README.md

metadata

language:
  - en
  - multilingual
  - es
  - pt
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:13930944
  - loss:MSELoss
base_model: sentence-transformers/paraphrase-MiniLM-L6-v2
widget:
  - source_sentence: >-
      Custer recommends that Congress find a way to end the treaties with the
      Lakota as soon as possible.
    sentences:
      - >-
        Custer recomienda al Congreso encontrar un modo de terminar los tratados
        con los lakota lo antes posible.
      - Pero estos poros de aquí son especiales.
      - Esta es la intersección más directa, obvia, de las dos cosas.
  - source_sentence: And the USFDA has a jurisdictional problem.
    sentences:
      - E a FDA dos Estados Unidos tem um problema de jurisdição.
      - >-
        Eu estimei que, atualmente no mundo, gastamos cerca de 106 vidas em
        média ensinando as pessoas a calcular manualmente.
      - Posso comprar aquele produto sem comprometer minha ética?
  - source_sentence: >-
      In Sri Lanka, a decades-long civil war between the Tamil minority and the
      Sinhala majority led to a bloody climax in 2009, after perhaps as many as
      100,000 people had been killed since 1983.
    sentences:
      - >-
        Portanto, temos de investir no desenvolvimento de líderes, líderes que
        tenham as habilidades, visão e determinação para fazer a paz.
      - >-
        No Sri Lanka, uma guerra civil de décadas entre a minoria tâmil e a
        maioria cingalesa levou a um clímax sangrento em 2009, após cerca de 100
        mil pessoas serem assassinadas desde 1983.
      - >-
        Nos anos 90, houve uma série de escândalos relativos à produção de bens
        de marca nos EUA -- trabalho infantil, trabalho forçado, graves abusos
        de saúde e segurança --
  - source_sentence: >-
      The provisions in the agreement may be complex, but so is the underlying
      conflict.
    sentences:
      - >-
        As saladas que você vê no McDonald's vêm desse trabalho -- eles terão
        uma salada asiática. Na Pepsi, dois terços do crescimento de rendimento
        vieram de seus alimentos saudáveis.
      - >-
        Não apenas esta, mas conectados com as idéias que estão aqui, para
        fazê-las mais coerentes.
      - >-
        O disposto no acordo pode ser complexo, mas assim é o conflito
        subjacente.
  - source_sentence: We now call this place home.
    sentences:
      - >-
        e outros não contêm. Neste desenho, a célula branca azulada, no canto
        superior esquerdo não reage à luz porque não possui o poro ativado por
        luz.
      - Moramos ali. Agora é aqui a nossa casa.
      - É mais fácil do que se possa imaginar.
datasets:
  - sentence-transformers/parallel-sentences-talks
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - negative_mse
  - src2trg_accuracy
  - trg2src_accuracy
  - mean_accuracy
  - pearson_cosine
  - spearman_cosine
model-index:
  - name: SentenceTransformer based on sentence-transformers/paraphrase-MiniLM-L6-v2
    results:
      - task:
          type: knowledge-distillation
          name: Knowledge Distillation
        dataset:
          name: en pt br
          type: en-pt-br
        metrics:
          - type: negative_mse
            value: -4.06170654296875
            name: Negative Mse
      - task:
          type: translation
          name: Translation
        dataset:
          name: en pt br
          type: en-pt-br
        metrics:
          - type: src2trg_accuracy
            value: 0.9858870967741935
            name: Src2Trg Accuracy
          - type: trg2src_accuracy
            value: 0.9808467741935484
            name: Trg2Src Accuracy
          - type: mean_accuracy
            value: 0.983366935483871
            name: Mean Accuracy
      - task:
          type: knowledge-distillation
          name: Knowledge Distillation
        dataset:
          name: en es
          type: en-es
        metrics:
          - type: negative_mse
            value: -4.247319221496582
            name: Negative Mse
      - task:
          type: translation
          name: Translation
        dataset:
          name: en es
          type: en-es
        metrics:
          - type: src2trg_accuracy
            value: 0.908008008008008
            name: Src2Trg Accuracy
          - type: trg2src_accuracy
            value: 0.897997997997998
            name: Trg2Src Accuracy
          - type: mean_accuracy
            value: 0.903003003003003
            name: Mean Accuracy
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts17 es en test
          type: sts17-es-en-test
        metrics:
          - type: pearson_cosine
            value: 0.7713723133430411
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.7861741769541355
            name: Spearman Cosine
      - task:
          type: knowledge-distillation
          name: Knowledge Distillation
        dataset:
          name: en pt
          type: en-pt
        metrics:
          - type: negative_mse
            value: -4.255536079406738
            name: Negative Mse
      - task:
          type: translation
          name: Translation
        dataset:
          name: en pt
          type: en-pt
        metrics:
          - type: src2trg_accuracy
            value: 0.8951160928742994
            name: Src2Trg Accuracy
          - type: trg2src_accuracy
            value: 0.8824059247397918
            name: Trg2Src Accuracy
          - type: mean_accuracy
            value: 0.8887610088070457
            name: Mean Accuracy

