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esci-nomic-embed-text-v1_5_1

Sentence Similarity
sentence-transformers
Safetensors
nomic_bert
feature-extraction
dataset_size:100K<n<1M
loss:CachedMultipleNegativesRankingLoss
custom_code
Eval Results
text-embeddings-inference
Inference Endpoints
Model card Files Community
esci-nomic-embed-text-v1_5_1 / README.md
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metadata
language: []
library_name: sentence-transformers
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - dataset_size:100K<n<1M
  - loss:CachedMultipleNegativesRankingLoss
base_model: nomic-ai/nomic-embed-text-v1.5
metrics:
  - cosine_accuracy
  - dot_accuracy
  - manhattan_accuracy
  - euclidean_accuracy
  - max_accuracy
widget:
  - source_sentence: 'search_query: adorime'
    sentences:
      - 'search_query: green air scents llc'
      - 'search_query: dpms sbr accessories'
      - 'search_query: sweaters cowl neck men'
  - source_sentence: 'search_query: serving'
    sentences:
      - 'search_query: ceramic cups without handles'
      - 'search_query: 100 mm cigarette case'
      - 'search_query: toddler girl leopard midi'
  - source_sentence: 'search_query: haierc'
    sentences:
      - 'search_query: homder'
      - 'search_query: 3d milling metal cnc'
      - 'search_query: sandals for women'
  - source_sentence: 'search_query: poppies'
    sentences:
      - 'search_query: fake plants without pot'
      - 'search_query: tonsil stone remover'
      - 'search_query: vestido corto sexy de mujer'
  - source_sentence: 'search_query: dab rig'
    sentences:
      - 'search_query: volcano weed vaporizer'
      - 'search_query: 22 gold chain for men'
      - 'search_query: apple watch screen protector'
pipeline_tag: sentence-similarity
model-index:
  - name: SentenceTransformer based on nomic-ai/nomic-embed-text-v1.5
    results:
      - task:
          type: triplet
          name: Triplet
        dataset:
          name: triplet esci
          type: triplet-esci
        metrics:
          - type: cosine_accuracy
            value: 0.7405
            name: Cosine Accuracy
          - type: dot_accuracy
            value: 0.269
            name: Dot Accuracy
          - type: manhattan_accuracy
            value: 0.7432
            name: Manhattan Accuracy
          - type: euclidean_accuracy
            value: 0.7457
            name: Euclidean Accuracy
          - type: max_accuracy
            value: 0.7457
            name: Max Accuracy

SentenceTransformer based on nomic-ai/nomic-embed-text-v1.5

This is a sentence-transformers model finetuned from nomic-ai/nomic-embed-text-v1.5. 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: nomic-ai/nomic-embed-text-v1.5
  • Maximum Sequence Length: 8192 tokens
  • Output Dimensionality: 768 tokens
  • Similarity Function: Cosine Similarity

Model Sources

Full Model Architecture 

SentenceTransformer(
  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: NomicBertModel 
  (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("sentence_transformers_model_id")
# Run inference
sentences = [
    'search_query: dab rig',
    'search_query: volcano weed vaporizer',
    'search_query: 22 gold chain for men',
]
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 0.7405
dot_accuracy 0.269
manhattan_accuracy 0.7432
euclidean_accuracy 0.7457
max_accuracy 0.7457

Training Details

Training Dataset

Unnamed Dataset

  • Size: 167,039 training samples
  • Columns: anchor, positive, and negative
  • Approximate statistics based on the first 1000 samples:
    anchor positive negative
    type string string string
    details
    • min: 7 tokens
    • mean: 11.1 tokens
    • max: 38 tokens
    • min: 14 tokens
    • mean: 43.23 tokens
    • max: 124 tokens
    • min: 16 tokens
    • mean: 43.16 tokens
    • max: 97 tokens
  • Samples:
    anchor positive negative
    search_query: foos ball coffee table search_document: KICK Vanquish 55" in Foosball Table, KICK, Blue/Gray search_document: KICK Legend 55" Foosball Table (Black), KICK, Black
    search_query: bathroom rugs white washable search_document: Luxury Bath Mat Floor Towel Set - Absorbent Cotton Hotel Spa Shower/Bathtub Mats [Not a Bathroom Rug] 22"x34" White
    search_query: kids gloves search_document: EvridWear Boys Girls Magic Stretch Gripper Gloves 3 Pair Pack Assortment, Kids One Size Winter Warm Gloves Children (8-14Years, 3 Pairs Camo), Evridwear, 3 Pairs Camo search_document: Body Glove Little Boys 2-Piece UPF 50+ Rash Guard Swimsuit Set (2 Piece), All Black, Size 5, Body Glove, All Black
  • Loss: CachedMultipleNegativesRankingLoss with these parameters:
    {
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Evaluation Dataset

