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xlm-roberta-base-multilingual-en-ar-fr-de-es-tr-it

Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
loss:MSELoss
Eval Results
text-embeddings-inference
Inference Endpoints
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SentenceTransformer based on FacebookAI/xlm-roberta-base

This is a sentence-transformers model finetuned from FacebookAI/xlm-roberta-base on the en-ar, en-fr, en-de, en-es, en-tr and en-it datasets. 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: FacebookAI/xlm-roberta-base
  • Maximum Sequence Length: 128 tokens
  • Output Dimensionality: 768 tokens
  • 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': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 768, '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})
)

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("tomaarsen/xlm-roberta-base-multilingual-en-ar-fr-de-es-tr-it")
# Run inference
sentences = [
    'Wir sind eins.',
    'Das versuchen wir zu bieten.',
    'Ihre Gehirne sind ungefähr 100 Millionen Mal komplizierter.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

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

Evaluation

Metrics

Knowledge Distillation

Metric Value
negative_mse -20.3955

Translation

Metric Value
src2trg_accuracy 0.7603
trg2src_accuracy 0.7825
mean_accuracy 0.7714

Semantic Similarity

Metric Value
pearson_cosine 0.4098
spearman_cosine 0.4425
pearson_manhattan 0.4069
spearman_manhattan 0.4194
pearson_euclidean 0.3801
spearman_euclidean 0.3865
pearson_dot 0.4078
spearman_dot 0.3768
pearson_max 0.4098
spearman_max 0.4425

Knowledge Distillation

Metric Value
negative_mse -19.6232

Translation

Metric Value
src2trg_accuracy 0.8982
trg2src_accuracy 0.8901
mean_accuracy 0.8942

Semantic Similarity

Metric Value
pearson_cosine 0.5018
spearman_cosine 0.5334
pearson_manhattan 0.4461
spearman_manhattan 0.4547
pearson_euclidean 0.4431
spearman_euclidean 0.4481
pearson_dot 0.4017
spearman_dot 0.4134
pearson_max 0.5018
spearman_max 0.5334

Knowledge Distillation

Metric Value
negative_mse -19.7279

Translation

Metric Value
src2trg_accuracy 0.892
trg2src_accuracy 0.891
mean_accuracy 0.8915

Semantic Similarity

Metric Value
pearson_cosine 0.5263
spearman_cosine 0.5618
pearson_manhattan 0.5085
spearman_manhattan 0.5218
pearson_euclidean 0.5055
spearman_euclidean 0.5206
pearson_dot 0.3742
spearman_dot 0.3691
pearson_max 0.5263
spearman_max 0.5618

Knowledge Distillation

Metric Value
negative_mse -19.4724

Translation

Metric Value
src2trg_accuracy 0.9434
trg2src_accuracy 0.9465
mean_accuracy 0.9449

Semantic Similarity

Metric Value
pearson_cosine 0.4945
spearman_cosine 0.5021
pearson_manhattan 0.4445
spearman_manhattan 0.4284
pearson_euclidean 0.4357
spearman_euclidean 0.417
pearson_dot 0.3751
spearman_dot 0.3796
pearson_max 0.4945
spearman_max 0.5021

Knowledge Distillation

Metric Value
negative_mse -20.7547

Translation

Metric Value
src2trg_accuracy 0.7432
trg2src_accuracy 0.7432
mean_accuracy 0.7432

Semantic Similarity

Metric Value
pearson_cosine 0.5545
spearman_cosine 0.5819
pearson_manhattan 0.5104
spearman_manhattan 0.5088
pearson_euclidean 0.5046
spearman_euclidean 0.5053
pearson_dot 0.4726
spearman_dot 0.4298
pearson_max 0.5545
spearman_max 0.5819

Knowledge Distillation

Metric Value
negative_mse -19.7699

Translation

Metric Value
src2trg_accuracy 0.8781
trg2src_accuracy 0.8832
mean_accuracy 0.8807

Semantic Similarity

Metric Value
pearson_cosine 0.5064
spearman_cosine 0.525
pearson_manhattan 0.4517
spearman_manhattan 0.4623
pearson_euclidean 0.4423
spearman_euclidean 0.4507
pearson_dot 0.4202
spearman_dot 0.4225
pearson_max 0.5064
spearman_max 0.525