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:
  • en-pt-br
  • en-es
  • en-pt
• Languages: en, multilingual, es, pt

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': 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

• Datasets: en-pt-br, en-es and en-pt
• Evaluated with TranslationEvaluator

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

• Dataset: sts17-es-en-test
• Evaluated with EmbeddingSimilarityEvaluator

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	-	-	-	-	-	-	-	-	-	-
0.8012	10900	0.0415	-	-	-	-	-	-	-	-	-	-
0.8085	11000	0.0416	-	-	-	-	-	-	-	-	-	-
0.8159	11100	0.0415	-	-	-	-	-	-	-	-	-	-
0.8232	11200	0.0415	-	-	-	-	-	-	-	-	-	-
0.8306	11300	0.0415	-	-	-	-	-	-	-	-	-	-
0.8380	11400	0.0415	-	-	-	-	-	-	-	-	-	-
0.8453	11500	0.0415	-	-	-	-	-	-	-	-	-	-
0.8527	11600	0.0415	-	-	-	-	-	-	-	-	-	-
0.8600	11700	0.0414	-	-	-	-	-	-	-	-	-	-
0.8674	11800	0.0414	-	-	-	-	-	-	-	-	-	-
0.8747	11900	0.0414	-	-	-	-	-	-	-	-	-	-
0.8821	12000	0.0414	0.0402	0.0417	0.0417	-4.2234926	0.9637	-4.3952775	0.8716	0.6684	-4.404395	0.8526
0.8894	12100	0.0414	-	-	-	-	-	-	-	-	-	-
0.8968	12200	0.0414	-	-	-	-	-	-	-	-	-	-
0.9041	12300	0.0414	-	-	-	-	-	-	-	-	-	-
0.9115	12400	0.0414	-	-	-	-	-	-	-	-	-	-
0.9188	12500	0.0413	-	-	-	-	-	-	-	-	-	-
0.9262	12600	0.0413	-	-	-	-	-	-	-	-	-	-
0.9335	12700	0.0414	-	-	-	-	-	-	-	-	-	-
0.9409	12800	0.0414	-	-	-	-	-	-	-	-	-	-
0.9482	12900	0.0413	-	-	-	-	-	-	-	-	-	-
0.9556	13000	0.0413	-	-	-	-	-	-	-	-	-	-
0.9629	13100	0.0413	-	-	-	-	-	-	-	-	-	-
0.9703	13200	0.0413	-	-	-	-	-	-	-	-	-	-
0.9776	13300	0.0413	-	-	-	-	-	-	-	-	-	-
0.9850	13400	0.0413	-	-	-	-	-	-	-	-	-	-
0.9923	13500	0.0412	-	-	-	-	-	-	-	-	-	-
0.9997	13600	0.0413	-	-	-	-	-	-	-	-	-	-
1.0070	13700	0.0413	-	-	-	-	-	-	-	-	-	-
1.0144	13800	0.0413	-	-	-	-	-	-	-	-	-	-
1.0217	13900	0.0412	-	-	-	-	-	-	-	-	-	-
1.0291	14000	0.0412	0.0400	0.0415	0.0416	-4.194809	0.9682	-4.36698	0.8798	0.6892	-4.37619	0.8621
1.0364	14100	0.0412	-	-	-	-	-	-	-	-	-	-
1.0438	14200	0.0412	-	-	-	-	-	-	-	-	-	-
1.0511	14300	0.0412	-	-	-	-	-	-	-	-	-	-
1.0585	14400	0.0412	-	-	-	-	-	-	-	-	-	-
1.0658	14500	0.0412	-	-	-	-	-	-	-	-	-	-
1.0732	14600	0.0412	-	-	-	-	-	-	-	-	-	-
1.0805	14700	0.0412	-	-	-	-	-	-	-	-	-	-
1.0879	14800	0.0412	-	-	-	-	-	-	-	-	-	-
