.0852
0.6320	33500	0.0482
0.6339	33600	0.0509
0.6358	33700	0.0626
0.6377	33800	0.0609
0.6396	33900	0.0508
0.6414	34000	0.0486
0.6433	34100	0.0508
0.6452	34200	0.0581
0.6471	34300	0.0409
0.6490	34400	0.0703
0.6509	34500	0.0606
0.6528	34600	0.0517
0.6546	34700	0.0493
0.6565	34800	0.0271
0.6584	34900	0.0337
0.6603	35000	0.0369
0.6622	35100	0.0474
0.6641	35200	0.0562
0.6660	35300	0.0663
0.6678	35400	0.0419
0.6697	35500	0.0766
0.6716	35600	0.0439
0.6735	35700	0.0538
0.6754	35800	0.0512
0.6773	35900	0.0388
0.6792	36000	0.0528
0.6811	36100	0.0489
0.6829	36200	0.0454
0.6848	36300	0.0449
0.6867	36400	0.055
0.6886	36500	0.0344
0.6905	36600	0.0485
0.6924	36700	0.0496
0.6943	36800	0.0705
0.6961	36900	0.0617
0.6980	37000	0.054
0.6999	37100	0.0613
0.7018	37200	0.0549
0.7037	37300	0.0378
0.7056	37400	0.0508
0.7075	37500	0.0613
0.7094	37600	0.0602
0.7112	37700	0.0592
0.7131	37800	0.0441
0.7150	37900	0.0445
0.7169	38000	0.0464
0.7188	38100	0.0537
0.7207	38200	0.0521
0.7226	38300	0.0447
0.7244	38400	0.044
0.7263	38500	0.0506
0.7282	38600	0.043
0.7301	38700	0.0441
0.7320	38800	0.0444
0.7339	38900	0.0416
0.7358	39000	0.0556
0.7377	39100	0.0829
0.7395	39200	0.043
0.7414	39300	0.0366
0.7433	39400	0.0457
0.7452	39500	0.0622
0.7471	39600	0.0353
0.7490	39700	0.0597
0.7509	39800	0.0468
0.7527	39900	0.0418
0.7546	40000	0.0606
0.7565	40100	0.0613
0.7584	40200	0.0654
0.7603	40300	0.046
0.7622	40400	0.034
0.7641	40500	0.0378
0.7660	40600	0.0461
0.7678	40700	0.0404
0.7697	40800	0.0583
0.7716	40900	0.0636
0.7735	41000	0.0537
0.7754	41100	0.0336
0.7773	41200	0.0315
0.7792	41300	0.0536
0.7810	41400	0.0532
0.7829	41500	0.0553
0.7848	41600	0.0458
0.7867	41700	0.0372
0.7886	41800	0.0346
0.7905	41900	0.0419
0.7924	42000	0.0461
0.7942	42100	0.0517
0.7961	42200	0.0574
0.7980	42300	0.0411
0.7999	42400	0.0389
0.8018	42500	0.0578
0.8037	42600	0.0637
0.8056	42700	0.0434
0.8075	42800	0.0776
0.8093	42900	0.0644
0.8112	43000	0.0537
0.8131	43100	0.0519
0.8150	43200	0.0241
0.8169	43300	0.0295
0.8188	43400	0.0618
0.8207	43500	0.0275
0.8225	43600	0.0605
0.8244	43700	0.0414
0.8263	43800	0.0446
0.8282	43900	0.0449
0.8301	44000	0.0558
0.8320	44100	0.0336
0.8339	44200	0.0555
0.8358	44300	0.0399
0.8376	44400	0.0319
0.8395	44500	0.0331
0.8414	44600	0.0415
0.8433	44700	0.0424
0.8452	44800	0.0287
0.8471	44900	0.044
0.8490	45000	0.0375
0.8508	45100	0.032
0.8527	45200	0.0406
0.8546	45300	0.0429
0.8565	45400	0.0727
0.8584	45500	0.05
0.8603	45600	0.0436
0.8622	45700	0.0401
0.8641	45800	0.0312
0.8659	45900	0.036
0.8678	46000	0.0558
0.8697	46100	0.0436
0.8716	46200	0.0517
0.8735	46300	0.0361
0.8754	46400	0.038
0.8773	46500	0.0418
0.8791	46600	0.0407
0.8810	46700	0.0336
0.8829	46800	0.0559
0.8848	46900	0.0488
0.8867	47000	0.0463
0.8886	47100	0.0504
0.8905	47200	0.0414
0.8924	47300	0.0428
0.8942	47400	0.0389
0.8961	47500	0.0422
0.8980	47600	0.0533
0.8999	47700	0.0386
0.9018	47800	0.0672
0.9037	47900	0.0505
0.9056	48000	0.0632
0.9074	48100	0.0263
0.9093	48200	0.0448
0.9112	48300	0.0413
0.9131	48400	0.0532
0.9150	48500	0.0503
0.9169	48600	0.0472
0.9188	48700	0.0255
0.9207	48800	0.035
0.9225	48900	0.0353
0.9244	49000	0.0407
0.9263	49100	0.0154
0.9282	49200	0.0535
0.9301	49300	0.0435
0.9320	49400	0.0461
0.9339	49500	0.0288
0.9357	49600	0.0366
0.9376	49700	0.0411
0.9395	49800	0.0605
0.9414	49900	0.0551
0.9433	50000	0.0297
0.9452	50100	0.0388
0.9471	50200	0.0402
0.9489	50300	0.0321
0.9508	50400	0.0538
0.9527	50500	0.036
0.9546	50600	0.0318
0.9565	50700	0.0398
0.9584	50800	0.0405
0.9603	50900	0.0408
0.9622	51000	0.0485
0.9640	51100	0.047
0.9659	51200	0.0452
0.9678	51300	0.0469
0.9697	51400	0.0473
0.9716	51500	0.039
0.9735	51600	0.0579
0.9754	51700	0.0332
0.9772	51800	0.0322
0.9791	51900	0.0324
0.9810	52000	0.035
0.9829	52100	0.0517
0.9848	52200	0.0275
0.9867	52300	0.0466
0.9886	52400	0.0452
0.9905	52500	0.0446
0.9923	52600	0.0357
0.9942	52700	0.0368
0.9961	52800	0.0365
0.9980	52900	0.0303
0.9999	53000	0.0288

Framework Versions

Python: 3.11.12
Sentence Transformers: 3.4.1
Transformers: 4.51.1
PyTorch: 2.6.0+cu124
Accelerate: 1.5.2
Datasets: 3.5.0
Tokenizers: 0.21.1

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",
}

MultipleNegativesRankingLoss

bibtex

@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}
}

	anchor	positive
type	string	string
details	min: 7 tokens mean: 85.73 tokens max: 229 tokens	min: 7 tokens mean: 85.86 tokens max: 229 tokens