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metadata
language:
  - en
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:19139199
  - loss:MatryoshkaLoss
  - loss:MultipleNegativesRankingLoss
base_model: answerdotai/ModernBERT-base
widget:
  - source_sentence: '[unused0]what period do we live in'
    sentences:
      - "[unused1]The answer depends on what you want in a cruise experience. If youâ\x80\x99re interested in lively entertainment, drinking and parties, and crazy excitement for more festive cruise experience, and the best time to go on a Caribbean cruise is between mid February through much of March."
      - >-
        [unused1]Earth is currently in the Quaternary Period of the Cenozoic
        Era.
      - >-
        [unused1]If we did not live in a structured society with other people,
        laws would not be necessary. We would simply do as we please, with
        little regard for others. But ever since individuals began to associate
        with other people -- to live in society --laws have been the glue that
        has kept society together.lmost everything we do is governed by some set
        of rules. There are rules for games, for social clubs, for sports and
        for adults in the workplace. There are also rules imposed by morality
        and custom that play an important role in telling us what we should and
        should not do.
  - source_sentence: '[unused0]how different religion define christology'
    sentences:
      - "[unused1]Find origin, gender, rashi, nakshatra, zodiac signs, moon signs, religion, caste, compatibility and astrology behind the name Janhavi, Find what name Janhavi means in different region, in different religion, in different rashi, for different planets, and in different nakshatraâ\x80\x99s."
      - >-
        [unused1]Christianity is an Abrahamic religion that began as a Second
        Temple Judaic sect in the mid-1st century. Originating in Judea, it
        quickly spread to Europe, Syria, Mesopotamia, Asia Minor, Transcaucasia,
        Egypt, Ethiopia, and India, and by the end of the 4th century had become
        the official state church of the Roman Empire.
      - "[unused1]The answer depends on what you want in a cruise experience. If youâ\x80\x99re interested in lively entertainment, drinking and parties, and crazy excitement for more festive cruise experience, and the best time to go on a Caribbean cruise is between mid February through much of March."
  - source_sentence: '[unused0]what was juliet morgan family'
    sentences:
      - >-
        [unused1]Famous Financier. Financier, art collector and philanthropist
        John Pierpont Morgan, best known as J.P. Morgan, was born on April 17,
        1837, in Hartford, Connecticut. The son of a banker, Morgan went into
        the family business and became one of the most famous financiers in
        history.
      - >-
        [unused1]Juliet Pierpont Morgan was born on month day 1847, at birth
        place, Connecticut, to Junius Spencer Morgan and Juliet Morgan (born
        Pierpont). Junius was born on April 14 1813, in Holyoke, Hampden County,
        Massachusetts, USA.
      - >-
        [unused1]Juliette Hampton Morgan was a librarian and civil rights
        activist in Montgomery, Alabama. The only daughter from a well-to-do
        white family, Morgan was an early member of the community that pushed
        for integration. As a librarian she often spoke out against the acts of
        injustice she witnessed against African-Americans by writing letters to
        the Montgomery Advertiser, the local newspaper. She was castigated by
        the community for her racial views and was targeted by segregationists
        who broke her windo
  - source_sentence: '[unused0]how different religion define christology'
    sentences:
      - >-
        [unused1]Carbon Monoxide is produced from gas appliances in your home.
        Carbon Dioxide is produced from the exhaust fumes from your automobile.
        Either of these gases can be harmful to a human. These gases can be even
        more harmful to small animals in your home. Depending on the size of
        your home you should have a carbon monoxide detector on every level of
        your home.
      - >-
        [unused1]Doctrine is a set of accepted beliefs held by a group. In
        religion, it is the set of true beliefs that define the parameters of
        that belief system.
      - >-
        [unused1]Christianity is an Abrahamic religion that began as a Second
        Temple Judaic sect in the mid-1st century. Originating in Judea, it
        quickly spread to Europe, Syria, Mesopotamia, Asia Minor, Transcaucasia,
        Egypt, Ethiopia, and India, and by the end of the 4th century had become
        the official state church of the Roman Empire.
  - source_sentence: '[unused0]what period do we live in'
    sentences:
      - "[unused1]Thereâ\x80\x99s a big difference in drive time depending on what part of Ewa Beach you live in â\x80\x94 the homes in Ocean Point (most southern part of Ewa) have a 20 minute longer drive than we do, even though we both live in the town of Ewa Beach."
      - >-
        [unused1]Save. Bankruptcy does not automatically disqualify someone for
        receiving security clearances, but it will slow the process down--as
        will any derogatory financial information. If you are applying for
        top-secret clearance, background investigators will review the reasons
        for your bankruptcy.
      - >-
        [unused1]Earth is currently in the Quaternary Period of the Cenozoic
        Era.
datasets:
  - sentence-transformers/msmarco-bm25
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - cosine_accuracy
model-index:
  - name: SentenceTransformer based on estrogen/ModernBERT-base-sbert-initialized
    results:
      - task:
          type: triplet
          name: Triplet
        dataset:
          name: ms marco
          type: ms_marco
        metrics:
          - type: cosine_accuracy
            value: 0.9537
            name: Cosine Accuracy
          - type: cosine_accuracy
            value: 0.9587
            name: Cosine Accuracy

