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swahili-serengeti-E250-nli-matryoshka

Sentence Similarity
sentence-transformers
Safetensors
electra
feature-extraction
Generated from Trainer
dataset_size:557850
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results
Inference Endpoints
Model card Files Community
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metadata
language: []
library_name: sentence-transformers
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:557850
  - loss:MatryoshkaLoss
  - loss:MultipleNegativesRankingLoss
base_model: UBC-NLP/serengeti-E250
datasets: []
metrics:
  - pearson_cosine
  - spearman_cosine
  - pearson_manhattan
  - spearman_manhattan
  - pearson_euclidean
  - spearman_euclidean
  - pearson_dot
  - spearman_dot
  - pearson_max
  - spearman_max
widget:
  - source_sentence: >-
      Mwanamume aliyepangwa vizuri anasimama kwa mguu mmoja karibu na pwani safi
      ya bahari.
    sentences:
      - mtu anacheka wakati wa kufua nguo
      - Mwanamume fulani yuko nje karibu na ufuo wa bahari.
      - Mwanamume fulani ameketi kwenye sofa yake.
  - source_sentence: >-
      Mwanamume mwenye ngozi nyeusi akivuta sigareti karibu na chombo cha taka
      cha kijani.
    sentences:
      - Karibu na chombo cha taka mwanamume huyo alisimama na kuvuta sigareti
      - Kitanda ni chafu.
      - >-
        Alipokuwa kwenye dimbwi la kuogelea mvulana huyo mwenye ugonjwa wa
        albino alijihadhari na jua kupita kiasi
  - source_sentence: >-
      Mwanamume kijana mwenye nywele nyekundu anaketi ukutani akisoma gazeti
      huku mwanamke na msichana mchanga wakipita.
    sentences:
      - >-
        Mwanamume aliyevalia shati la bluu amegonga ukuta kando ya barabara na
        gari la bluu na gari nyekundu lenye maji nyuma.
      - >-
        Mwanamume mchanga anatazama gazeti huku wanawake wawili wakipita karibu
        naye.
      - >-
        Mwanamume huyo mchanga analala huku Mama akimwongoza binti yake kwenye
        bustani.
  - source_sentence: Wasichana wako nje.
    sentences:
      - Wasichana wawili wakisafiri kwenye sehemu ya kusisimua.
      - >-
        Kuna watu watatu wakiongoza gari linaloweza kugeuzwa-geuzwa wakipita
        watu wengine.
      - >-
        Wasichana watatu wamesimama pamoja katika chumba, mmoja anasikiliza,
        mwingine anaandika ukutani na wa tatu anaongea nao.
  - source_sentence: >-
      Mwanamume aliyevalia koti la bluu la kuzuia upepo, amelala uso chini
      kwenye benchi ya bustani, akiwa na chupa ya pombe iliyofungwa kwenye
      mojawapo ya miguu ya benchi.
    sentences:
      - Mwanamume amelala uso chini kwenye benchi ya bustani.
      - Mwanamke anaunganisha uzi katika mipira kando ya rundo la mipira
      - Mwanamume fulani anacheza dansi kwenye klabu hiyo akifungua chupa.
pipeline_tag: sentence-similarity
model-index:
  - name: SentenceTransformer based on UBC-NLP/serengeti-E250
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts test 768
          type: sts-test-768
        metrics:
          - type: pearson_cosine
            value: 0.7113368462970326
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.706531149090894
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.7134349154531519
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.7023005843725415
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.7137962920501839
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.7020941994285994
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.3920803758314358
            name: Pearson Dot
          - type: spearman_dot
            value: 0.3601086266312748
            name: Spearman Dot
          - type: pearson_max
            value: 0.7137962920501839
            name: Pearson Max
          - type: spearman_max
            value: 0.706531149090894
            name: Spearman Max
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts test 512
          type: sts-test-512
        metrics:
          - type: pearson_cosine
            value: 0.7090618585285485
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.7045766195278508
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.7129955390384859
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.7021695501159393
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.7138697740168334
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.7032055408694606
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.39352767760073326
            name: Pearson Dot
          - type: spearman_dot
            value: 0.3628376619678567
            name: Spearman Dot
          - type: pearson_max
            value: 0.7138697740168334
            name: Pearson Max
          - type: spearman_max
            value: 0.7045766195278508
            name: Spearman Max
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts test 256
          type: sts-test-256
        metrics:
          - type: pearson_cosine
            value: 0.7067837420770313
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.7044452613349608
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.7137425083925593
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.7032345257234871
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.7146861583047366
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.7039212190752775
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.37462153895392747
            name: Pearson Dot
          - type: spearman_dot
            value: 0.34441190254194326
            name: Spearman Dot
          - type: pearson_max
            value: 0.7146861583047366
            name: Pearson Max
          - type: spearman_max
            value: 0.7044452613349608
            name: Spearman Max
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts test 128
          type: sts-test-128
        metrics:
          - type: pearson_cosine
            value: 0.7046839100746249
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.7050559450173808
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.7120431790616113
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.7010054121016321
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.7132280398983044
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.701626975970973
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.35455409787695585
            name: Pearson Dot
          - type: spearman_dot
            value: 0.32292034736383524
            name: Spearman Dot
          - type: pearson_max
            value: 0.7132280398983044
            name: Pearson Max
          - type: spearman_max
            value: 0.7050559450173808
            name: Spearman Max
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts test 64
          type: sts-test-64
        metrics:
          - type: pearson_cosine
            value: 0.7012310578605567
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.7044132231714119
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.7091211798265005
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.6972792688781575
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.7103033981031003
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.6985716335223231
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.3379821887901175
            name: Pearson Dot
          - type: spearman_dot
            value: 0.30513652558145304
            name: Spearman Dot
          - type: pearson_max
            value: 0.7103033981031003
            name: Pearson Max
          - type: spearman_max
            value: 0.7044132231714119
            name: Spearman Max