1.0952	14900	0.0411	-	-	-	-	-	-	-	-	-	-
1.1026	15000	0.0411	-	-	-	-	-	-	-	-	-	-
1.1099	15100	0.0411	-	-	-	-	-	-	-	-	-	-
1.1173	15200	0.0411	-	-	-	-	-	-	-	-	-	-
1.1246	15300	0.0411	-	-	-	-	-	-	-	-	-	-
1.1320	15400	0.0411	-	-	-	-	-	-	-	-	-	-
1.1393	15500	0.041	-	-	-	-	-	-	-	-	-	-
1.1467	15600	0.0412	-	-	-	-	-	-	-	-	-	-
1.1540	15700	0.041	-	-	-	-	-	-	-	-	-	-
1.1614	15800	0.0411	-	-	-	-	-	-	-	-	-	-
1.1687	15900	0.0412	-	-	-	-	-	-	-	-	-	-
1.1761	16000	0.0411	0.0399	0.0414	0.0414	-4.1725326	0.9728	-4.347921	0.8845	0.7072	-4.3567324	0.8679
1.1834	16100	0.0411	-	-	-	-	-	-	-	-	-	-
1.1908	16200	0.0411	-	-	-	-	-	-	-	-	-	-
1.1981	16300	0.041	-	-	-	-	-	-	-	-	-	-
1.2055	16400	0.041	-	-	-	-	-	-	-	-	-	-
1.2128	16500	0.0411	-	-	-	-	-	-	-	-	-	-
1.2202	16600	0.041	-	-	-	-	-	-	-	-	-	-
1.2275	16700	0.0411	-	-	-	-	-	-	-	-	-	-
1.2349	16800	0.041	-	-	-	-	-	-	-	-	-	-
1.2422	16900	0.041	-	-	-	-	-	-	-	-	-	-
1.2496	17000	0.041	-	-	-	-	-	-	-	-	-	-
1.2569	17100	0.041	-	-	-	-	-	-	-	-	-	-
1.2643	17200	0.041	-	-	-	-	-	-	-	-	-	-
1.2716	17300	0.041	-	-	-	-	-	-	-	-	-	-
1.2790	17400	0.041	-	-	-	-	-	-	-	-	-	-
1.2863	17500	0.041	-	-	-	-	-	-	-	-	-	-
1.2937	17600	0.041	-	-	-	-	-	-	-	-	-	-
1.3010	17700	0.041	-	-	-	-	-	-	-	-	-	-
1.3084	17800	0.0409	-	-	-	-	-	-	-	-	-	-
1.3157	17900	0.041	-	-	-	-	-	-	-	-	-	-
1.3231	18000	0.041	0.0398	0.0413	0.0413	-4.156324	0.9733	-4.3334045	0.8877	0.7126	-4.3421884	0.8721
1.3304	18100	0.041	-	-	-	-	-	-	-	-	-	-
1.3378	18200	0.0409	-	-	-	-	-	-	-	-	-	-
1.3451	18300	0.0409	-	-	-	-	-	-	-	-	-	-
1.3525	18400	0.0409	-	-	-	-	-	-	-	-	-	-
1.3598	18500	0.0409	-	-	-	-	-	-	-	-	-	-
1.3672	18600	0.0409	-	-	-	-	-	-	-	-	-	-
1.3745	18700	0.041	-	-	-	-	-	-	-	-	-	-
1.3819	18800	0.0409	-	-	-	-	-	-	-	-	-	-
1.3892	18900	0.0408	-	-	-	-	-	-	-	-	-	-
1.3966	19000	0.0409	-	-	-	-	-	-	-	-	-	-
1.4039	19100	0.0409	-	-	-	-	-	-	-	-	-	-
1.4113	19200	0.0409	-	-	-	-	-	-	-	-	-	-
1.4186	19300	0.0408	-	-	-	-	-	-	-	-	-	-
1.4260	19400	0.0409	-	-	-	-	-	-	-	-	-	-
1.4333	19500	0.0409	-	-	-	-	-	-	-	-	-	-
1.4407	19600	0.0408	-	-	-	-	-	-	-	-	-	-
1.4480	19700	0.0409	-	-	-	-	-	-	-	-	-	-
1.4554	19800	0.0409	-	-	-	-	-	-	-	-	-	-
1.4627	19900	0.0409	-	-	-	-	-	-	-	-	-	-
1.4701	20000	0.0409	0.0397	0.0413	0.0413	-4.1426473	0.9763	-4.321089	0.8898	0.7271	-4.3293304	0.8749
