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luanafelbarros
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xlm-roberta-base-multilingual-mkqa

Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:234000
loss:MSELoss
Eval Results (legacy)
text-embeddings-inference
Model card Files
xet
 Community

SentenceTransformer based on FacebookAI/xlm-roberta-base

This is a sentence-transformers model finetuned from FacebookAI/xlm-roberta-base. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

  • Model Type: Sentence Transformer
  • Base model: FacebookAI/xlm-roberta-base
  • Maximum Sequence Length: 128 tokens
  • Output Dimensionality: 768 dimensions
  • Similarity Function: Cosine Similarity

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("luanafelbarros/xlm-roberta-base-multilingual-mkqa")
# Run inference
sentences = [
    'where does food wars anime end in the manga',
    '《食戟之靈》漫畫幾時完',
    'zh_hk',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

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

Evaluation

Metrics

Knowledge Distillation

  • Datasets: MSE-val-en-to-ar, MSE-val-en-to-da, MSE-val-en-to-de, MSE-val-en-to-en, MSE-val-en-to-es, MSE-val-en-to-fi, MSE-val-en-to-fr, MSE-val-en-to-he, MSE-val-en-to-hu, MSE-val-en-to-it, MSE-val-en-to-ja, MSE-val-en-to-ko, MSE-val-en-to-km, MSE-val-en-to-ms, MSE-val-en-to-nl, MSE-val-en-to-no, MSE-val-en-to-pl, MSE-val-en-to-pt, MSE-val-en-to-ru, MSE-val-en-to-sv, MSE-val-en-to-th, MSE-val-en-to-tr, MSE-val-en-to-vi, MSE-val-en-to-zh_cn, MSE-val-en-to-zh_hk and MSE-val-en-to-zh_tw
  • Evaluated with MSEEvaluator
Metric MSE-val-en-to-ar MSE-val-en-to-da MSE-val-en-to-de MSE-val-en-to-en MSE-val-en-to-es MSE-val-en-to-fi MSE-val-en-to-fr MSE-val-en-to-he MSE-val-en-to-hu MSE-val-en-to-it MSE-val-en-to-ja MSE-val-en-to-ko MSE-val-en-to-km MSE-val-en-to-ms MSE-val-en-to-nl MSE-val-en-to-no MSE-val-en-to-pl MSE-val-en-to-pt MSE-val-en-to-ru MSE-val-en-to-sv MSE-val-en-to-th MSE-val-en-to-tr MSE-val-en-to-vi MSE-val-en-to-zh_cn MSE-val-en-to-zh_hk MSE-val-en-to-zh_tw
negative_mse -19.9351 -16.2271 -17.0315 -14.7466 -16.739 -17.6995 -16.8551 -19.1143 -17.8625 -16.9311 -18.7746 -19.6834 -19.3393 -16.4985 -15.9824 -16.2615 -17.5108 -16.5283 -17.3583 -16.3128 -17.5869 -17.3905 -17.175 -18.1255 -18.1899 -18.6787

Training Details

Training Dataset

Unnamed Dataset

  • Size: 234,000 training samples
  • Columns: english, non-english, target, and label
  • Approximate statistics based on the first 1000 samples:
    english non-english target label
    type string string string list
    details
    • min: 10 tokens
    • mean: 13.21 tokens
    • max: 19 tokens
    • min: 7 tokens
    • mean: 13.87 tokens
    • max: 31 tokens
    • min: 3 tokens
    • mean: 3.38 tokens
    • max: 6 tokens
    • size: 768 elements
  • Samples:
    english non-english target label
    what are all the wizard of oz movies the wizard of oz ما هي كل افلام ar [0.5303382277488708, -0.31762194633483887, -0.2945275902748108, -0.6602655649185181, -1.4617066383361816, ...]
    what are all the wizard of oz movies hvad er alle troldmanden fra oz filmene da [0.5303382277488708, -0.31762194633483887, -0.2945275902748108, -0.6602655649185181, -1.4617066383361816, ...]
    what are all the wizard of oz movies Wie heißen alle Der Zauberer von Oz Filme de [0.5303382277488708, -0.31762194633483887, -0.2945275902748108, -0.6602655649185181, -1.4617066383361816, ...]
  • Loss: MSELoss

