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all-MiniLM-L6-multilingual-v2-en-es-pt-pt-br-v2

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:13930944
loss:MSELoss
Eval Results
text-embeddings-inference
Model card Files Community

SentenceTransformer based on sentence-transformers/paraphrase-MiniLM-L6-v2

This is a sentence-transformers model finetuned from sentence-transformers/paraphrase-MiniLM-L6-v2 on the en-pt-br, en-es and en-pt datasets. 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/paraphrase-MiniLM-L6-v2
  • Maximum Sequence Length: 128 tokens
  • Output Dimensionality: 384 dimensions
  • Similarity Function: Cosine Similarity
  • Training Datasets:
  • Languages: en, multilingual, es, pt

Model Sources

Full Model Architecture 

SentenceTransformer(
  (0): Transformer({'max_seq_length': 128, '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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("jvanhoof/all-MiniLM-L6-multilingual-v2-en-es-pt-pt-br")
# Run inference
sentences = [
    'We now call this place home.',
    'Moramos ali. Agora é aqui a nossa casa.',
    'É mais fácil do que se possa imaginar.',
]
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]

Evaluation

Metrics

Knowledge Distillation

  • Datasets: en-pt-br, en-es and en-pt
  • Evaluated with MSEEvaluator
Metric en-pt-br en-es en-pt
negative_mse -4.0617 -4.2473 -4.2555

Translation

Metric en-pt-br en-es en-pt
src2trg_accuracy 0.9859 0.908 0.8951
trg2src_accuracy 0.9808 0.898 0.8824
mean_accuracy 0.9834 0.903 0.8888

Semantic Similarity

Metric Value
pearson_cosine 0.7714
spearman_cosine 0.7862

Training Details

Training Datasets

en-pt-br

  • Dataset: en-pt-br at 0c70bc6
  • Size: 405,807 training samples
  • Columns: english, non_english, and label
  • Approximate statistics based on the first 1000 samples:
    english non_english label
    type string string list
    details
    • min: 4 tokens
    • mean: 23.98 tokens
    • max: 128 tokens
    • min: 6 tokens
    • mean: 36.86 tokens
    • max: 128 tokens
    • size: 384 elements
  • Samples:
    english non_english label
    And then there are certain conceptual things that can also benefit from hand calculating, but I think they're relatively small in number. E também existem alguns aspectos conceituais que também podem se beneficiar do cálculo manual, mas eu acho que eles são relativamente poucos. [-0.2655501961708069, 0.2715710997581482, 0.13977409899234772, 0.007375418208539486, -0.09395705163478851, ...]
    One thing I often ask about is ancient Greek and how this relates. Uma coisa sobre a qual eu pergunto com frequencia é grego antigo e como ele se relaciona a isto. [0.34961527585983276, -0.01806497573852539, 0.06103038787841797, 0.11750973761081696, -0.34720802307128906, ...]
    See, the thing we're doing right now is we're forcing people to learn mathematics. Vejam, o que estamos fazendo agora, é que estamos forçando as pessoas a aprender matemática. [0.031645823270082474, -0.1787087768316269, -0.30170342326164246, 0.1304805874824524, -0.29176947474479675, ...]
  • Loss: MSELoss

en-es

  • Dataset: en-es
  • Size: 6,889,042 training samples
  • Columns: english, non_english, and label
  • Approximate statistics based on the first 1000 samples:
    english non_english label
    type string string list
    details
    • min: 4 tokens
    • mean: 24.04 tokens
    • max: 128 tokens
    • min: 5 tokens
    • mean: 35.11 tokens
    • max: 128 tokens
    • size: 384 elements
  • Samples:
    english non_english label
    And then there are certain conceptual things that can also benefit from hand calculating, but I think they're relatively small in number. Y luego hay ciertas aspectos conceptuales que pueden beneficiarse del cálculo a mano pero creo que son relativamente pocos. [-0.2655501961708069, 0.2715710997581482, 0.13977409899234772, 0.007375418208539486, -0.09395705163478851, ...]
    One thing I often ask about is ancient Greek and how this relates. Algo que pregunto a menudo es sobre el griego antiguo y cómo se relaciona. [0.34961527585983276, -0.01806497573852539, 0.06103038787841797, 0.11750973761081696, -0.34720802307128906, ...]
    See, the thing we're doing right now is we're forcing people to learn mathematics. Vean, lo que estamos haciendo ahora es forzar a la gente a aprender matemáticas. [0.031645823270082474, -0.1787087768316269, -0.30170342326164246, 0.1304805874824524, -0.29176947474479675, ...]
  • Loss: MSELoss