Training Details

Training Datasets

en-ar

  • Dataset: en-ar at d366ddd
  • Size: 5,000 training samples
  • Columns: non_english and label
  • Approximate statistics based on the first 1000 samples:
    non_english label
    type string list
    details
    • min: 4 tokens
    • mean: 27.3 tokens
    • max: 128 tokens
    • size: 768 elements
  • Samples:
    non_english label
    حسناً ان ما نقوم به اليوم .. هو ان نجبر الطلاب لتعلم الرياضيات [0.3943225145339966, 0.18910610675811768, -0.3788299858570099, 0.4386662542819977, 0.2727023661136627, ...]
    انها المادة الاهم .. [0.6257511377334595, -0.1750679910182953, -0.5734405517578125, 0.11480475962162018, 1.1682192087173462, ...]
    انا لا انفي لدقيقة واحدة ان الذين يهتمون بالحسابات اليدوية والذين هوايتهم القيام بذلك .. او القيام بالطرق التقليدية في اي مجال ان يقوموا بذلك كما يريدون . [-0.04564047232270241, 0.4971524775028229, 0.28066301345825195, -0.726702094078064, -0.17846377193927765, ...]
  • Loss: MSELoss

en-fr

  • Dataset: en-fr at d366ddd
  • Size: 5,000 training samples
  • Columns: non_english and label
  • Approximate statistics based on the first 1000 samples:
    non_english label
    type string list
    details
    • min: 3 tokens
    • mean: 30.18 tokens
    • max: 128 tokens
    • size: 768 elements
  • Samples:
    non_english label
    Je ne crois pas que ce soit justifié. [-0.361753910779953, 0.7323777079582214, 0.6518164277076721, -0.8461216688156128, -0.007496988866478205, ...]
    Je fais cette distinction entre ce qu'on force les gens à faire et les matières générales, et la matière que quelqu'un va apprendre parce que ça lui plait et peut-être même exceller dans ce domaine. [0.3047865629196167, 0.5270194411277771, 0.26616284251213074, 0.2612147927284241, 0.1950961947441101, ...]
    Quels sont les problèmes en relation avec ça? [0.2123892903327942, -0.09616081416606903, -0.41965243220329285, -0.5469444394111633, -0.6056491136550903, ...]
  • Loss: MSELoss

en-de

  • Dataset: en-de at d366ddd
  • Size: 5,000 training samples
  • Columns: non_english and label
  • Approximate statistics based on the first 1000 samples:
    non_english label
    type string list
    details
    • min: 4 tokens
    • mean: 27.04 tokens
    • max: 128 tokens
    • size: 768 elements
  • Samples:
    non_english label
    Ich denke, dass es sich aus diesem Grund lohnt, den Leuten das Rechnen von Hand beizubringen. [0.0960279330611229, 0.7833179831504822, -0.09527698159217834, 0.8104371428489685, 0.7545774579048157, ...]
    Außerdem gibt es ein paar bestimmte konzeptionelle Dinge, die das Rechnen per Hand rechtfertigen, aber ich glaube es sind sehr wenige. [-0.5939837098121643, 0.9714100956916809, 0.6800686717033386, -0.21585524082183838, -0.7509503364562988, ...]
    Eine Sache, die ich mich oft frage, ist Altgriechisch, und wie das zusammengehört. [-0.09777048230171204, 0.07093209028244019, -0.42989012598991394, -0.1457514613866806, 1.4382753372192383, ...]
  • Loss: MSELoss

en-es

  • Dataset: en-es at d366ddd
  • Size: 5,000 training samples
  • Columns: non_english and label
  • Approximate statistics based on the first 1000 samples:
    non_english label
    type string list
    details
    • min: 4 tokens
    • mean: 25.42 tokens
    • max: 128 tokens
    • size: 768 elements
  • Samples:
    non_english label
    Y luego hay ciertas aspectos conceptuales que pueden beneficiarse del cálculo a mano pero creo que son relativamente pocos. [-0.5939835906028748, 0.9714106917381287, 0.6800685524940491, -0.2158554196357727, -0.7509507536888123, ...]
    Algo que pregunto a menudo es sobre el griego antiguo y cómo se relaciona. [-0.09777048230171204, 0.07093209028244019, -0.42989012598991394, -0.1457514613866806, 1.4382753372192383, ...]
    Vean, lo que estamos haciendo ahora es forzar a la gente a aprender matemáticas. [0.3943225145339966, 0.18910610675811768, -0.3788299858570099, 0.4386662542819977, 0.2727023661136627, ...]
  • Loss: MSELoss