Evaluation Dataset

Unnamed Dataset

  • Size: 13,000 evaluation samples
  • Columns: english, non-english, target, and label
  • Approximate statistics based on the first 1000 samples:
    english non-english target label
    type string string string list
    details
    • min: 10 tokens
    • mean: 13.05 tokens
    • max: 22 tokens
    • min: 5 tokens
    • mean: 13.79 tokens
    • max: 34 tokens
    • min: 3 tokens
    • mean: 3.38 tokens
    • max: 6 tokens
    • size: 768 elements
  • Samples:
    english non-english target label
    a change to the constitution must be approved by يجب الموافقة على تغيير الدستور ar [1.0918692350387573, 0.8024187684059143, 0.23035858571529388, 0.16300565004348755, -0.6033854484558105, ...]
    a change to the constitution must be approved by en ændring af forfatningen skal godkendes af da [1.0918692350387573, 0.8024187684059143, 0.23035858571529388, 0.16300565004348755, -0.6033854484558105, ...]
    a change to the constitution must be approved by Eine Änderung der Verfassung muss gebilligt werden durch de [1.0918692350387573, 0.8024187684059143, 0.23035858571529388, 0.16300565004348755, -0.6033854484558105, ...]
  • 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
  • warmup_ratio: 0.1
  • fp16: 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: 1
  • eval_accumulation_steps: None
  • torch_empty_cache_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: 3
  • max_steps: -1
  • lr_scheduler_type: linear
  • lr_scheduler_kwargs: {}
  • warmup_ratio: 0.1
  • warmup_steps: 0
  • log_level: passive
  • log_level_replica: warning
  • log_on_each_node: True
  • logging_nan_inf_filter: True
  • save_safetensors: True
  • save_on_each_node: False
  • save_only_model: False
  • restore_callback_states_from_checkpoint: False
  • no_cuda: False
  • use_cpu: False
  • use_mps_device: False
  • seed: 42
  • data_seed: None
  • jit_mode_eval: False
  • use_ipex: False
  • bf16: False
  • fp16: 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: 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 Validation Loss MSE-val-en-to-ar_negative_mse MSE-val-en-to-da_negative_mse MSE-val-en-to-de_negative_mse MSE-val-en-to-en_negative_mse MSE-val-en-to-es_negative_mse MSE-val-en-to-fi_negative_mse MSE-val-en-to-fr_negative_mse MSE-val-en-to-he_negative_mse MSE-val-en-to-hu_negative_mse MSE-val-en-to-it_negative_mse MSE-val-en-to-ja_negative_mse MSE-val-en-to-ko_negative_mse MSE-val-en-to-km_negative_mse MSE-val-en-to-ms_negative_mse MSE-val-en-to-nl_negative_mse MSE-val-en-to-no_negative_mse MSE-val-en-to-pl_negative_mse MSE-val-en-to-pt_negative_mse MSE-val-en-to-ru_negative_mse MSE-val-en-to-sv_negative_mse MSE-val-en-to-th_negative_mse MSE-val-en-to-tr_negative_mse MSE-val-en-to-vi_negative_mse MSE-val-en-to-zh_cn_negative_mse MSE-val-en-to-zh_hk_negative_mse MSE-val-en-to-zh_tw_negative_mse
0.0273 100 0.7471 - - - - - - - - - - - - - - - - - - - - - - - - - - -
0.0547 200 0.5344 - - - - - - - - - - - - - - - - - - - - - - - - - - -
0.0820 300 0.4011 - - - - - - - - - - - - - - - - - - - - - - - - - - -
0.1094 400 0.3686 - - - - - - - - - - - - - - - - - - - - - - - - - - -
0.1367 500 0.3558 - - - - - - - - - - - - - - - - - - - - - - - - - - -