1.4774	20100	0.0408	-	-	-	-	-	-	-	-	-	-
1.4848	20200	0.0409	-	-	-	-	-	-	-	-	-	-
1.4921	20300	0.0409	-	-	-	-	-	-	-	-	-	-
1.4995	20400	0.0409	-	-	-	-	-	-	-	-	-	-
1.5068	20500	0.0409	-	-	-	-	-	-	-	-	-	-
1.5142	20600	0.0408	-	-	-	-	-	-	-	-	-	-
1.5215	20700	0.0409	-	-	-	-	-	-	-	-	-	-
1.5289	20800	0.0408	-	-	-	-	-	-	-	-	-	-
1.5362	20900	0.0409	-	-	-	-	-	-	-	-	-	-
1.5436	21000	0.0409	-	-	-	-	-	-	-	-	-	-
1.5509	21100	0.0409	-	-	-	-	-	-	-	-	-	-
1.5583	21200	0.0408	-	-	-	-	-	-	-	-	-	-
1.5656	21300	0.0408	-	-	-	-	-	-	-	-	-	-
1.5730	21400	0.0408	-	-	-	-	-	-	-	-	-	-
1.5803	21500	0.0408	-	-	-	-	-	-	-	-	-	-
1.5877	21600	0.0409	-	-	-	-	-	-	-	-	-	-
1.5950	21700	0.0409	-	-	-	-	-	-	-	-	-	-
1.6024	21800	0.0408	-	-	-	-	-	-	-	-	-	-
1.6097	21900	0.0409	-	-	-	-	-	-	-	-	-	-
1.6171	22000	0.0408	0.0397	0.0412	0.0412	-4.1324744	0.9768	-4.312636	0.8920	0.7339	-4.320667	0.8767
1.6244	22100	0.0409	-	-	-	-	-	-	-	-	-	-
1.6318	22200	0.0408	-	-	-	-	-	-	-	-	-	-
1.6391	22300	0.0409	-	-	-	-	-	-	-	-	-	-
1.6465	22400	0.0408	-	-	-	-	-	-	-	-	-	-
1.6538	22500	0.0408	-	-	-	-	-	-	-	-	-	-
1.6612	22600	0.0408	-	-	-	-	-	-	-	-	-	-
1.6686	22700	0.0408	-	-	-	-	-	-	-	-	-	-
1.6759	22800	0.0408	-	-	-	-	-	-	-	-	-	-
1.6833	22900	0.0407	-	-	-	-	-	-	-	-	-	-
1.6906	23000	0.0408	-	-	-	-	-	-	-	-	-	-
1.6980	23100	0.0408	-	-	-	-	-	-	-	-	-	-
1.7053	23200	0.0407	-	-	-	-	-	-	-	-	-	-
1.7127	23300	0.0408	-	-	-	-	-	-	-	-	-	-
1.7200	23400	0.0408	-	-	-	-	-	-	-	-	-	-
1.7274	23500	0.0408	-	-	-	-	-	-	-	-	-	-
1.7347	23600	0.0408	-	-	-	-	-	-	-	-	-	-
1.7421	23700	0.0408	-	-	-	-	-	-	-	-	-	-
1.7494	23800	0.0408	-	-	-	-	-	-	-	-	-	-
1.7568	23900	0.0407	-	-	-	-	-	-	-	-	-	-
1.7641	24000	0.0408	0.0396	0.0411	0.0412	-4.1245446	0.9748	-4.304793	0.8929	0.7383	-4.312529	0.8785
1.7715	24100	0.0407	-	-	-	-	-	-	-	-	-	-
1.7788	24200	0.0408	-	-	-	-	-	-	-	-	-	-
1.7862	24300	0.0408	-	-	-	-	-	-	-	-	-	-
1.7935	24400	0.0407	-	-	-	-	-	-	-	-	-	-
1.8009	24500	0.0407	-	-	-	-	-	-	-	-	-	-
1.8082	24600	0.0408	-	-	-	-	-	-	-	-	-	-
1.8156	24700	0.0407	-	-	-	-	-	-	-	-	-	-
1.8229	24800	0.0408	-	-	-	-	-	-	-	-	-	-
1.8303	24900	0.0407	-	-	-	-	-	-	-	-	-	-
1.8376	25000	0.0408	-	-	-	-	-	-	-	-	-	-
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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",
}