Unnamed Dataset

  • Size: 10,000 evaluation samples
  • Columns: anchor, positive, and negative
  • Approximate statistics based on the first 1000 samples:
    anchor positive negative
    type string string string
    details
    • min: 7 tokens
    • mean: 11.44 tokens
    • max: 31 tokens
    • min: 16 tokens
    • mean: 42.26 tokens
    • max: 92 tokens
    • min: 16 tokens
    • mean: 42.28 tokens
    • max: 105 tokens
  • Samples:
    anchor positive negative
    search_query: defender series iphone 8 search_document: Hand-e Muscle Series Belt Clip Case for Apple iPhone 7 / iPhone 8 / iPhone SE “2020” (4.7”) 2-in-1 Protective Defender w Screen Protector & Holster & Kickstand/Shock & Drop Proof – Camouflage/Orange, Hand-e, Camouflage / Orange search_document: OtterBox Defender Series Rugged Case for iPhone 8 PLUS & iPhone 7 PLUS - Case Only - Non-Retail Packaging - Dark Lake - With Microbial Defense, OtterBox, Dark Lake
    search_query: joy mangano search_document: Joy by Joy Mangano 11-Piece Complete Luxury Towel Set, Ivory, Joy Mangano, Ivory search_document: BAGSMART Jewelry Organizer Case Travel Jewelry Storage Bag for Necklace, Earrings, Rings, Bracelet, Soft Pink, BAGSMART, Soft Pink
    search_query: cashel fly masks for horses without ears search_document: Cashel Crusader Designer Horse Fly Mask, Leopard, Weanling, Cashel, Leopard search_document: Cashel Crusader Designer Horse Fly Mask with Ears, Teal Tribal, Weanling, Cashel, Teal Tribal
  • Loss: CachedMultipleNegativesRankingLoss with these parameters:
    {
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Training Hyperparameters

Non-Default Hyperparameters

  • per_device_train_batch_size: 4
  • per_device_eval_batch_size: 4
  • gradient_accumulation_steps: 4
  • learning_rate: 1e-06
  • num_train_epochs: 5
  • lr_scheduler_type: cosine_with_restarts
  • warmup_ratio: 0.1
  • dataloader_drop_last: True
  • dataloader_num_workers: 4
  • dataloader_prefetch_factor: 2
  • load_best_model_at_end: 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: 4
  • per_device_eval_batch_size: 4
  • per_gpu_train_batch_size: None
  • per_gpu_eval_batch_size: None
  • gradient_accumulation_steps: 4
  • eval_accumulation_steps: None
  • learning_rate: 1e-06
  • weight_decay: 0.0
  • adam_beta1: 0.9
  • adam_beta2: 0.999
  • adam_epsilon: 1e-08
  • max_grad_norm: 1.0
  • num_train_epochs: 5
  • max_steps: -1
  • lr_scheduler_type: cosine_with_restarts
  • lr_scheduler_kwargs: {}
  • warmup_ratio: 0.1
  • warmup_steps: 0
  • log_level: passive
  • log_level_replica: warning
  • log_on_each_node: True
  • logging_nan_inf_filter: True
  • save_safetensors: True
  • save_on_each_node: False
  • save_only_model: False
  • no_cuda: False
  • use_cpu: False
  • use_mps_device: False
  • seed: 42
  • data_seed: None
  • jit_mode_eval: False
  • use_ipex: False
  • bf16: False
  • fp16: 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: True
  • dataloader_num_workers: 4
  • dataloader_prefetch_factor: 2
  • past_index: -1
  • disable_tqdm: False
  • remove_unused_columns: True
  • label_names: None
  • load_best_model_at_end: True
  • 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}
  • 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
  • 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
  • batch_sampler: no_duplicates
  • multi_dataset_batch_sampler: proportional

