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

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:7030944
loss:MSELoss
Eval Results
text-embeddings-inference
Inference Endpoints
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SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

This is a sentence-transformers model finetuned from sentence-transformers/all-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/all-MiniLM-L6-v2
  • Maximum Sequence Length: 256 tokens
  • Output Dimensionality: 384 dimensions
  • Similarity Function: Cosine Similarity
  • Training Datasets:
  • Languages: en, multilingual, ar, bg, ca, cs, da, de, el, es, et, fa, fi, fr, gl, gu, he, hi, hr, hu, hy, id, it, ja, ka, ko, ku, lt, lv, mk, mn, mr, ms, my, nb, nl, pl, pt, ro, ru, sk, sl, sq, sr, sv, th, tr, uk, ur, vi, zh

Model Sources

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.

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 = [
    "So what's the problem, why has this chasm opened up, and what can we do to fix it?",
    'Então qual é o problema? Por que se abriu este abismo, e o que podemos fazer para o resolver?',
    'O que o design e a construção oferecem ao ensino público',
]
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 -0.0991 -0.11 -0.1143

Translation

Metric en-pt-br en-es en-pt
src2trg_accuracy 0.9859 0.9114 0.8921
trg2src_accuracy 0.9798 0.9046 0.8818
mean_accuracy 0.9829 0.908 0.887

Semantic Similarity

Metric Value
pearson_cosine 0.7504
spearman_cosine 0.7603

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: 24.11 tokens
    • max: 256 tokens
    • min: 6 tokens
    • mean: 37.01 tokens
    • max: 256 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. [-0.0019007200608029962, 0.0689753070473671, -0.00522591220214963, 0.020715437829494476, -0.07340357452630997, ...]
    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. [0.06295035779476166, 0.07436762005090714, 0.012160283513367176, 0.016489440575242043, -0.04803427681326866, ...]
    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. [0.020892487838864326, 0.04348783195018768, 0.04366326704621315, 0.006932021584361792, -0.014990451745688915, ...]
  • Loss: MSELoss

en-es

  • Dataset: en-es
  • Size: 3,439,042 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: 24.16 tokens
    • max: 256 tokens
    • min: 5 tokens
    • mean: 35.26 tokens
    • max: 256 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. [-0.0019007298396900296, 0.06897532939910889, -0.005225935019552708, 0.020715486258268356, -0.07340355962514877, ...]
    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. [0.06295035779476166, 0.07436762005090714, 0.012160283513367176, 0.016489440575242043, -0.04803427681326866, ...]
    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. [0.020892487838864326, 0.04348784685134888, 0.043663300573825836, 0.0069320122711360455, -0.014990522526204586, ...]
  • Loss: MSELoss

en-pt

  • Dataset: en-pt
  • Size: 3,186,095 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: 23.63 tokens
    • max: 256 tokens
    • min: 5 tokens
    • mean: 35.37 tokens
    • max: 256 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. [-0.048315733671188354, 0.006750611122697592, 0.04261479899287224, -0.0639658197760582, 0.036691851913928986, ...]
    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. [0.07536645978689194, 0.016234878450632095, 0.018208693712949753, 0.012537049129605293, -0.016377247869968414, ...]
    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. [0.046284060925245285, 0.034320130944252014, 0.05807732418179512, -0.059097982943058014, 0.01139863021671772, ...]
  • 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: 24.47 tokens
    • max: 191 tokens
    • min: 5 tokens
    • mean: 39.01 tokens
    • max: 256 tokens
    • size: 384 elements
  • Samples:
    english non_english label
    Thank you so much, Chris. Muito obrigado, Chris. [0.026920655742287636, 0.053147971630096436, 0.14048898220062256, -0.10380183160305023, -0.041187822818756104, ...]
    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. [0.024387279525399208, 0.0950012058019638, 0.12180330604314804, -0.07149265706539154, -0.018444526940584183, ...]
    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. [0.015005475841462612, 0.014678296633064747, 0.1311199963092804, 0.03133270516991615, 0.06942538917064667, ...]
  • 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: 24.52 tokens
    • max: 191 tokens
    • min: 4 tokens
    • mean: 36.77 tokens
    • max: 252 tokens
    • size: 384 elements
  • Samples:
    english non_english label
    Thank you so much, Chris. Muchas gracias Chris. [0.026920655742287636, 0.053147971630096436, 0.14048898220062256, -0.10380183160305023, -0.041187822818756104, ...]
    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. [0.024387288838624954, 0.09500124305486679, 0.12180333584547043, -0.07149265706539154, -0.018444539979100227, ...]
    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. [0.015005475841462612, 0.014678296633064747, 0.1311199963092804, 0.03133270516991615, 0.06942538917064667, ...]
  • 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: 24.01 tokens
    • max: 191 tokens
    • min: 5 tokens
    • mean: 37.14 tokens
    • max: 256 tokens
    • size: 384 elements
  • Samples:
    english non_english label
    Thank you so much, Chris. Muito obrigado, Chris. [0.02692059800028801, 0.053147926926612854, 0.14048898220062256, -0.10380185395479202, -0.041187841445207596, ...]
    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. [0.024387234821915627, 0.09500119835138321, 0.12180334329605103, -0.07149267196655273, -0.018444577232003212, ...]
    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. [0.015005475841462612, 0.014678296633064747, 0.1311199963092804, 0.03133270516991615, 0.06942538917064667, ...]
  • Loss: MSELoss