en-tr

  • Dataset: en-tr at d366ddd
  • Size: 5,000 training samples
  • Columns: non_english and label
  • Approximate statistics based on the first 1000 samples:
    non_english label
    type string list
    details
    • min: 4 tokens
    • mean: 24.72 tokens
    • max: 128 tokens
    • size: 768 elements
  • Samples:
    non_english label
    Eğer insanlar elle hesaba ilgililerse ya da öğrenmek için özel amaçları varsa konu ne kadar acayip olursa olsun bunu öğrenmeliler, engellemeyi bir an için bile önermiyorum. [-0.04564047232270241, 0.4971524775028229, 0.28066301345825195, -0.726702094078064, -0.17846377193927765, ...]
    İnsanların kendi ilgi alanlarını takip etmeleri, kesinlikle doğru bir şeydir. [0.2061387449502945, 0.5284574031829834, 0.3577779233455658, 0.28818392753601074, 0.17228049039840698, ...]
    Ben bir biçimde Antik Yunan hakkında ilgiliyimdir. ancak tüm nüfusu Antik Yunan gibi bir konu hakkında bilgi edinmeye zorlamamalıyız. [0.12050342559814453, 0.15652479231357574, 0.48636534810066223, -0.13693244755268097, 0.42764803767204285, ...]
  • Loss: MSELoss

en-it

  • Dataset: en-it at d366ddd
  • Size: 5,000 training samples
  • Columns: non_english and label
  • Approximate statistics based on the first 1000 samples:
    non_english label
    type string list
    details
    • min: 3 tokens
    • mean: 26.41 tokens
    • max: 128 tokens
    • size: 768 elements
  • Samples:
    non_english label
    Non credo che sia giustificato. [-0.36175352334976196, 0.7323781251907349, 0.651816189289093, -0.8461223840713501, -0.007496151141822338, ...]
    Perciò faccio distinzione tra quello che stiamo facendo fare alle persone, le materie che si ritengono principali, e le materie che le persone potrebbero seguire per loro interesse o forse a volte anche incitate a farlo. [0.3047865927219391, 0.5270194411277771, 0.26616284251213074, 0.2612147927284241, 0.1950961947441101, ...]
    Ma che argomenti porta la gente su questi temi? [0.2123885154724121, -0.09616123884916306, -0.4196523427963257, -0.5469440817832947, -0.6056501865386963, ...]
  • Loss: MSELoss

Evaluation Datasets

en-ar

  • Dataset: en-ar at d366ddd
  • Size: 993 evaluation samples
  • Columns: non_english and label
  • Approximate statistics based on the first 1000 samples:
    non_english label
    type string list
    details
    • min: 3 tokens
    • mean: 28.03 tokens
    • max: 128 tokens
    • size: 768 elements
  • Samples:
    non_english label
    شكرا جزيلا كريس. [-0.4331263303756714, 1.0602688789367676, -0.07791043072938919, -0.4170420169830322, 1.6768444776535034, ...]
    انه فعلا شرف عظيم لي ان أصعد المنصة للمرة الثانية. أنا في غاية الامتنان. [0.27005696296691895, 0.5391750335693359, -0.2580486238002777, -0.6613674759864807, 0.6738830804824829, ...]
    لقد بهرت فعلا بهذا المؤتمر, وأريد أن أشكركم جميعا على تعليقاتكم الطيبة على ما قلته تلك الليلة. [-0.25320106744766235, 0.04791366308927536, -0.13174884021282196, -0.7357578277587891, 0.2366354614496231, ...]
  • Loss: MSELoss