SentenceTransformer based on estrogen/ModernBERT-base-sbert-initialized

This is a sentence-transformers model finetuned from estrogen/ModernBERT-base-sbert-initialized on the msmarco-bm25 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 Sources

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel 
  (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("estrogen/ModernBERT-base-marco")
# Run inference
sentences = [
    '[unused0]what period do we live in',
    '[unused1]Earth is currently in the Quaternary Period of the Cenozoic Era.',
    '[unused1]Thereâ\x80\x99s a big difference in drive time depending on what part of Ewa Beach you live in â\x80\x94 the homes in Ocean Point (most southern part of Ewa) have a 20 minute longer drive than we do, even though we both live in the town of Ewa Beach.',
]
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

Triplet

Metric Value
cosine_accuracy 0.9537

Triplet

Metric Value
cosine_accuracy 0.9587

Training Details

Training Dataset

msmarco-bm25

  • Dataset: msmarco-bm25 at ce8a493
  • Size: 19,139,199 training samples
  • Columns: query, positive, and negative
  • Approximate statistics based on the first 1000 samples:
    query positive negative
    type string string string
    details
    • min: 6 tokens
    • mean: 10.51 tokens
    • max: 17 tokens
    • min: 45 tokens
    • mean: 86.37 tokens
    • max: 212 tokens
    • min: 25 tokens
    • mean: 80.75 tokens
    • max: 222 tokens
  • Samples:
    query positive negative
    [unused0]what are the liberal arts? [unused1]liberal arts. 1. the academic course of instruction at a college intended to provide general knowledge and comprising the arts, humanities, natural sciences, and social sciences, as opposed to professional or technical subjects. [unused1]The New York State Education Department requires 60 Liberal Arts credits in a Bachelor of Science program and 90 Liberal Arts credits in a Bachelor of Arts program. In the list of course descriptions, courses which are liberal arts for all students are identified by (Liberal Arts) after the course number.
    [unused0]what are the liberal arts? [unused1]liberal arts. 1. the academic course of instruction at a college intended to provide general knowledge and comprising the arts, humanities, natural sciences, and social sciences, as opposed to professional or technical subjects. [unused1]You can choose from an array of liberal arts majors. Most of these are offered in the liberal arts departments of colleges that belong to universities and at smaller colleges that are designated as liberal arts institutions.
    [unused0]what are the liberal arts? [unused1]liberal arts. 1. the academic course of instruction at a college intended to provide general knowledge and comprising the arts, humanities, natural sciences, and social sciences, as opposed to professional or technical subjects. [unused1]Majors. You can choose from an array of liberal arts majors. Most of these are offered in the liberal arts departments of colleges that belong to universities and at smaller colleges that are designated as liberal arts institutions.
  • Loss: MatryoshkaLoss with these parameters:
    {
        "loss": "MultipleNegativesRankingLoss",
        "matryoshka_dims": [
            768,
            512,
            256,
            128,
            64
        ],
        "matryoshka_weights": [
            1,
            0.9,
            0.81,
            0.7290000000000001,
            0.6561
        ],
        "n_dims_per_step": -1
    }
    