en-pt

  • Dataset: en-pt
  • Size: 6,636,095 training samples
  • Columns: english, non_english, and label
  • Approximate statistics based on the first 1000 samples:
    english non_english label
    type string string list
    details
    • min: 4 tokens
    • mean: 23.5 tokens
    • max: 128 tokens
    • min: 5 tokens
    • mean: 35.23 tokens
    • max: 128 tokens
    • size: 384 elements
  • Samples:
    english non_english label
    And the country that does this first will, in my view, leapfrog others in achieving a new economy even, an improved economy, an improved outlook. E o país que fizer isto primeiro vai, na minha opinião, ultrapassar outros em alcançar uma nova economia até uma economia melhorada, uma visão melhorada. [-0.1395619511604309, -0.1703503578901291, 0.21396367251873016, -0.29212212562561035, 0.2718254327774048, ...]
    In fact, I even talk about us moving from what we often call now the "knowledge economy" to what we might call a "computational knowledge economy," where high-level math is integral to what everyone does in the way that knowledge currently is. De facto, eu até falo de mudarmos do que chamamos hoje a economia do conhecimento para o que poderemos chamar a economia do conhecimento computacional, onde a matemática de alto nível está integrada no que toda a gente faz da forma que o conhecimento actualmente está. [-0.002996142255142331, -0.34310653805732727, -0.09672430157661438, 0.23709852993488312, -0.013354267925024033, ...]
    We can engage so many more students with this, and they can have a better time doing it. Podemos cativar tantos mais estudantes com isto, e eles podem divertir-se mais a fazê-lo. [0.2670706808567047, 0.09549400955438614, -0.17057836055755615, -0.2152799665927887, -0.2832679748535156, ...]
  • Loss: MSELoss

Evaluation Datasets

en-pt-br

  • Dataset: en-pt-br at 0c70bc6
  • Size: 992 evaluation samples
  • Columns: english, non_english, and label
  • Approximate statistics based on the first 992 samples:
    english non_english label
    type string string list
    details
    • min: 4 tokens
    • mean: 24.37 tokens
    • max: 128 tokens
    • min: 5 tokens
    • mean: 38.6 tokens
    • max: 128 tokens
    • size: 384 elements
  • Samples:
    english non_english label
    Thank you so much, Chris. Muito obrigado, Chris. [-0.1929965764284134, 0.051721055060625076, 0.3780047297477722, -0.20386895537376404, -0.2625442445278168, ...]
    And it's truly a great honor to have the opportunity to come to this stage twice; I'm extremely grateful. É realmente uma grande honra ter a oportunidade de estar neste palco pela segunda vez. Estou muito agradecido. [0.04667849838733673, 0.16640479862689972, 0.05405835807323456, -0.2507464587688446, -0.5305444002151489, ...]
    I have been blown away by this conference, and I want to thank all of you for the many nice comments about what I had to say the other night. Eu fui muito aplaudido por esta conferência e quero agradecer a todos pelos muitos comentários delicados sobre o que eu tinha a dizer naquela noite. [0.04410325363278389, 0.2660813629627228, -0.013608227483928204, 0.08376947790384293, 0.22691071033477783, ...]
  • Loss: MSELoss

en-es

  • Dataset: en-es
  • Size: 9,990 evaluation samples
  • Columns: english, non_english, and label
  • Approximate statistics based on the first 1000 samples:
    english non_english label
    type string string list
    details
    • min: 4 tokens
    • mean: 24.39 tokens
    • max: 128 tokens
    • min: 4 tokens
    • mean: 36.38 tokens
    • max: 128 tokens
    • size: 384 elements
  • Samples:
    english non_english label
    Thank you so much, Chris. Muchas gracias Chris. [-0.19299663603305817, 0.051721103489398956, 0.37800467014312744, -0.20386885106563568, -0.2625444531440735, ...]
    And it's truly a great honor to have the opportunity to come to this stage twice; I'm extremely grateful. Y es en verdad un gran honor tener la oportunidad de venir a este escenario por segunda vez. Estoy extremadamente agradecido. [0.04667845368385315, 0.16640479862689972, 0.05405828729271889, -0.25074639916419983, -0.5305443406105042, ...]
    I have been blown away by this conference, and I want to thank all of you for the many nice comments about what I had to say the other night. He quedado conmovido por esta conferencia, y deseo agradecer a todos ustedes sus amables comentarios acerca de lo que tenía que decir la otra noche. [0.04410335421562195, 0.2660813629627228, -0.01360794436186552, 0.08376938849687576, 0.22691065073013306, ...]
  • Loss: MSELoss