en-fr

  • Dataset: en-fr at d366ddd
  • Size: 992 evaluation samples
  • Columns: non_english and label
  • Approximate statistics based on the first 1000 samples:
    non_english label
    type string list
    details
    • min: 4 tokens
    • mean: 30.72 tokens
    • max: 128 tokens
    • size: 768 elements
  • Samples:
    non_english label
    Merci beaucoup, Chris. [-0.4331263303756714, 1.0602688789367676, -0.07791043072938919, -0.4170420169830322, 1.6768444776535034, ...]
    C'est vraiment un honneur de pouvoir venir sur cette scène une deuxième fois. Je suis très reconnaissant. [0.27005696296691895, 0.5391750335693359, -0.2580486238002777, -0.6613674759864807, 0.6738830804824829, ...]
    J'ai été très impressionné par cette conférence, et je tiens à vous remercier tous pour vos nombreux et sympathiques commentaires sur ce que j'ai dit l'autre soir. [-0.25320106744766235, 0.04791366308927536, -0.13174884021282196, -0.7357578277587891, 0.2366354614496231, ...]
  • Loss: MSELoss

en-de

  • Dataset: en-de at d366ddd
  • Size: 991 evaluation samples
  • Columns: non_english and label
  • Approximate statistics based on the first 1000 samples:
    non_english label
    type string list
    details
    • min: 4 tokens
    • mean: 27.71 tokens
    • max: 128 tokens
    • size: 768 elements
  • Samples:
    non_english label
    Vielen Dank, Chris. [-0.4331263303756714, 1.0602688789367676, -0.07791043072938919, -0.4170420169830322, 1.6768444776535034, ...]
    Es ist mir wirklich eine Ehre, zweimal auf dieser Bühne stehen zu dürfen. Tausend Dank dafür. [0.27005696296691895, 0.5391750335693359, -0.2580486238002777, -0.6613674759864807, 0.6738830804824829, ...]
    Ich bin wirklich begeistert von dieser Konferenz, und ich danke Ihnen allen für die vielen netten Kommentare zu meiner Rede vorgestern Abend. [-0.25320106744766235, 0.04791366308927536, -0.13174884021282196, -0.7357578277587891, 0.2366354614496231, ...]
  • Loss: MSELoss

en-es

  • Dataset: en-es at d366ddd
  • Size: 990 evaluation samples
  • Columns: non_english and label
  • Approximate statistics based on the first 1000 samples:
    non_english label
    type string list
    details
    • min: 4 tokens
    • mean: 26.47 tokens
    • max: 128 tokens
    • size: 768 elements
  • Samples:
    non_english label
    Muchas gracias Chris. [-0.4331263303756714, 1.0602688789367676, -0.07791043072938919, -0.4170420169830322, 1.6768444776535034, ...]
    Y es en verdad un gran honor tener la oportunidad de venir a este escenario por segunda vez. Estoy extremadamente agradecido. [0.27005696296691895, 0.5391750335693359, -0.2580486238002777, -0.6613674759864807, 0.6738830804824829, ...]
    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. [-0.25320106744766235, 0.04791366308927536, -0.13174884021282196, -0.7357578277587891, 0.2366354614496231, ...]
  • Loss: MSELoss

en-tr

  • Dataset: en-tr at d366ddd
  • Size: 993 evaluation samples
  • Columns: non_english and label
  • Approximate statistics based on the first 1000 samples:
    non_english label
    type string list
    details
    • min: 4 tokens
    • mean: 25.4 tokens
    • max: 128 tokens
    • size: 768 elements
  • Samples:
    non_english label
    Çok teşekkür ederim Chris. [-0.4331263303756714, 1.0602688789367676, -0.07791043072938919, -0.4170420169830322, 1.6768444776535034, ...]
    Bu sahnede ikinci kez yer alma fırsatına sahip olmak gerçekten büyük bir onur. Çok minnettarım. [0.27005696296691895, 0.5391750335693359, -0.2580486238002777, -0.6613674759864807, 0.6738830804824829, ...]
    Bu konferansta çok mutlu oldum, ve anlattıklarımla ilgili güzel yorumlarınız için sizlere çok teşekkür ederim. [-0.25320106744766235, 0.04791366308927536, -0.13174884021282196, -0.7357578277587891, 0.2366354614496231, ...]
  • Loss: MSELoss