Evaluation Dataset

msmarco-bm25

  • Dataset: msmarco-bm25 at ce8a493
  • Size: 19,139,199 evaluation samples
  • Columns: query, positive, and negative
  • Approximate statistics based on the first 1000 samples:
    query positive negative
    type string string string
    details
    • min: 5 tokens
    • mean: 10.42 tokens
    • max: 23 tokens
    • min: 20 tokens
    • mean: 80.07 tokens
    • max: 167 tokens
    • min: 18 tokens
    • mean: 82.48 tokens
    • max: 213 tokens
  • Samples:
    query positive negative
    [unused0]different uses of corn [unused1]Corn or maize oil is extracted from the germ of corn, and its main use is for cooking. It is also a key ingredient in margarine and other processed foods. Corn oil is also a feedstock used for biodiesel.From 2012 to 2014, the use of nonfood-grade (NFG) corn oil for biodiesel production has grown tremendously.ses of Corn Oil. Apart from serving as a less-than-ideal cooking oil, corn oil has several industrial uses, including as an addition to soap, salve, paint, ink, textiles, and insecticides. It also sometimes functions as a carrier for drug molecules in pharmaceutical products. [unused1]Impact of Ethanol on Corn Prices. The U.S. produces 40 percent of the world’s corn, [5] and ethanol production uses about 40 percent of U.S. corn production, [6] but roughly one-third of the value of the corn used in ethanol production returns to the feed market as DDGS.
    [unused0]different uses of corn [unused1]Corn or maize oil is extracted from the germ of corn, and its main use is for cooking. It is also a key ingredient in margarine and other processed foods. Corn oil is also a feedstock used for biodiesel.From 2012 to 2014, the use of nonfood-grade (NFG) corn oil for biodiesel production has grown tremendously.ses of Corn Oil. Apart from serving as a less-than-ideal cooking oil, corn oil has several industrial uses, including as an addition to soap, salve, paint, ink, textiles, and insecticides. It also sometimes functions as a carrier for drug molecules in pharmaceutical products. [unused1]But ask different reptile keepers how long corn do corn snakes get and you won't get one standard answer. Like us humans, who may grow to little more than 5 feet tall to well over 6 feet in adults, different corn snakes attain different sizes.
    [unused0]different uses of corn [unused1]Corn or maize oil is extracted from the germ of corn, and its main use is for cooking. It is also a key ingredient in margarine and other processed foods. Corn oil is also a feedstock used for biodiesel.From 2012 to 2014, the use of nonfood-grade (NFG) corn oil for biodiesel production has grown tremendously.ses of Corn Oil. Apart from serving as a less-than-ideal cooking oil, corn oil has several industrial uses, including as an addition to soap, salve, paint, ink, textiles, and insecticides. It also sometimes functions as a carrier for drug molecules in pharmaceutical products. [unused1]The corn system uses a large amount of natural resources. Even though it does not deliver as much food as comparable systems around the globe, the American corn system continues to use a large proportion of our country’s natural resources.he corn system uses a large amount of natural resources. Even though it does not deliver as much food as comparable systems around the globe, the American corn system continues to use a large proportion of our country’s natural resources.
  • Loss: MatryoshkaLoss with these parameters:
    {
        "loss": "MultipleNegativesRankingLoss",
        "matryoshka_dims": [
            768,
            512,
            256,
            128,
            64
        ],
        "matryoshka_weights": [
            1,
            0.9,
            0.81,
            0.7290000000000001,
            0.6561
        ],
        "n_dims_per_step": -1
    }
    

Training Hyperparameters

Non-Default Hyperparameters

  • eval_strategy: steps
  • per_device_train_batch_size: 256
  • per_device_eval_batch_size: 256
  • num_train_epochs: 1
  • warmup_ratio: 0.1
  • bf16: True
  • prompts: {'query': '[unused0]', 'positive': '[unused1]', 'negative': '[unused1]'}
  • batch_sampler: no_duplicates

All Hyperparameters

Click to expand
  • overwrite_output_dir: False
  • do_predict: False
  • eval_strategy: steps
  • prediction_loss_only: True
  • per_device_train_batch_size: 256
  • per_device_eval_batch_size: 256
  • 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: 5e-05
  • weight_decay: 0.0
  • adam_beta1: 0.9
  • adam_beta2: 0.999
  • adam_epsilon: 1e-08
  • max_grad_norm: 1.0
  • num_train_epochs: 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
  • restore_callback_states_from_checkpoint: False
  • no_cuda: False
  • use_cpu: False
  • use_mps_device: False
  • seed: 42
  • data_seed: None
  • jit_mode_eval: False
  • use_ipex: False
  • bf16: True
  • fp16: False
  • fp16_opt_level: O1
  • half_precision_backend: auto
  • bf16_full_eval: False
  • fp16_full_eval: False
  • tf32: None
  • local_rank: 0
  • ddp_backend: None
  • tpu_num_cores: None
  • tpu_metrics_debug: False
  • debug: []
  • dataloader_drop_last: False
  • dataloader_num_workers: 0
  • dataloader_prefetch_factor: None
  • past_index: -1
  • disable_tqdm: False
  • remove_unused_columns: True
  • label_names: None
  • load_best_model_at_end: False
  • ignore_data_skip: False
  • fsdp: []
  • fsdp_min_num_params: 0
  • fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
  • fsdp_transformer_layer_cls_to_wrap: None
  • accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
  • deepspeed: None
  • label_smoothing_factor: 0.0
  • optim: adamw_torch
  • optim_args: None
  • adafactor: False
  • group_by_length: False
  • length_column_name: length
  • ddp_find_unused_parameters: None
  • ddp_bucket_cap_mb: None
  • ddp_broadcast_buffers: False
  • dataloader_pin_memory: True
  • dataloader_persistent_workers: False
  • skip_memory_metrics: True
  • use_legacy_prediction_loop: False
  • push_to_hub: False
  • resume_from_checkpoint: None
  • hub_model_id: None
  • hub_strategy: every_save
  • hub_private_repo: None
  • 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: {'query': '[unused0]', 'positive': '[unused1]', 'negative': '[unused1]'}
  • batch_sampler: no_duplicates
  • multi_dataset_batch_sampler: proportional