en-pt

  • Dataset: en-pt
  • Size: 9,992 evaluation samples
  • Columns: english, non_english, and label
  • Approximate statistics based on the first 1000 samples:
    english non_english label
    type string string list
    details
    • min: 4 tokens
    • mean: 23.82 tokens
    • max: 128 tokens
    • min: 5 tokens
    • mean: 36.7 tokens
    • max: 128 tokens
    • size: 384 elements
  • Samples:
    english non_english label
    Thank you so much, Chris. Muito obrigado, Chris. [-0.19299663603305817, 0.051721103489398956, 0.37800467014312744, -0.20386885106563568, -0.2625444531440735, ...]
    And it's truly a great honor to have the opportunity to come to this stage twice; I'm extremely grateful. É realmente uma grande honra ter a oportunidade de pisar este palco pela segunda vez. Estou muito agradecido. [0.04667849838733673, 0.16640479862689972, 0.05405835807323456, -0.2507464587688446, -0.5305444002151489, ...]
    I have been blown away by this conference, and I want to thank all of you for the many nice comments about what I had to say the other night. Fiquei muito impressionado com esta conferência e quero agradecer a todos os imensos comentários simpáticos sobre o que eu tinha a dizer naquela noite. [0.04410335421562195, 0.2660813629627228, -0.01360794436186552, 0.08376938849687576, 0.22691065073013306, ...]
  • Loss: MSELoss

Training Hyperparameters

Non-Default Hyperparameters

  • eval_strategy: steps
  • per_device_train_batch_size: 128
  • per_device_eval_batch_size: 128
  • gradient_accumulation_steps: 8
  • num_train_epochs: 6
  • warmup_ratio: 0.15
  • bf16: 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: 8
  • 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: 6
  • max_steps: -1
  • lr_scheduler_type: linear
  • lr_scheduler_kwargs: {}
  • warmup_ratio: 0.15
  • 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: True
  • fp16: False
  • fp16_opt_level: O1
  • half_precision_backend: auto
  • bf16_full_eval: False
  • fp16_full_eval: False
  • tf32: None
  • local_rank: 0
  • ddp_backend: None
  • tpu_num_cores: None
  • tpu_metrics_debug: False
  • debug: []
  • dataloader_drop_last: False
  • dataloader_num_workers: 0
  • dataloader_prefetch_factor: None
  • past_index: -1
  • disable_tqdm: False
  • remove_unused_columns: True
  • label_names: None
  • load_best_model_at_end: False
  • ignore_data_skip: False
  • fsdp: []
  • fsdp_min_num_params: 0
  • fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
  • fsdp_transformer_layer_cls_to_wrap: None
  • accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
  • deepspeed: None
  • label_smoothing_factor: 0.0
  • optim: adamw_torch
  • optim_args: None
  • adafactor: False
  • group_by_length: False
  • length_column_name: length
  • ddp_find_unused_parameters: None
  • ddp_bucket_cap_mb: None
  • ddp_broadcast_buffers: False
  • dataloader_pin_memory: True
  • dataloader_persistent_workers: False
  • skip_memory_metrics: True
  • use_legacy_prediction_loop: False
  • push_to_hub: False
  • resume_from_checkpoint: None
  • hub_model_id: None
  • hub_strategy: every_save
  • hub_private_repo: False
  • hub_always_push: False
  • gradient_checkpointing: False
  • gradient_checkpointing_kwargs: None
  • include_inputs_for_metrics: False
  • 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: batch_sampler
  • multi_dataset_batch_sampler: proportional

