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Add new SentenceTransformer model.
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metadata
language: []
library_name: sentence-transformers
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:1115700
  - 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: Ndege mwenye mdomo mrefu katikati ya ndege.
    sentences:
      - Panya anayekimbia juu ya gurudumu.
      - Mtu anashindana katika mashindano ya mbio.
      - Ndege anayeruka.
  - source_sentence: >-
      Msichana mchanga mwenye nywele nyeusi anakabili kamera na kushikilia mfuko
      wa karatasi wakati amevaa shati la machungwa na mabawa ya kipepeo yenye
      rangi nyingi.
    sentences:
      - Mwanamke mzee anakataa kupigwa picha.
      - mtu akila na mvulana mdogo kwenye kijia cha jiji
      - Msichana mchanga anakabili kamera.
  - source_sentence: >-
      Wanawake na watoto wameketi nje katika kivuli wakati kikundi cha watoto
      wadogo wameketi ndani katika kivuli.
    sentences:
      - Mwanamke na watoto na kukaa chini.
      - Mwanamke huyo anakimbia.
      - Watu wanasafiri kwa baiskeli.
  - source_sentence: >-
      Mtoto mdogo anaruka mikononi mwa mwanamke aliyevalia suti nyeusi ya
      kuogelea akiwa kwenye dimbwi.
    sentences:
      - >-
        Mtoto akiruka mikononi mwa mwanamke aliyevalia suti ya kuogelea kwenye
        dimbwi.
      - Someone is holding oranges and walking
      - Mama na binti wakinunua viatu.
  - source_sentence: >-
      Mwanamume na mwanamke wachanga waliovaa mikoba wanaweka au kuondoa kitu
      kutoka kwenye mti mweupe wa zamani, huku watu wengine wamesimama au
      wameketi nyuma.
    sentences:
      - tai huruka
      - mwanamume na mwanamke wenye mikoba
      - Wanaume wawili wameketi karibu na mwanamke.
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: 0.7084016023985643
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.7080643276583263
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.7163851544290831
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.7066259909380899
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.716171309296757
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.7064427148038006
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.38463559218643695
            name: Pearson Dot
          - type: spearman_dot
            value: 0.3566836293112297
            name: Spearman Dot
          - type: pearson_max
            value: 0.7163851544290831
            name: Pearson Max
          - type: spearman_max
            value: 0.7080643276583263
            name: Spearman Max
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts test 512
          type: sts-test-512
        metrics:
          - type: pearson_cosine
            value: 0.7059523092716506
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.7046582726338858
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.714245009590492
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.7048777976859945
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.7150194670982656
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.7055458365374757
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.3855295554891442
            name: Pearson Dot
          - type: spearman_dot
            value: 0.3585966097040326
            name: Spearman Dot
          - type: pearson_max
            value: 0.7150194670982656
            name: Pearson Max
          - type: spearman_max
            value: 0.7055458365374757
            name: Spearman Max
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts test 256
          type: sts-test-256
        metrics:
          - type: pearson_cosine
            value: 0.7069259070512649
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.7072103115498357
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.7151518946293685
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.7050845216566457
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.7154956682724514
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.70486417475867
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.37291132473389677
            name: Pearson Dot
          - type: spearman_dot
            value: 0.3480769113927452
            name: Spearman Dot
          - type: pearson_max
            value: 0.7154956682724514
            name: Pearson Max
          - type: spearman_max
            value: 0.7072103115498357
            name: Spearman Max
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts test 128
          type: sts-test-128
        metrics:
          - type: pearson_cosine
            value: 0.7022542784280805
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.7062378358777478
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.711575484251127
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.701312903814612
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.7125043324593673
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.7011154675785318
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.34394993785114003
            name: Pearson Dot
          - type: spearman_dot
            value: 0.31686351995727197
            name: Spearman Dot
          - type: pearson_max
            value: 0.7125043324593673
            name: Pearson Max
          - type: spearman_max
            value: 0.7062378358777478
            name: Spearman Max
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts test 64
          type: sts-test-64
        metrics:
          - type: pearson_cosine
            value: 0.6950172826546709
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.6993973161633343
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.7059726901866531
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.6938542774412633
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.7066346687971139
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.6949014564343952
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.30982738809482646
            name: Pearson Dot
          - type: spearman_dot
            value: 0.2855406388879541
            name: Spearman Dot
          - type: pearson_max
            value: 0.7066346687971139
            name: Pearson Max
          - type: spearman_max
            value: 0.6993973161633343
            name: Spearman Max