Training Hyperparameters

Non-Default Hyperparameters

  • eval_strategy: steps
  • per_device_train_batch_size: 64
  • per_device_eval_batch_size: 64
  • gradient_accumulation_steps: 16
  • 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: 64
  • per_device_eval_batch_size: 64
  • per_gpu_train_batch_size: None
  • per_gpu_eval_batch_size: None
  • gradient_accumulation_steps: 16
  • 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
0.0146 100 0.0033 - - - - - - - - - -
0.0291 200 0.0031 - - - - - - - - - -
0.0437 300 0.0028 - - - - - - - - - -
0.0583 400 0.0027 - - - - - - - - - -
0.0728 500 0.0026 - - - - - - - - - -
0.0874 600 0.0025 - - - - - - - - - -
0.1019 700 0.0024 - - - - - - - - - -
0.1165 800 0.0024 - - - - - - - - - -
0.1311 900 0.0023 - - - - - - - - - -
0.1456 1000 0.0022 - - - - - - - - - -
0.1602 1100 0.0022 - - - - - - - - - -
0.1748 1200 0.0021 - - - - - - - - - -
0.1893 1300 0.0021 - - - - - - - - - -
0.2039 1400 0.0021 - - - - - - - - - -
0.2185 1500 0.002 - - - - - - - - - -
0.2330 1600 0.002 - - - - - - - - - -
0.2476 1700 0.0019 - - - - - - - - - -
0.2622 1800 0.0019 - - - - - - - - - -
0.2767 1900 0.0019 - - - - - - - - - -
0.2913 2000 0.0018 0.0017 0.0017 0.0017 -0.2294 0.7319 -0.2300 0.6270 0.2838 -0.2356 0.5901
0.3058 2100 0.0018 - - - - - - - - - -
0.3204 2200 0.0018 - - - - - - - - - -
0.3350 2300 0.0017 - - - - - - - - - -
0.3495 2400 0.0017 - - - - - - - - - -
0.3641 2500 0.0017 - - - - - - - - - -
0.3787 2600 0.0017 - - - - - - - - - -
0.3932 2700 0.0017 - - - - - - - - - -
0.4078 2800 0.0016 - - - - - - - - - -
0.4224 2900 0.0016 - - - - - - - - - -
0.4369 3000 0.0016 - - - - - - - - - -
0.4515 3100 0.0016 - - - - - - - - - -
0.4660 3200 0.0015 - - - - - - - - - -
0.4806 3300 0.0015 - - - - - - - - - -
0.4952 3400 0.0015 - - - - - - - - - -
0.5097 3500 0.0015 - - - - - - - - - -
0.5243 3600 0.0015 - - - - - - - - - -
0.5389 3700 0.0015 - - - - - - - - - -
0.5534 3800 0.0014 - - - - - - - - - -
0.5680 3900 0.0014 - - - - - - - - - -
0.5826 4000 0.0014 0.0012 0.0013 0.0013 -0.1733 0.9214 -0.1805 0.8074 0.4249 -0.1861 0.7836
0.5971 4100 0.0014 - - - - - - - - - -
0.6117 4200 0.0014 - - - - - - - - - -
0.6263 4300 0.0014 - - - - - - - - - -
0.6408 4400 0.0014 - - - - - - - - - -
0.6554 4500 0.0013 - - - - - - - - - -
0.6699 4600 0.0013 - - - - - - - - - -
0.6845 4700 0.0013 - - - - - - - - - -
0.6991 4800 0.0013 - - - - - - - - - -
0.7136 4900 0.0013 - - - - - - - - - -
0.7282 5000 0.0013 - - - - - - - - - -
0.7428 5100 0.0013 - - - - - - - - - -
0.7573 5200 0.0013 - - - - - - - - - -