Training Logs

Click to expand
Epoch Step Training Loss en-pt-br loss en-es loss en-pt loss en-pt-br_negative_mse en-pt-br_mean_accuracy en-es_negative_mse en-es_mean_accuracy sts17-es-en-test_spearman_cosine en-pt_negative_mse en-pt_mean_accuracy
0.0074 100 0.0512 - - - - - - - - - -
0.0147 200 0.0505 - - - - - - - - - -
0.0221 300 0.0496 - - - - - - - - - -
0.0294 400 0.0489 - - - - - - - - - -
0.0368 500 0.0483 - - - - - - - - - -
0.0441 600 0.0479 - - - - - - - - - -
0.0515 700 0.0476 - - - - - - - - - -
0.0588 800 0.0474 - - - - - - - - - -
0.0662 900 0.0471 - - - - - - - - - -
0.0735 1000 0.0469 - - - - - - - - - -
0.0809 1100 0.0467 - - - - - - - - - -
0.0882 1200 0.0464 - - - - - - - - - -
0.0956 1300 0.0461 - - - - - - - - - -
0.1029 1400 0.046 - - - - - - - - - -
0.1103 1500 0.0458 - - - - - - - - - -
0.1176 1600 0.0456 - - - - - - - - - -
0.1250 1700 0.0455 - - - - - - - - - -
0.1323 1800 0.0454 - - - - - - - - - -
0.1397 1900 0.0452 - - - - - - - - - -
0.1470 2000 0.0452 0.0441 0.0454 0.0455 -4.785339 0.5978 -4.9081144 0.5252 0.2460 -4.929552 0.4744
0.1544 2100 0.0449 - - - - - - - - - -
0.1617 2200 0.0449 - - - - - - - - - -
0.1691 2300 0.0448 - - - - - - - - - -
0.1764 2400 0.0447 - - - - - - - - - -
0.1838 2500 0.0446 - - - - - - - - - -
0.1911 2600 0.0445 - - - - - - - - - -
0.1985 2700 0.0443 - - - - - - - - - -
0.2058 2800 0.0443 - - - - - - - - - -
0.2132 2900 0.0442 - - - - - - - - - -
0.2205 3000 0.0441 - - - - - - - - - -
0.2279 3100 0.0441 - - - - - - - - - -
0.2352 3200 0.0439 - - - - - - - - - -
0.2426 3300 0.0439 - - - - - - - - - -
0.2499 3400 0.0439 - - - - - - - - - -
0.2573 3500 0.0438 - - - - - - - - - -
0.2646 3600 0.0437 - - - - - - - - - -
0.2720 3700 0.0436 - - - - - - - - - -
0.2793 3800 0.0435 - - - - - - - - - -
0.2867 3900 0.0436 - - - - - - - - - -
0.2940 4000 0.0435 0.0424 0.0437 0.0438 -4.5627975 0.8054 -4.6922235 0.7096 0.3575 -4.715491 0.6680
0.3014 4100 0.0434 - - - - - - - - - -
0.3087 4200 0.0432 - - - - - - - - - -
0.3161 4300 0.0433 - - - - - - - - - -
0.3234 4400 0.0432 - - - - - - - - - -
0.3308 4500 0.0432 - - - - - - - - - -
0.3381 4600 0.0431 - - - - - - - - - -
0.3455 4700 0.0431 - - - - - - - - - -
0.3528 4800 0.043 - - - - - - - - - -
0.3602 4900 0.043 - - - - - - - - - -
0.3675 5000 0.043 - - - - - - - - - -
0.3749 5100 0.0429 - - - - - - - - - -
0.3822 5200 0.0429 - - - - - - - - - -
0.3896 5300 0.0427 - - - - - - - - - -
0.3969 5400 0.0428 - - - - - - - - - -
0.4043 5500 0.0428 - - - - - - - - - -
0.4116 5600 0.0427 - - - - - - - - - -
0.4190 5700 0.0426 - - - - - - - - - -
0.4263 5800 0.0427 - - - - - - - - - -
0.4337 5900 0.0427 - - - - - - - - - -
0.4410 6000 0.0425 0.0414 0.0428 0.0429 -4.415331 0.8997 -4.566894 0.7969 0.4509 -4.5856175 0.7684
0.4484 6100 0.0425 - - - - - - - - - -
0.4557 6200 0.0425 - - - - - - - - - -
0.4631 6300 0.0426 - - - - - - - - - -
0.4704 6400 0.0424 - - - - - - - - - -
0.4778 6500 0.0424 - - - - - - - - - -
0.4851 6600 0.0424 - - - - - - - - - -
0.4925 6700 0.0423 - - - - - - - - - -
0.4998 6800 0.0425 - - - - - - - - - -
0.5072 6900 0.0423 - - - - - - - - - -
0.5145 7000 0.0422 - - - - - - - - - -
0.5219 7100 0.0423 - - - - - - - - - -