0.1641 600 0.3527 - - - - - - - - - - - - - - - - - - - - - - - - - - -
0.1914 700 0.3479 - - - - - - - - - - - - - - - - - - - - - - - - - - -
0.2188 800 0.3373 - - - - - - - - - - - - - - - - - - - - - - - - - - -
0.2461 900 0.3315 - - - - - - - - - - - - - - - - - - - - - - - - - - -
0.2734 1000 0.3243 0.3143 -31.0036 -30.4995 -30.5974 -30.3236 -30.5190 -30.6680 -30.5902 -30.8805 -30.7873 -30.6191 -30.7149 -30.7932 -30.8955 -30.5254 -30.5554 -30.5243 -30.6522 -30.5353 -30.5800 -30.5240 -30.7348 -30.7127 -30.6429 -30.5608 -30.5626 -30.5837
0.3008 1100 0.3175 - - - - - - - - - - - - - - - - - - - - - - - - - - -
0.3281 1200 0.3126 - - - - - - - - - - - - - - - - - - - - - - - - - - -
0.3555 1300 0.3082 - - - - - - - - - - - - - - - - - - - - - - - - - - -
0.3828 1400 0.3049 - - - - - - - - - - - - - - - - - - - - - - - - - - -
0.4102 1500 0.3019 - - - - - - - - - - - - - - - - - - - - - - - - - - -
0.4375 1600 0.2988 - - - - - - - - - - - - - - - - - - - - - - - - - - -
0.4649 1700 0.2979 - - - - - - - - - - - - - - - - - - - - - - - - - - -
0.4922 1800 0.2926 - - - - - - - - - - - - - - - - - - - - - - - - - - -
0.5196 1900 0.2885 - - - - - - - - - - - - - - - - - - - - - - - - - - -
0.5469 2000 0.2879 0.2787 -26.4435 -25.3475 -25.5656 -24.8280 -25.4096 -25.8103 -25.4399 -26.1209 -25.8292 -25.5216 -26.0866 -26.4725 -26.2586 -25.5986 -25.3495 -25.2907 -25.6509 -25.3489 -25.4795 -25.3660 -25.7628 -25.7572 -25.6763 -25.7273 -25.7893 -25.8524
0.5742 2100 0.2843 - - - - - - - - - - - - - - - - - - - - - - - - - - -
0.6016 2200 0.2821 - - - - - - - - - - - - - - - - - - - - - - - - - - -
0.6289 2300 0.2795 - - - - - - - - - - - - - - - - - - - - - - - - - - -
0.6563 2400 0.2808 - - - - - - - - - - - - - - - - - - - - - - - - - - -
0.6836 2500 0.2771 - - - - - - - - - - - - - - - - - - - - - - - - - - -
0.7110 2600 0.2745 - - - - - - - - - - - - - - - - - - - - - - - - - - -
0.7383 2700 0.272 - - - - - - - - - - - - - - - - - - - - - - - - - - -
0.7657 2800 0.2711 - - - - - - - - - - - - - - - - - - - - - - - - - - -
0.7930 2900 0.2685 - - - - - - - - - - - - - - - - - - - - - - - - - - -
0.8203 3000 0.267 0.2638 -24.2447 -22.3985 -22.7542 -21.5879 -22.5929 -23.2891 -22.6798 -23.7047 -23.1739 -22.7708 -23.5962 -24.2250 -23.9269 -22.8039 -22.2681 -22.3432 -22.9390 -22.5717 -22.8201 -22.4143 -23.1236 -23.1100 -22.9658 -23.0786 -23.2390 -23.3243
0.8477 3100 0.2718 - - - - - - - - - - - - - - - - - - - - - - - - - - -
0.8750 3200 0.2674 - - - - - - - - - - - - - - - - - - - - - - - - - - -
0.9024 3300 0.2662 - - - - - - - - - - - - - - - - - - - - - - - - - - -
0.9297 3400 0.2631 - - - - - - - - - - - - - - - - - - - - - - - - - - -
0.9571 3500 0.26 - - - - - - - - - - - - - - - - - - - - - - - - - - -
0.9844 3600 0.2586 - - - - - - - - - - - - - - - - - - - - - - - - - - -
1.0118 3700 0.2575 - - - - - - - - - - - - - - - - - - - - - - - - - - -
1.0391 3800 0.2549 - - - - - - - - - - - - - - - - - - - - - - - - - - -
1.0664 3900 0.2529 - - - - - - - - - - - - - - - - - - - - - - - - - - -
1.0938 4000 0.2511 0.2469 -22.9347 -20.4196 -20.9011 -19.3762 -20.7242 -21.5322 -20.7711 -22.3208 -21.5176 -20.9047 -22.1008 -22.8701 -22.4827 -20.7383 -20.2571 -20.3842 -21.1960 -20.6791 -21.0474 -20.4460 -21.3999 -21.3937 -21.1382 -21.5265 -21.6918 -21.8791