en-it

  • Dataset: en-it at d366ddd
  • Size: 993 evaluation samples
  • Columns: non_english and label
  • Approximate statistics based on the first 1000 samples:
    non_english label
    type string list
    details
    • min: 4 tokens
    • mean: 27.94 tokens
    • max: 128 tokens
    • size: 768 elements
  • Samples:
    non_english label
    Grazie mille, Chris. [-0.4331263303756714, 1.0602688789367676, -0.07791043072938919, -0.4170420169830322, 1.6768444776535034, ...]
    E’ veramente un grande onore venire su questo palco due volte. Vi sono estremamente grato. [0.27005696296691895, 0.5391750335693359, -0.2580486238002777, -0.6613674759864807, 0.6738830804824829, ...]
    Sono impressionato da questa conferenza, e voglio ringraziare tutti voi per i tanti, lusinghieri commenti, anche perché... Ne ho bisogno!! [-0.25320106744766235, 0.04791366308927536, -0.13174884021282196, -0.7357578277587891, 0.2366354614496231, ...]
  • Loss: MSELoss

Training Hyperparameters

Non-Default Hyperparameters

  • eval_strategy: steps
  • per_device_train_batch_size: 64
  • per_device_eval_batch_size: 64
  • learning_rate: 2e-05
  • num_train_epochs: 5
  • warmup_ratio: 0.1
  • fp16: True

All Hyperparameters

Click to expand
  • overwrite_output_dir: False
  • do_predict: False
  • eval_strategy: steps
  • prediction_loss_only: False
  • 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: 1
  • eval_accumulation_steps: None
  • learning_rate: 2e-05
  • weight_decay: 0.0
  • adam_beta1: 0.9
  • adam_beta2: 0.999
  • adam_epsilon: 1e-08
  • max_grad_norm: 1.0
  • num_train_epochs: 5
  • 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
  • no_cuda: False
  • use_cpu: False
  • use_mps_device: False
  • seed: 42
  • data_seed: None
  • jit_mode_eval: False
  • use_ipex: False
  • bf16: False
  • fp16: True
  • fp16_opt_level: O1
  • half_precision_backend: auto
  • bf16_full_eval: False
  • fp16_full_eval: False
  • tf32: None
  • local_rank: 0
  • ddp_backend: None
  • tpu_num_cores: None
  • tpu_metrics_debug: False
  • debug: []
  • dataloader_drop_last: False
  • dataloader_num_workers: 0
  • dataloader_prefetch_factor: None
  • past_index: -1
  • disable_tqdm: False
  • remove_unused_columns: True
  • label_names: None
  • load_best_model_at_end: False
  • ignore_data_skip: False
  • fsdp: []
  • fsdp_min_num_params: 0
  • fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
  • fsdp_transformer_layer_cls_to_wrap: None
  • accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
  • deepspeed: None
  • label_smoothing_factor: 0.0
  • optim: adamw_torch
  • optim_args: None
  • adafactor: False
  • group_by_length: False
  • length_column_name: length
  • ddp_find_unused_parameters: None
  • ddp_bucket_cap_mb: None
  • ddp_broadcast_buffers: None
  • dataloader_pin_memory: True
  • dataloader_persistent_workers: False
  • skip_memory_metrics: True
  • use_legacy_prediction_loop: False
  • push_to_hub: False
  • resume_from_checkpoint: None
  • hub_model_id: None
  • hub_strategy: every_save
  • hub_private_repo: False
  • hub_always_push: False
  • gradient_checkpointing: False
  • gradient_checkpointing_kwargs: None
  • include_inputs_for_metrics: False
  • eval_do_concat_batches: True
  • fp16_backend: auto
  • push_to_hub_model_id: None
  • push_to_hub_organization: None
  • mp_parameters:
  • auto_find_batch_size: False
  • full_determinism: False
  • torchdynamo: None
  • ray_scope: last
  • ddp_timeout: 1800
  • torch_compile: False
  • torch_compile_backend: None
  • torch_compile_mode: None
  • dispatch_batches: None
  • split_batches: None
  • include_tokens_per_second: False
  • include_num_input_tokens_seen: False
  • neftune_noise_alpha: None
  • optim_target_modules: None
  • batch_sampler: batch_sampler
  • multi_dataset_batch_sampler: proportional