SentenceTransformer based on UBC-NLP/serengeti-E250

This is a sentence-transformers model finetuned from UBC-NLP/serengeti-E250. 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: UBC-NLP/serengeti-E250
  • Maximum Sequence Length: 512 tokens
  • Output Dimensionality: 768 tokens
  • Similarity Function: Cosine Similarity

Model Sources

Full Model Architecture 

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: ElectraModel 
  (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("sartifyllc/swahili-serengeti-E250-nli-matryoshka")
# Run inference
sentences = [
    'Mwanamume aliyevalia koti la bluu la kuzuia upepo, amelala uso chini kwenye benchi ya bustani, akiwa na chupa ya pombe iliyofungwa kwenye mojawapo ya miguu ya benchi.',
    'Mwanamume amelala uso chini kwenye benchi ya bustani.',
    'Mwanamume fulani anacheza dansi kwenye klabu hiyo akifungua chupa.',
]
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

Semantic Similarity

Metric Value
pearson_cosine 0.7113
spearman_cosine 0.7065
pearson_manhattan 0.7134
spearman_manhattan 0.7023
pearson_euclidean 0.7138
spearman_euclidean 0.7021
pearson_dot 0.3921
spearman_dot 0.3601
pearson_max 0.7138
spearman_max 0.7065

Semantic Similarity

Metric Value
pearson_cosine 0.7091
spearman_cosine 0.7046
pearson_manhattan 0.713
spearman_manhattan 0.7022
pearson_euclidean 0.7139
spearman_euclidean 0.7032
pearson_dot 0.3935
spearman_dot 0.3628
pearson_max 0.7139
spearman_max 0.7046

Semantic Similarity

Metric Value
pearson_cosine 0.7068
spearman_cosine 0.7044
pearson_manhattan 0.7137
spearman_manhattan 0.7032
pearson_euclidean 0.7147
spearman_euclidean 0.7039
pearson_dot 0.3746
spearman_dot 0.3444
pearson_max 0.7147
spearman_max 0.7044

Semantic Similarity

Metric Value
pearson_cosine 0.7047
spearman_cosine 0.7051
pearson_manhattan 0.712
spearman_manhattan 0.701
pearson_euclidean 0.7132
spearman_euclidean 0.7016
pearson_dot 0.3546
spearman_dot 0.3229
pearson_max 0.7132
spearman_max 0.7051

Semantic Similarity

Metric Value
pearson_cosine 0.7012
spearman_cosine 0.7044
pearson_manhattan 0.7091
spearman_manhattan 0.6973
pearson_euclidean 0.7103
spearman_euclidean 0.6986
pearson_dot 0.338
spearman_dot 0.3051
pearson_max 0.7103
spearman_max 0.7044

Training Details

Training Hyperparameters

Non-Default Hyperparameters

  • per_device_train_batch_size: 16
  • per_device_eval_batch_size: 16
  • learning_rate: 2e-05
  • num_train_epochs: 1
  • warmup_ratio: 0.1
  • bf16: True
  • batch_sampler: no_duplicates