1.1211 4100 0.2502 - - - - - - - - - - - - - - - - - - - - - - - - - - -
1.1485 4200 0.2491 - - - - - - - - - - - - - - - - - - - - - - - - - - -
1.1758 4300 0.248 - - - - - - - - - - - - - - - - - - - - - - - - - - -
1.2032 4400 0.2463 - - - - - - - - - - - - - - - - - - - - - - - - - - -
1.2305 4500 0.2445 - - - - - - - - - - - - - - - - - - - - - - - - - - -
1.2579 4600 0.2432 - - - - - - - - - - - - - - - - - - - - - - - - - - -
1.2852 4700 0.2419 - - - - - - - - - - - - - - - - - - - - - - - - - - -
1.3126 4800 0.2405 - - - - - - - - - - - - - - - - - - - - - - - - - - -
1.3399 4900 0.2404 - - - - - - - - - - - - - - - - - - - - - - - - - - -
1.3672 5000 0.2394 0.2354 -21.7963 -18.8622 -19.4636 -17.6703 -19.2473 -20.1437 -19.3378 -21.1200 -20.1560 -19.4587 -20.9473 -21.6343 -21.2979 -19.1964 -18.6653 -18.8517 -19.8565 -19.1500 -19.6760 -18.9243 -19.9718 -19.9191 -19.6695 -20.2707 -20.4090 -20.6846
1.3946 5100 0.2375 - - - - - - - - - - - - - - - - - - - - - - - - - - -
1.4219 5200 0.2374 - - - - - - - - - - - - - - - - - - - - - - - - - - -
1.4493 5300 0.236 - - - - - - - - - - - - - - - - - - - - - - - - - - -
1.4766 5400 0.2335 - - - - - - - - - - - - - - - - - - - - - - - - - - -
1.5040 5500 0.2346 - - - - - - - - - - - - - - - - - - - - - - - - - - -
1.5313 5600 0.2335 - - - - - - - - - - - - - - - - - - - - - - - - - - -
1.5587 5700 0.232 - - - - - - - - - - - - - - - - - - - - - - - - - - -
1.5860 5800 0.2314 - - - - - - - - - - - - - - - - - - - - - - - - - - -
1.6133 5900 0.2304 - - - - - - - - - - - - - - - - - - - - - - - - - - -
1.6407 6000 0.2303 0.2289 -21.1967 -17.9192 -18.5833 -16.6276 -18.3510 -19.2977 -18.4551 -20.3960 -19.3202 -18.5573 -20.1420 -20.9358 -20.6084 -18.2396 -17.7261 -17.9322 -19.0167 -18.2305 -18.8471 -17.9794 -19.1440 -19.0105 -18.7845 -19.4778 -19.6095 -19.9643
1.6680 6100 0.2294 - - - - - - - - - - - - - - - - - - - - - - - - - - -
1.6954 6200 0.229 - - - - - - - - - - - - - - - - - - - - - - - - - - -
1.7227 6300 0.2275 - - - - - - - - - - - - - - - - - - - - - - - - - - -
1.7501 6400 0.2285 - - - - - - - - - - - - - - - - - - - - - - - - - - -
1.7774 6500 0.2279 - - - - - - - - - - - - - - - - - - - - - - - - - - -
1.8048 6600 0.2275 - - - - - - - - - - - - - - - - - - - - - - - - - - -
1.8321 6700 0.2256 - - - - - - - - - - - - - - - - - - - - - - - - - - -
1.8594 6800 0.2259 - - - - - - - - - - - - - - - - - - - - - - - - - - -
1.8868 6900 0.2237 - - - - - - - - - - - - - - - - - - - - - - - - - - -
1.9141 7000 0.2232 0.2242 -20.6888 -17.2295 -17.9547 -15.8517 -17.7267 -18.6854 -17.8191 -19.8853 -18.7432 -17.9054 -19.5866 -20.4321 -20.1381 -17.5215 -16.9982 -17.2683 -18.4340 -17.5295 -18.2454 -17.3006 -18.5072 -18.3554 -18.1438 -18.9634 -19.0843 -19.4826
1.9415 7100 0.2231 - - - - - - - - - - - - - - - - - - - - - - - - - - -
1.9688 7200 0.2225 - - - - - - - - - - - - - - - - - - - - - - - - - - -
1.9962 7300 0.2235 - - - - - - - - - - - - - - - - - - - - - - - - - - -
2.0235 7400 0.2224 - - - - - - - - - - - - - - - - - - - - - - - - - - -
2.0509 7500 0.2206 - - - - - - - - - - - - - - - - - - - - - - - - - - -