0.7719 5300 0.0013 - - - - - - - - - -
0.7865 5400 0.0013 - - - - - - - - - -
0.8010 5500 0.0012 - - - - - - - - - -
0.8156 5600 0.0012 - - - - - - - - - -
0.8301 5700 0.0012 - - - - - - - - - -
0.8447 5800 0.0012 - - - - - - - - - -
0.8593 5900 0.0012 - - - - - - - - - -
0.8738 6000 0.0012 0.0010 0.0010 0.0011 -0.1443 0.9617 -0.1538 0.8627 0.5948 -0.1587 0.8420
0.8884 6100 0.0012 - - - - - - - - - -
0.9030 6200 0.0012 - - - - - - - - - -
0.9175 6300 0.0012 - - - - - - - - - -
0.9321 6400 0.0012 - - - - - - - - - -
0.9467 6500 0.0012 - - - - - - - - - -
0.9612 6600 0.0012 - - - - - - - - - -
0.9758 6700 0.0012 - - - - - - - - - -
0.9904 6800 0.0011 - - - - - - - - - -
1.0049 6900 0.0011 - - - - - - - - - -
1.0195 7000 0.0011 - - - - - - - - - -
1.0340 7100 0.0011 - - - - - - - - - -
1.0486 7200 0.0011 - - - - - - - - - -
1.0632 7300 0.0011 - - - - - - - - - -
1.0777 7400 0.0011 - - - - - - - - - -
1.0923 7500 0.0011 - - - - - - - - - -
1.1069 7600 0.0011 - - - - - - - - - -
1.1214 7700 0.0011 - - - - - - - - - -
1.1360 7800 0.0011 - - - - - - - - - -
1.1506 7900 0.0011 - - - - - - - - - -
1.1651 8000 0.0011 0.0009 0.0009 0.0010 -0.1298 0.9713 -0.1396 0.8814 0.6558 -0.1442 0.8606
1.1797 8100 0.0011 - - - - - - - - - -
1.1942 8200 0.0011 - - - - - - - - - -
1.2088 8300 0.0011 - - - - - - - - - -
1.2234 8400 0.0011 - - - - - - - - - -
1.2379 8500 0.0011 - - - - - - - - - -
1.2525 8600 0.0011 - - - - - - - - - -
1.2671 8700 0.0011 - - - - - - - - - -
1.2816 8800 0.0011 - - - - - - - - - -
1.2962 8900 0.0011 - - - - - - - - - -
1.3108 9000 0.001 - - - - - - - - - -
1.3253 9100 0.001 - - - - - - - - - -
1.3399 9200 0.001 - - - - - - - - - -
1.3545 9300 0.001 - - - - - - - - - -
1.3690 9400 0.001 - - - - - - - - - -
1.3836 9500 0.001 - - - - - - - - - -
1.3981 9600 0.001 - - - - - - - - - -
1.4127 9700 0.001 - - - - - - - - - -
1.4273 9800 0.001 - - - - - - - - - -
1.4418 9900 0.001 - - - - - - - - - -
1.4564 10000 0.001 0.0008 0.0009 0.0009 -0.1218 0.9733 -0.1318 0.8898 0.6796 -0.1361 0.8698
1.4710 10100 0.001 - - - - - - - - - -
1.4855 10200 0.001 - - - - - - - - - -
1.5001 10300 0.001 - - - - - - - - - -
1.5147 10400 0.001 - - - - - - - - - -
1.5292 10500 0.001 - - - - - - - - - -
1.5438 10600 0.001 - - - - - - - - - -
1.5583 10700 0.001 - - - - - - - - - -
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Framework Versions

  • Python: 3.9.20
  • Sentence Transformers: 3.3.0
  • Transformers: 4.46.2
  • PyTorch: 2.5.1+cu121
  • 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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