All Hyperparameters

Click to expand
  • overwrite_output_dir: False
  • do_predict: False
  • prediction_loss_only: True
  • per_device_train_batch_size: 16
  • per_device_eval_batch_size: 16
  • 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: 1
  • 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: 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, '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
  • 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: no_duplicates
  • multi_dataset_batch_sampler: proportional

Training Logs

Click to expand
Epoch Step Training Loss sts-test-128_spearman_cosine sts-test-256_spearman_cosine sts-test-512_spearman_cosine sts-test-64_spearman_cosine sts-test-768_spearman_cosine
0.0057 100 25.7713 - - - - -
0.0115 200 20.7886 - - - - -
0.0172 300 17.0398 - - - - -
0.0229 400 15.3913 - - - - -
0.0287 500 14.0214 - - - - -
0.0344 600 12.2125 - - - - -
0.0402 700 10.3033 - - - - -
0.0459 800 9.3822 - - - - -
0.0516 900 8.9276 - - - - -
0.0574 1000 8.552 - - - - -
0.0631 1100 8.6293 - - - - -
0.0688 1200 8.5353 - - - - -
0.0746 1300 8.6431 - - - - -
0.0803 1400 8.3192 - - - - -
0.0860 1500 7.1834 - - - - -
0.0918 1600 6.7834 - - - - -
0.0975 1700 6.4758 - - - - -
0.1033 1800 6.756 - - - - -
0.1090 1900 7.807 - - - - -
0.1147 2000 6.8836 - - - - -
0.1205 2100 6.9948 - - - - -
0.1262 2200 6.5031 - - - - -
0.1319 2300 6.3596 - - - - -
0.1377 2400 6.0257 - - - - -
0.1434 2500 5.9757 - - - - -
0.1491 2600 5.464 - - - - -
0.1549 2700 5.6518 - - - - -
0.1606 2800 6.2899 - - - - -
0.1664 2900 6.4876 - - - - -
0.1721 3000 6.9466 - - - - -
0.1778 3100 6.8439 - - - - -
0.1836 3200 6.2545 - - - - -
0.1893 3300 5.9795 - - - - -
0.1950 3400 5.3904 - - - - -
0.2008 3500 6.2798 - - - - -
0.2065 3600 5.6882 - - - - -
0.2122 3700 6.195 - - - - -
0.2180 3800 5.8728 - - - - -
0.2237 3900 6.2428 - - - - -
0.2294 4000 5.801 - - - - -
0.2352 4100 5.6918 - - - - -
0.2409 4200 5.3977 - - - - -
0.2467 4300 5.8792 - - - - -
0.2524 4400 5.9297 - - - - -
0.2581 4500 6.161 - - - - -
0.2639 4600 5.6571 - - - - -
0.2696 4700 5.5849 - - - - -
0.2753 4800 5.6382 - - - - -
0.2811 4900 5.2978 - - - - -
0.2868 5000 5.108 - - - - -
0.2925 5100 5.1158 - - - - -
0.2983 5200 5.6218 - - - - -
0.3040 5300 5.643 - - - - -
0.3098 5400 5.6894 - - - - -
0.3155 5500 5.373 - - - - -
0.3212 5600 5.0673 - - - - -
0.3270 5700 5.1915 - - - - -
0.3327 5800 5.3705 - - - - -
0.3384 5900 5.6432 - - - - -
0.3442 6000 5.2567 - - - - -
0.3499 6100 5.4516 - - - - -
0.3556 6200 5.4844 - - - - -
0.3614 6300 4.8238 - - - - -
0.3671 6400 4.8271 - - - - -
0.3729 6500 4.9863 - - - - -
0.3786 6600 5.4894 - - - - -
0.3843 6700 4.95 - - - - -
0.3901 6800 5.0881 - - - - -
0.3958 6900 5.249 - - - - -
0.4015 7000 5.0082 - - - - -
0.4073 7100 5.5064 - - - - -
0.4130 7200 5.0885 - - - - -
0.4187 7300 5.0321 - - - - -
0.4245 7400 4.8212 - - - - -
0.4302 7500 5.4231 - - - - -
0.4360 7600 4.7687 - - - - -
0.4417 7700 4.5707 - - - - -
0.4474 7800 5.2229 - - - - -
0.4532 7900 5.2446 - - - - -
0.4589 8000 4.682 - - - - -
0.4646 8100 4.888 - - - - -
0.4704 8200 5.0496 - - - - -
0.4761 8300 4.7089 - - - - -
0.4818 8400 4.9567 - - - - -
0.4876 8500 4.7913 - - - - -