Training Logs

Epoch Step Training Loss en-ar loss en-it loss en-de loss en-fr loss en-es loss en-tr loss en-ar_mean_accuracy en-ar_negative_mse en-de_mean_accuracy en-de_negative_mse en-es_mean_accuracy en-es_negative_mse en-fr_mean_accuracy en-fr_negative_mse en-it_mean_accuracy en-it_negative_mse en-tr_mean_accuracy en-tr_negative_mse sts17-en-ar-test_spearman_max sts17-en-de-test_spearman_max sts17-en-tr-test_spearman_max sts17-es-en-test_spearman_max sts17-fr-en-test_spearman_max sts17-it-en-test_spearman_max
0.2110 100 0.5581 - - - - - - - - - - - - - - - - - - - - - - - -
0.4219 200 0.3071 - - - - - - - - - - - - - - - - - - - - - - - -
0.6329 300 0.2675 - - - - - - - - - - - - - - - - - - - - - - - -
0.8439 400 0.2606 - - - - - - - - - - - - - - - - - - - - - - - -
1.0549 500 0.2589 0.2519 0.2498 0.2511 0.2488 0.2503 0.2512 0.1254 -25.1903 0.2523 -25.1089 0.2591 -25.0276 0.2409 -24.8803 0.2180 -24.9768 0.1158 -25.1219 0.0308 0.1281 0.1610 0.1465 0.0552 0.0518
1.2658 600 0.2504 - - - - - - - - - - - - - - - - - - - - - - - -
1.4768 700 0.2427 - - - - - - - - - - - - - - - - - - - - - - - -
1.6878 800 0.2337 - - - - - - - - - - - - - - - - - - - - - - - -
1.8987 900 0.2246 - - - - - - - - - - - - - - - - - - - - - - - -
2.1097 1000 0.2197 0.2202 0.2157 0.2151 0.2147 0.2139 0.2218 0.5841 -22.0204 0.8012 -21.5087 0.8495 -21.3935 0.7959 -21.4660 0.7815 -21.5699 0.6007 -22.1778 0.3346 0.4013 0.4727 0.3353 0.3827 0.3292
2.3207 1100 0.2163 - - - - - - - - - - - - - - - - - - - - - - - -
2.5316 1200 0.2123 - - - - - - - - - - - - - - - - - - - - - - - -
2.7426 1300 0.2069 - - - - - - - - - - - - - - - - - - - - - - - -
2.9536 1400 0.2048 - - - - - - - - - - - - - - - - - - - - - - - -
3.1646 1500 0.2009 0.2086 0.2029 0.2022 0.2012 0.2002 0.2111 0.7367 -20.8567 0.8739 -20.2247 0.9303 -20.0215 0.8755 -20.1213 0.8600 -20.2900 0.7165 -21.1119 0.4087 0.5473 0.5551 0.4724 0.4882 0.4690
3.3755 1600 0.2019 - - - - - - - - - - - - - - - - - - - - - - - -
3.5865 1700 0.1989 - - - - - - - - - - - - - - - - - - - - - - - -
3.7975 1800 0.196 - - - - - - - - - - - - - - - - - - - - - - - -
4.0084 1900 0.1943 - - - - - - - - - - - - - - - - - - - - - - - -
4.2194 2000 0.194 0.2040 0.1977 0.1973 0.1962 0.1947 0.2075 0.7714 -20.3955 0.8915 -19.7279 0.9449 -19.4724 0.8942 -19.6232 0.8807 -19.7699 0.7432 -20.7547 0.4425 0.5618 0.5819 0.5021 0.5334 0.5250
4.4304 2100 0.1951 - - - - - - - - - - - - - - - - - - - - - - - -
4.6414 2200 0.1928 - - - - - - - - - - - - - - - - - - - - - - - -
4.8523 2300 0.1909 - - - - - - - - - - - - - - - - - - - - - - - -

Environmental Impact

Carbon emissions were measured using CodeCarbon.

  • Energy Consumed: 0.060 kWh
  • Carbon Emitted: 0.023 kg of CO2
  • Hours Used: 0.179 hours

Training Hardware

  • On Cloud: No
  • GPU Model: 1 x NVIDIA GeForce RTX 3090
  • CPU Model: 13th Gen Intel(R) Core(TM) i7-13700K
  • RAM Size: 31.78 GB

Framework Versions

  • Python: 3.11.6
  • Sentence Transformers: 3.0.0.dev0
  • Transformers: 4.41.0.dev0
  • PyTorch: 2.3.0+cu121
  • Accelerate: 0.26.1
  • Datasets: 2.18.0
  • Tokenizers: 0.19.1

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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