SentenceTransformer based on UBC-NLP/serengeti-E250

This is a sentence-transformers model finetuned from UBC-NLP/serengeti-E250 on the Mollel/swahili-n_li-triplet-swh-eng dataset. 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
  • Training Dataset:
    • Mollel/swahili-n_li-triplet-swh-eng

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/MultiLinguSwahili-MultiLinguSwahili-serengeti-E250-nli-matryoshka-nli-matryoshka")
# Run inference
sentences = [
    'Mwanamume na mwanamke wachanga waliovaa mikoba wanaweka au kuondoa kitu kutoka kwenye mti mweupe wa zamani, huku watu wengine wamesimama au wameketi nyuma.',
    'mwanamume na mwanamke wenye mikoba',
    'tai huruka',
]
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 0.7084
spearman_cosine 0.7081
pearson_manhattan 0.7164
spearman_manhattan 0.7066
pearson_euclidean 0.7162
spearman_euclidean 0.7064
pearson_dot 0.3846
spearman_dot 0.3567
pearson_max 0.7164
spearman_max 0.7081

Semantic Similarity

Metric Value
pearson_cosine 0.706
spearman_cosine 0.7047
pearson_manhattan 0.7142
spearman_manhattan 0.7049
pearson_euclidean 0.715
spearman_euclidean 0.7055
pearson_dot 0.3855
spearman_dot 0.3586
pearson_max 0.715
spearman_max 0.7055

Semantic Similarity

Metric Value
pearson_cosine 0.7069
spearman_cosine 0.7072
pearson_manhattan 0.7152
spearman_manhattan 0.7051
pearson_euclidean 0.7155
spearman_euclidean 0.7049
pearson_dot 0.3729
spearman_dot 0.3481
pearson_max 0.7155
spearman_max 0.7072

Semantic Similarity

Metric Value
pearson_cosine 0.7023
spearman_cosine 0.7062
pearson_manhattan 0.7116
spearman_manhattan 0.7013
pearson_euclidean 0.7125
spearman_euclidean 0.7011
pearson_dot 0.3439
spearman_dot 0.3169
pearson_max 0.7125
spearman_max 0.7062

Semantic Similarity

Metric Value
pearson_cosine 0.695
spearman_cosine 0.6994
pearson_manhattan 0.706
spearman_manhattan 0.6939
pearson_euclidean 0.7066
spearman_euclidean 0.6949
pearson_dot 0.3098
spearman_dot 0.2855
pearson_max 0.7066
spearman_max 0.6994

Training Details

Training Dataset

Mollel/swahili-n_li-triplet-swh-eng

  • Dataset: Mollel/swahili-n_li-triplet-swh-eng
  • Size: 1,115,700 training samples
  • Columns: anchor, positive, and negative
  • Approximate statistics based on the first 1000 samples:
    anchor positive negative
    type string string string
    details
    • min: 6 tokens
    • mean: 11.27 tokens
    • max: 48 tokens
    • min: 5 tokens
    • mean: 13.0 tokens
    • max: 29 tokens
    • min: 4 tokens
    • mean: 12.56 tokens
    • max: 29 tokens
  • Samples:
    anchor positive negative
    A person on a horse jumps over a broken down airplane. A person is outdoors, on a horse. A person is at a diner, ordering an omelette.
    Mtu aliyepanda farasi anaruka juu ya ndege iliyovunjika. Mtu yuko nje, juu ya farasi. Mtu yuko kwenye mkahawa, akiagiza omelette.
    Children smiling and waving at camera There are children present The kids are frowning
  • Loss: MatryoshkaLoss with these parameters:
    {
        "loss": "MultipleNegativesRankingLoss",
        "matryoshka_dims": [
            768,
            512,
            256,
            128,
            64
        ],
        "matryoshka_weights": [
            1,
            1,
            1,
            1,
            1
        ],
        "n_dims_per_step": -1
    }
    

Evaluation Dataset

Mollel/swahili-n_li-triplet-swh-eng

  • Dataset: Mollel/swahili-n_li-triplet-swh-eng
  • Size: 13,168 evaluation samples
  • Columns: anchor, positive, and negative
  • Approximate statistics based on the first 1000 samples:
    anchor positive negative
    type string string string
    details
    • min: 5 tokens
    • mean: 18.07 tokens
    • max: 53 tokens
    • min: 4 tokens
    • mean: 9.45 tokens
    • max: 33 tokens
    • min: 4 tokens
    • mean: 10.27 tokens
    • max: 29 tokens
  • Samples:
    anchor positive negative
    Two women are embracing while holding to go packages. Two woman are holding packages. The men are fighting outside a deli.
    Wanawake wawili wanakumbatiana huku wakishikilia vifurushi vya kwenda. Wanawake wawili wanashikilia vifurushi. Wanaume hao wanapigana nje ya duka la vyakula vitamu.
    Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink. Two kids in numbered jerseys wash their hands. Two kids in jackets walk to school.
  • Loss: MatryoshkaLoss with these parameters:
    {
        "loss": "MultipleNegativesRankingLoss",
        "matryoshka_dims": [
            768,
            512,
            256,
            128,
            64
        ],
        "matryoshka_weights": [
            1,
            1,
            1,
            1,
            1
        ],
        "n_dims_per_step": -1
    }
    