0.4933 8600 4.8904 - - - - -
0.4991 8700 5.247 - - - - -
0.5048 8800 4.8254 - - - - -
0.5105 8900 4.973 - - - - -
0.5163 9000 4.6657 - - - - -
0.5220 9100 4.9224 - - - - -
0.5277 9200 4.8163 - - - - -
0.5335 9300 4.3673 - - - - -
0.5392 9400 4.6509 - - - - -
0.5449 9500 5.0667 - - - - -
0.5507 9600 4.8771 - - - - -
0.5564 9700 5.1056 - - - - -
0.5622 9800 4.8297 - - - - -
0.5679 9900 5.0156 - - - - -
0.5736 10000 5.0758 - - - - -
0.5794 10100 4.9551 - - - - -
0.5851 10200 4.9594 - - - - -
0.5908 10300 5.136 - - - - -
0.5966 10400 4.7873 - - - - -
0.6023 10500 4.5154 - - - - -
0.6080 10600 4.928 - - - - -
0.6138 10700 5.1825 - - - - -
0.6195 10800 5.046 - - - - -
0.6253 10900 5.0111 - - - - -
0.6310 11000 4.9458 - - - - -
0.6367 11100 5.188 - - - - -
0.6425 11200 4.6219 - - - - -
0.6482 11300 5.3367 - - - - -
0.6539 11400 4.9851 - - - - -
0.6597 11500 5.2068 - - - - -
0.6654 11600 4.3789 - - - - -
0.6711 11700 5.3533 - - - - -
0.6769 11800 5.3983 - - - - -
0.6826 11900 4.6 - - - - -
0.6883 12000 4.6668 - - - - -
0.6941 12100 5.0814 - - - - -
0.6998 12200 5.0787 - - - - -
0.7056 12300 4.6325 - - - - -
0.7113 12400 4.9415 - - - - -
0.7170 12500 4.7053 - - - - -
0.7228 12600 4.3212 - - - - -
0.7285 12700 4.8205 - - - - -
0.7342 12800 4.8602 - - - - -
0.7400 12900 4.6944 - - - - -
0.7457 13000 4.7785 - - - - -
0.7514 13100 4.3515 - - - - -
0.7572 13200 5.7561 - - - - -
0.7629 13300 5.3526 - - - - -
0.7687 13400 5.187 - - - - -
0.7744 13500 5.0143 - - - - -
0.7801 13600 4.515 - - - - -
0.7859 13700 4.639 - - - - -
0.7916 13800 4.5556 - - - - -
0.7973 13900 4.3526 - - - - -
0.8031 14000 4.3091 - - - - -
0.8088 14100 4.1761 - - - - -
0.8145 14200 4.0484 - - - - -
0.8203 14300 4.1886 - - - - -
0.8260 14400 4.237 - - - - -
0.8318 14500 4.2167 - - - - -
0.8375 14600 4.0329 - - - - -
0.8432 14700 3.9902 - - - - -
0.8490 14800 3.8211 - - - - -
0.8547 14900 4.0048 - - - - -
0.8604 15000 3.7979 - - - - -
0.8662 15100 3.8117 - - - - -
0.8719 15200 3.909 - - - - -
0.8776 15300 3.8526 - - - - -
0.8834 15400 3.79 - - - - -
0.8891 15500 3.7792 - - - - -
0.8949 15600 3.7469 - - - - -
0.9006 15700 3.8387 - - - - -
0.9063 15800 3.6418 - - - - -
0.9121 15900 3.645 - - - - -
0.9178 16000 3.4861 - - - - -
0.9235 16100 3.6416 - - - - -
0.9293 16200 3.6665 - - - - -
0.9350 16300 3.6809 - - - - -
0.9407 16400 3.7944 - - - - -
0.9465 16500 3.6585 - - - - -
0.9522 16600 3.5398 - - - - -
0.9580 16700 3.7036 - - - - -
0.9637 16800 3.6386 - - - - -
0.9694 16900 3.5501 - - - - -
0.9752 17000 3.7957 - - - - -
0.9809 17100 3.6076 - - - - -
0.9866 17200 3.4653 - - - - -
0.9924 17300 3.6768 - - - - -
0.9981 17400 3.49 - - - - -
1.0 17433 - 0.7051 0.7044 0.7046 0.7044 0.7065

Framework Versions

  • Python: 3.11.9
  • Sentence Transformers: 3.0.1
  • Transformers: 4.40.1
  • PyTorch: 2.3.0+cu121
  • Accelerate: 0.29.3
  • Datasets: 2.19.0
  • 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",
}

MatryoshkaLoss 

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning}, 
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

MultipleNegativesRankingLoss 

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply}, 
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}