0.5292 7200 0.0422 - - - - - - - - - -
0.5366 7300 0.0422 - - - - - - - - - -
0.5439 7400 0.0422 - - - - - - - - - -
0.5513 7500 0.0422 - - - - - - - - - -
0.5586 7600 0.0421 - - - - - - - - - -
0.5660 7700 0.0421 - - - - - - - - - -
0.5733 7800 0.0421 - - - - - - - - - -
0.5807 7900 0.0421 - - - - - - - - - -
0.5880 8000 0.0421 0.0409 0.0423 0.0424 -4.325846 0.9400 -4.488157 0.8366 0.5334 -4.5032544 0.8170
0.5954 8100 0.0421 - - - - - - - - - -
0.6027 8200 0.042 - - - - - - - - - -
0.6101 8300 0.042 - - - - - - - - - -
0.6174 8400 0.042 - - - - - - - - - -
0.6248 8500 0.0421 - - - - - - - - - -
0.6321 8600 0.0419 - - - - - - - - - -
0.6395 8700 0.042 - - - - - - - - - -
0.6468 8800 0.0419 - - - - - - - - - -
0.6542 8900 0.0419 - - - - - - - - - -
0.6615 9000 0.0419 - - - - - - - - - -
0.6689 9100 0.0418 - - - - - - - - - -
0.6762 9200 0.0418 - - - - - - - - - -
0.6836 9300 0.0418 - - - - - - - - - -
0.6909 9400 0.0418 - - - - - - - - - -
0.6983 9500 0.0418 - - - - - - - - - -
0.7056 9600 0.0417 - - - - - - - - - -
0.7130 9700 0.0417 - - - - - - - - - -
0.7203 9800 0.0417 - - - - - - - - - -
0.7277 9900 0.0417 - - - - - - - - - -
0.7350 10000 0.0417 0.0405 0.0420 0.0420 -4.2650604 0.9526 -4.434938 0.8587 0.6200 -4.4468904 0.8390
0.7424 10100 0.0416 - - - - - - - - - -
0.7497 10200 0.0417 - - - - - - - - - -
0.7571 10300 0.0416 - - - - - - - - - -
0.7644 10400 0.0416 - - - - - - - - - -
0.7718 10500 0.0416 - - - - - - - - - -
0.7791 10600 0.0416 - - - - - - - - - -
0.7865 10700 0.0416 - - - - - - - - - -
0.7938 10800 0.0416 - - - - - - - - - -
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5.8215 79200 0.0404 - - - - - - - - - -
5.8289 79300 0.0403 - - - - - - - - - -
5.8363 79400 0.0404 - - - - - - - - - -
5.8436 79500 0.0403 - - - - - - - - - -
5.8510 79600 0.0404 - - - - - - - - - -
5.8583 79700 0.0403 - - - - - - - - - -
5.8657 79800 0.0403 - - - - - - - - - -
5.8730 79900 0.0403 - - - - - - - - - -
5.8804 80000 0.0403 0.0392 0.0408 0.0408 -4.0617065 0.9834 -4.247319 0.9030 0.7862 -4.255536 0.8888
5.8877 80100 0.0404 - - - - - - - - - -
5.8951 80200 0.0404 - - - - - - - - - -
5.9024 80300 0.0403 - - - - - - - - - -
5.9098 80400 0.0404 - - - - - - - - - -
5.9171 80500 0.0403 - - - - - - - - - -
5.9245 80600 0.0403 - - - - - - - - - -
5.9318 80700 0.0404 - - - - - - - - - -
5.9392 80800 0.0403 - - - - - - - - - -
5.9465 80900 0.0404 - - - - - - - - - -
5.9539 81000 0.0403 - - - - - - - - - -
5.9612 81100 0.0403 - - - - - - - - - -
5.9686 81200 0.0403 - - - - - - - - - -
5.9759 81300 0.0404 - - - - - - - - - -
5.9833 81400 0.0403 - - - - - - - - - -
5.9906 81500 0.0403 - - - - - - - - - -
5.9980 81600 0.0403 - - - - - - - - - -

Framework Versions

  • Python: 3.12.7
  • Sentence Transformers: 3.3.0
  • Transformers: 4.46.2
  • PyTorch: 2.5.1+cu124
  • Accelerate: 1.1.1
  • Datasets: 3.1.0
  • Tokenizers: 0.20.3

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",
}

MSELoss 

@inproceedings{reimers-2020-multilingual-sentence-bert,
    title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2020",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/2004.09813",
}
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