Training Hyperparameters

Non-Default Hyperparameters

  • per_device_train_batch_size: 32
  • per_device_eval_batch_size: 32
  • 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: 32
  • per_device_eval_batch_size: 32
  • 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 26.7003 - - - - -
0.0115 200 20.7097 - - - - -
0.0172 300 17.2266 - - - - -
0.0229 400 15.7511 - - - - -
0.0287 500 14.5329 - - - - -
0.0344 600 12.6534 - - - - -
0.0402 700 10.6758 - - - - -
0.0459 800 9.421 - - - - -
0.0516 900 9.5664 - - - - -
0.0574 1000 8.5166 - - - - -
0.0631 1100 8.657 - - - - -
0.0688 1200 8.5473 - - - - -
0.0746 1300 8.3018 - - - - -
0.0803 1400 8.4488 - - - - -
0.0860 1500 7.1796 - - - - -
0.0918 1600 6.6136 - - - - -
0.0975 1700 6.2638 - - - - -
0.1033 1800 6.6955 - - - - -
0.1090 1900 7.3585 - - - - -
0.1147 2000 6.9043 - - - - -
0.1205 2100 6.677 - - - - -
0.1262 2200 6.3914 - - - - -
0.1319 2300 6.0045 - - - - -
0.1377 2400 5.8048 - - - - -
0.1434 2500 5.6898 - - - - -
0.1491 2600 5.229 - - - - -
0.1549 2700 5.2407 - - - - -
0.1606 2800 5.7074 - - - - -
0.1664 2900 6.2917 - - - - -
0.1721 3000 6.5651 - - - - -
0.1778 3100 6.7751 - - - - -
0.1836 3200 6.195 - - - - -
0.1893 3300 5.4697 - - - - -
0.1950 3400 5.1362 - - - - -
0.2008 3500 5.581 - - - - -
0.2065 3600 5.4309 - - - - -
0.2122 3700 5.6688 - - - - -
0.2180 3800 5.6923 - - - - -
0.2237 3900 5.8598 - - - - -
0.2294 4000 5.3498 - - - - -
0.2352 4100 5.3797 - - - - -
0.2409 4200 5.0389 - - - - -
0.2467 4300 5.6622 - - - - -
0.2524 4400 5.6249 - - - - -
0.2581 4500 5.6927 - - - - -
0.2639 4600 5.3612 - - - - -
0.2696 4700 5.2751 - - - - -
0.2753 4800 5.4224 - - - - -
0.2811 4900 5.0338 - - - - -
0.2868 5000 4.9813 - - - - -
0.2925 5100 4.8533 - - - - -
0.2983 5200 5.4137 - - - - -
0.3040 5300 5.4063 - - - - -
0.3098 5400 5.3107 - - - - -
0.3155 5500 5.0907 - - - - -
0.3212 5600 4.8644 - - - - -
0.3270 5700 4.7926 - - - - -
0.3327 5800 5.0268 - - - - -
0.3384 5900 5.3029 - - - - -
0.3442 6000 5.1246 - - - - -
0.3499 6100 5.1152 - - - - -
0.3556 6200 5.4265 - - - - -
0.3614 6300 4.7079 - - - - -
0.3671 6400 4.6368 - - - - -
0.3729 6500 4.662 - - - - -
0.3786 6600 5.3695 - - - - -
0.3843 6700 4.6974 - - - - -
0.3901 6800 4.6584 - - - - -
0.3958 6900 4.7413 - - - - -
0.4015 7000 4.6604 - - - - -
0.4073 7100 5.2476 - - - - -
0.4130 7200 4.9966 - - - - -
0.4187 7300 4.656 - - - - -
0.4245 7400 4.5711 - - - - -
0.4302 7500 5.0256 - - - - -
0.4360 7600 4.3856 - - - - -
0.4417 7700 4.2548 - - - - -
0.4474 7800 4.8584 - - - - -
0.4532 7900 4.8563 - - - - -
0.4589 8000 4.5101 - - - - -
0.4646 8100 4.4688 - - - - -
0.4704 8200 4.7076 - - - - -
0.4761 8300 4.3268 - - - - -
0.4818 8400 4.6622 - - - - -
0.4876 8500 4.4808 - - - - -