2.0782 7600 0.2205 - - - - - - - - - - - - - - - - - - - - - - - - - - -
2.1056 7700 0.2196 - - - - - - - - - - - - - - - - - - - - - - - - - - -
2.1329 7800 0.22 - - - - - - - - - - - - - - - - - - - - - - - - - - -
2.1602 7900 0.2188 - - - - - - - - - - - - - - - - - - - - - - - - - - -
2.1876 8000 0.2184 0.2209 -20.3380 -16.7285 -17.5078 -15.3142 -17.2366 -18.1903 -17.3419 -19.5057 -18.2970 -17.4283 -19.1880 -20.0709 -19.7478 -17.0291 -16.5125 -16.7629 -17.9586 -17.0487 -17.7907 -16.8237 -18.0585 -17.8714 -17.6527 -18.5499 -18.6504 -19.0688
2.2149 8100 0.2189 - - - - - - - - - - - - - - - - - - - - - - - - - - -
2.2423 8200 0.2178 - - - - - - - - - - - - - - - - - - - - - - - - - - -
2.2696 8300 0.2185 - - - - - - - - - - - - - - - - - - - - - - - - - - -
2.2970 8400 0.2175 - - - - - - - - - - - - - - - - - - - - - - - - - - -
2.3243 8500 0.2183 - - - - - - - - - - - - - - - - - - - - - - - - - - -
2.3517 8600 0.2176 - - - - - - - - - - - - - - - - - - - - - - - - - - -
2.3790 8700 0.2169 - - - - - - - - - - - - - - - - - - - - - - - - - - -
2.4063 8800 0.2172 - - - - - - - - - - - - - - - - - - - - - - - - - - -
2.4337 8900 0.2153 - - - - - - - - - - - - - - - - - - - - - - - - - - -
2.4610 9000 0.2162 0.2187 -20.1028 -16.4147 -17.2107 -14.9595 -16.9406 -17.9101 -17.0441 -19.2680 -18.0594 -17.1276 -18.9403 -19.8407 -19.5169 -16.6976 -16.1859 -16.4554 -17.6828 -16.7360 -17.5378 -16.5167 -17.7710 -17.5853 -17.3717 -18.3032 -18.3627 -18.8466
2.4884 9100 0.2159 - - - - - - - - - - - - - - - - - - - - - - - - - - -
2.5157 9200 0.2161 - - - - - - - - - - - - - - - - - - - - - - - - - - -
2.5431 9300 0.2148 - - - - - - - - - - - - - - - - - - - - - - - - - - -
2.5704 9400 0.2148 - - - - - - - - - - - - - - - - - - - - - - - - - - -
2.5978 9500 0.2154 - - - - - - - - - - - - - - - - - - - - - - - - - - -
2.6251 9600 0.2142 - - - - - - - - - - - - - - - - - - - - - - - - - - -
2.6524 9700 0.2144 - - - - - - - - - - - - - - - - - - - - - - - - - - -
2.6798 9800 0.215 - - - - - - - - - - - - - - - - - - - - - - - - - - -
2.7071 9900 0.2142 - - - - - - - - - - - - - - - - - - - - - - - - - - -
2.7345 10000 0.2139 0.2174 -19.9351 -16.2271 -17.0315 -14.7466 -16.7390 -17.6995 -16.8551 -19.1143 -17.8625 -16.9311 -18.7746 -19.6834 -19.3393 -16.4985 -15.9824 -16.2615 -17.5108 -16.5283 -17.3583 -16.3128 -17.5869 -17.3905 -17.1750 -18.1255 -18.1899 -18.6787
2.7618 10100 0.2134 - - - - - - - - - - - - - - - - - - - - - - - - - - -
2.7892 10200 0.2141 - - - - - - - - - - - - - - - - - - - - - - - - - - -
2.8165 10300 0.2147 - - - - - - - - - - - - - - - - - - - - - - - - - - -
2.8439 10400 0.2138 - - - - - - - - - - - - - - - - - - - - - - - - - - -
2.8712 10500 0.2133 - - - - - - - - - - - - - - - - - - - - - - - - - - -
2.8986 10600 0.2129 - - - - - - - - - - - - - - - - - - - - - - - - - - -
2.9259 10700 0.2129 - - - - - - - - - - - - - - - - - - - - - - - - - - -
2.9532 10800 0.2129 - - - - - - - - - - - - - - - - - - - - - - - - - - -
2.9806 10900 0.214 - - - - - - - - - - - - - - - - - - - - - - - - - - -

Framework Versions

  • Python: 3.10.12
  • Sentence Transformers: 3.3.1
  • 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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