Training Logs

Click to expand
Epoch Step Training Loss Validation Loss ms_marco_cosine_accuracy
0 0 - - 0.5833
0.0005 1 24.3765 - -
0.0010 2 24.4903 - -
0.0015 3 24.4595 - -
0.0020 4 24.2913 - -
0.0026 5 24.355 - -
0.0031 6 24.2594 - -
0.0036 7 24.1731 - -
0.0041 8 24.135 - -
0.0046 9 23.9455 - -
0.0051 10 23.9964 - -
0.0056 11 23.7097 - -
0.0061 12 23.6735 - -
0.0067 13 23.4437 - -
0.0072 14 23.3786 - -
0.0077 15 23.2145 - -
0.0082 16 23.056 - -
0.0087 17 22.9313 - -
0.0092 18 22.6844 - -
0.0097 19 22.5722 - -
0.0102 20 22.4012 - -
0.0107 21 22.1844 - -
0.0113 22 21.9721 - -
0.0118 23 21.7602 - -
0.0123 24 21.4919 - -
0.0128 25 21.2078 - -
0.0133 26 20.9653 - -
0.0138 27 20.6508 - -
0.0143 28 20.2809 - -
0.0148 29 19.8262 - -
0.0154 30 19.3564 - -
0.0159 31 18.9138 - -
0.0164 32 18.2485 - -
0.0169 33 17.63 - -
0.0174 34 17.028 - -
0.0179 35 16.2372 - -
0.0184 36 15.3384 - -
0.0189 37 14.3885 - -
0.0194 38 13.4842 - -
0.0200 39 12.6733 - -
0.0205 40 11.8822 - -
0.0210 41 11.2018 - -
0.0215 42 10.2756 - -
0.0220 43 9.2841 - -
0.0225 44 8.8121 - -
0.0230 45 7.973 - -
0.0235 46 7.3955 - -
0.0241 47 6.9961 - -
0.0246 48 6.9545 - -
0.0251 49 6.4636 - -
0.0256 50 5.9562 - -
0.0261 51 5.6653 - -
0.0266 52 5.0228 - -
0.0271 53 4.6302 - -
0.0276 54 4.0535 - -
0.0281 55 3.714 - -
0.0287 56 3.2454 - -
0.0292 57 2.9078 - -
0.0297 58 2.6236 - -
0.0302 59 2.4775 - -
0.0307 60 2.1067 - -
0.0312 61 2.1748 - -
0.0317 62 1.9844 - -
0.0322 63 1.7707 - -
0.0328 64 1.6281 - -
0.0333 65 1.9416 - -
0.0338 66 1.5796 - -
0.0343 67 1.5611 - -
0.0348 68 1.5282 - -
0.0353 69 1.6081 - -
0.0358 70 1.4455 - -
0.0363 71 1.3649 - -
0.0368 72 1.0884 - -
0.0374 73 0.9736 - -
0.0379 74 0.8036 - -
0.0384 75 0.8466 - -
0.0389 76 0.8594 - -
0.0394 77 0.8887 - -
0.0399 78 0.8299 - -
0.0404 79 0.8561 - -
0.0409 80 1.0354 - -
0.0415 81 1.1445 - -
0.0420 82 1.1131 - -
0.0425 83 1.114 - -
0.0430 84 1.032 - -
0.0435 85 0.8509 - -
0.0440 86 0.7415 - -
0.0445 87 1.1415 - -
0.0450 88 1.1622 - -
0.0455 89 1.2912 - -
0.0461 90 1.0092 - -
0.0466 91 0.7901 - -
0.0471 92 0.7782 - -
0.0476 93 0.6596 - -
0.0481 94 0.5789 - -
0.0486 95 0.8727 - -
0.0491 96 0.6869 - -
0.0496 97 0.7282 - -
0.0502 98 0.6578 - -
0.0507 99 0.6303 - -
0.0512 100 0.4592 2.8360 0.8286
0.0517 101 0.6197 - -
0.0522 102 0.737 - -
0.0527 103 0.538 - -
0.0532 104 0.435 - -
0.0537 105 0.3955 - -
0.0542 106 0.2621 - -
0.0548 107 0.2742 - -
0.0553 108 0.2802 - -
0.0558 109 0.2681 - -
0.0563 110 0.2489 - -
0.0568 111 0.2985 - -
0.0573 112 0.5011 - -
0.0578 113 0.4239 - -
0.0583 114 0.5619 - -
0.0589 115 0.3498 - -