0.4933 8600 4.676 - - - - -
0.4991 8700 5.0348 - - - - -
0.5048 8800 4.5497 - - - - -
0.5105 8900 4.7428 - - - - -
0.5163 9000 4.4418 - - - - -
0.5220 9100 4.4946 - - - - -
0.5277 9200 4.5249 - - - - -
0.5335 9300 4.2413 - - - - -
0.5392 9400 4.4799 - - - - -
0.5449 9500 4.6807 - - - - -
0.5507 9600 4.5901 - - - - -
0.5564 9700 4.7266 - - - - -
0.5622 9800 4.692 - - - - -
0.5679 9900 4.8651 - - - - -
0.5736 10000 4.7746 - - - - -
0.5794 10100 4.68 - - - - -
0.5851 10200 4.7697 - - - - -
0.5908 10300 4.8848 - - - - -
0.5966 10400 4.4004 - - - - -
0.6023 10500 4.2979 - - - - -
0.6080 10600 4.7266 - - - - -
0.6138 10700 4.8605 - - - - -
0.6195 10800 4.7436 - - - - -
0.6253 10900 4.6239 - - - - -
0.6310 11000 4.394 - - - - -
0.6367 11100 4.8081 - - - - -
0.6425 11200 4.2329 - - - - -
0.6482 11300 4.873 - - - - -
0.6539 11400 4.5557 - - - - -
0.6597 11500 4.7918 - - - - -
0.6654 11600 4.1607 - - - - -
0.6711 11700 4.8744 - - - - -
0.6769 11800 5.0072 - - - - -
0.6826 11900 4.3532 - - - - -
0.6883 12000 4.3319 - - - - -
0.6941 12100 4.6885 - - - - -
0.6998 12200 4.6682 - - - - -
0.7056 12300 4.4258 - - - - -
0.7113 12400 4.6136 - - - - -
0.7170 12500 4.3594 - - - - -
0.7228 12600 4.0627 - - - - -
0.7285 12700 4.5244 - - - - -
0.7342 12800 4.504 - - - - -
0.7400 12900 4.4694 - - - - -
0.7457 13000 4.4804 - - - - -
0.7514 13100 4.0588 - - - - -
0.7572 13200 4.8016 - - - - -
0.7629 13300 4.2971 - - - - -
0.7687 13400 4.1326 - - - - -
0.7744 13500 3.9763 - - - - -
0.7801 13600 3.7716 - - - - -
0.7859 13700 3.8448 - - - - -
0.7916 13800 3.6779 - - - - -
0.7973 13900 3.5938 - - - - -
0.8031 14000 3.3981 - - - - -
0.8088 14100 3.4151 - - - - -
0.8145 14200 3.2498 - - - - -
0.8203 14300 3.4909 - - - - -
0.8260 14400 3.4098 - - - - -
0.8318 14500 3.4448 - - - - -
0.8375 14600 3.2868 - - - - -
0.8432 14700 3.2196 - - - - -
0.8490 14800 3.0852 - - - - -
0.8547 14900 3.2341 - - - - -
0.8604 15000 3.164 - - - - -
0.8662 15100 3.0919 - - - - -
0.8719 15200 3.176 - - - - -
0.8776 15300 3.1361 - - - - -
0.8834 15400 3.0683 - - - - -
0.8891 15500 3.0275 - - - - -
0.8949 15600 3.0763 - - - - -
0.9006 15700 3.1828 - - - - -
0.9063 15800 3.0053 - - - - -
0.9121 15900 2.9696 - - - - -
0.9178 16000 2.8919 - - - - -
0.9235 16100 2.9922 - - - - -
0.9293 16200 2.9063 - - - - -
0.9350 16300 3.0633 - - - - -
0.9407 16400 3.1782 - - - - -
0.9465 16500 2.9206 - - - - -
0.9522 16600 2.8785 - - - - -
0.9580 16700 2.9934 - - - - -
0.9637 16800 3.0125 - - - - -
0.9694 16900 2.9338 - - - - -
0.9752 17000 2.9931 - - - - -
0.9809 17100 2.956 - - - - -
0.9866 17200 2.8415 - - - - -
0.9924 17300 3.0072 - - - - -
0.9981 17400 2.9046 - - - - -
1.0 17433 - 0.7062 0.7072 0.7047 0.6994 0.7081

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}
}