0.0594 116 0.5731 - -
0.0599 117 0.4297 - -
0.0604 118 0.4703 - -
0.0609 119 0.449 - -
0.0614 120 0.4708 - -
0.0619 121 0.4467 - -
0.0624 122 0.4263 - -
0.0629 123 0.3481 - -
0.0635 124 0.2899 - -
0.0640 125 0.4778 - -
0.0645 126 0.7366 - -
0.0650 127 0.5905 - -
0.0655 128 0.5095 - -
0.0660 129 0.3977 - -
0.0665 130 0.4885 - -
0.0670 131 0.4421 - -
0.0676 132 0.2875 - -
0.0681 133 0.4082 - -
0.0686 134 0.424 - -
0.0691 135 0.5184 - -
0.0696 136 0.4225 - -
0.0701 137 0.4612 - -
0.0706 138 0.3632 - -
0.0711 139 0.3489 - -
0.0716 140 0.3888 - -
0.0722 141 0.367 - -
0.0727 142 0.3191 - -
0.0732 143 0.4214 - -
0.0737 144 0.3566 - -
0.0742 145 0.2213 - -
0.0747 146 0.2371 - -
0.0752 147 0.2611 - -
0.0757 148 0.1732 - -
0.0763 149 0.223 - -
0.0768 150 0.1442 - -
0.0773 151 0.2201 - -
0.0778 152 0.379 - -
0.0783 153 0.3073 - -
0.0788 154 0.2447 - -
0.0793 155 0.3104 - -
0.0798 156 0.3395 - -
0.0803 157 0.2449 - -
0.0809 158 0.226 - -
0.0814 159 0.2058 - -
0.0819 160 0.2084 - -
0.0824 161 0.1993 - -
0.0829 162 0.1208 - -
0.0834 163 0.1055 - -
0.0839 164 0.2083 - -
0.0844 165 0.2167 - -
0.0850 166 0.2169 - -
0.0855 167 0.194 - -
0.0860 168 0.2136 - -
0.0865 169 0.2185 - -
0.0870 170 0.2675 - -
0.0875 171 0.2817 - -
0.0880 172 0.3723 - -
0.0885 173 0.5269 - -
0.0890 174 0.2643 - -
0.0896 175 0.2437 - -
0.0901 176 0.1549 - -
0.0906 177 0.1461 - -
0.0911 178 0.1395 - -
0.0916 179 0.1506 - -
0.0921 180 0.2542 - -
0.0926 181 0.2022 - -
0.0931 182 0.1756 - -
0.0937 183 0.2411 - -
0.0942 184 0.3402 - -
0.0947 185 0.3534 - -
0.0952 186 0.2632 - -
0.0957 187 0.3643 - -
0.0962 188 0.4046 - -
0.0967 189 0.2619 - -
0.0972 190 0.1575 - -
0.0977 191 0.12 - -
0.0983 192 0.1157 - -
0.0988 193 0.2453 - -
0.0993 194 0.2406 - -
0.0998 195 0.1784 - -
0.1003 196 0.2226 - -
0.1008 197 0.1963 - -
0.1013 198 0.0964 - -
0.1018 199 0.1413 - -
0.1024 200 0.1467 1.6950 0.915
0.1029 201 0.322 - -
0.1034 202 0.3444 - -
0.1039 203 0.2968 - -
0.1044 204 0.227 - -
0.1049 205 0.3337 - -
0.1054 206 0.2653 - -
0.1059 207 0.251 - -
0.1064 208 0.1667 - -
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0.9959 1946 0.4277 - -
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0.9974 1949 0.3924 - -
0.9980 1950 0.3328 - -
0.9985 1951 0.3756 - -
0.9990 1952 0.3523 - -
0.9995 1953 0.6408 - -
1.0 1954 0.392 - 0.9587

Framework Versions

  • Python: 3.10.12
  • Sentence Transformers: 3.3.1
  • Transformers: 4.48.0.dev0
  • PyTorch: 2.1.0+cu118
  • Accelerate: 1.2.1
  • Datasets: 3.2.0
  • Tokenizers: 0.21.0

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}
}