Training Logs

Click to expand
Epoch Step Training Loss loss triplet-esci_cosine_accuracy
0.0096 100 0.6669 - -
0.0192 200 0.6633 - -
0.0287 300 0.6575 - -
0.0383 400 0.6638 - -
0.0479 500 0.6191 - -
0.0575 600 0.6464 - -
0.0671 700 0.6291 - -
0.0766 800 0.5973 - -
0.0862 900 0.605 - -
0.0958 1000 0.6278 0.6525 0.7269
0.1054 1100 0.6041 - -
0.1149 1200 0.6077 - -
0.1245 1300 0.589 - -
0.1341 1400 0.5811 - -
0.1437 1500 0.5512 - -
0.1533 1600 0.5907 - -
0.1628 1700 0.5718 - -
0.1724 1800 0.5446 - -
0.1820 1900 0.546 - -
0.1916 2000 0.5141 0.6105 0.7386
0.2012 2100 0.5359 - -
0.2107 2200 0.5093 - -
0.2203 2300 0.5384 - -
0.2299 2400 0.5582 - -
0.2395 2500 0.5038 - -
0.2490 2600 0.5031 - -
0.2586 2700 0.5393 - -
0.2682 2800 0.4979 - -
0.2778 2900 0.5221 - -
0.2874 3000 0.4956 0.5852 0.7495
0.2969 3100 0.506 - -
0.3065 3200 0.4962 - -
0.3161 3300 0.4713 - -
0.3257 3400 0.5016 - -
0.3353 3500 0.4749 - -
0.3448 3600 0.4732 - -
0.3544 3700 0.4789 - -
0.3640 3800 0.4825 - -
0.3736 3900 0.4803 - -
0.3832 4000 0.4471 0.5743 0.7546
0.3927 4100 0.4593 - -
0.4023 4200 0.4481 - -
0.4119 4300 0.4603 - -
0.4215 4400 0.4569 - -
0.4310 4500 0.4807 - -
0.4406 4600 0.4368 - -
0.4502 4700 0.4532 - -
0.4598 4800 0.4432 - -
0.4694 4900 0.4802 - -
0.4789 5000 0.4643 0.5663 0.7593
0.4885 5100 0.4154 - -
0.4981 5200 0.4441 - -
0.5077 5300 0.4156 - -
0.5173 5400 0.4273 - -
0.5268 5500 0.3988 - -
0.5364 5600 0.3942 - -
0.5460 5700 0.4186 - -
0.5556 5800 0.423 - -
0.5651 5900 0.434 - -
0.5747 6000 0.4136 0.5704 0.7616
0.5843 6100 0.3968 - -
0.5939 6200 0.4045 - -
0.6035 6300 0.4122 - -
0.6130 6400 0.3618 - -
0.6226 6500 0.341 - -
0.6322 6600 0.3689 - -
0.6418 6700 0.3621 - -
0.6514 6800 0.3774 - -
0.6609 6900 0.3519 - -
0.6705 7000 0.3974 0.5729 0.7644
0.6801 7100 0.3443 - -
0.6897 7200 0.3665 - -
0.6993 7300 0.3683 - -
0.7088 7400 0.3593 - -
0.7184 7500 0.3419 - -
0.7280 7600 0.3587 - -
0.7376 7700 0.3463 - -
0.7471 7800 0.3417 - -
0.7567 7900 0.32 - -
0.7663 8000 0.32 0.5735 0.7677
0.7759 8100 0.3296 - -
0.7855 8200 0.3492 - -
0.7950 8300 0.3022 - -
0.8046 8400 0.3159 - -
0.8142 8500 0.3172 - -
0.8238 8600 0.3157 - -
0.8334 8700 0.3271 - -
0.8429 8800 0.337 - -
0.8525 8900 0.322 - -
0.8621 9000 0.3187 0.5803 0.7652
0.8717 9100 0.307 - -
0.8812 9200 0.2984 - -
0.8908 9300 0.2727 - -
0.9004 9400 0.304 - -
0.9100 9500 0.321 - -
0.9196 9600 0.304 - -
0.9291 9700 0.3302 - -
0.9387 9800 0.3302 - -
0.9483 9900 0.3134 - -
0.9579 10000 0.2936 0.5858 0.7671
0.9675 10100 0.2953 - -
0.9770 10200 0.3035 - -
0.9866 10300 0.303 - -
0.9962 10400 0.2606 - -
1.0058 10500 0.2615 - -
1.0153 10600 0.2703 - -
1.0249 10700 0.2761 - -
1.0345 10800 0.2559 - -
1.0441 10900 0.2672 - -
1.0537 11000 0.2656 0.5933 0.7676
1.0632 11100 0.2825 - -
1.0728 11200 0.2484 - -
1.0824 11300 0.2472 - -
1.0920 11400 0.2678 - -
1.1016 11500 0.2443 - -
1.1111 11600 0.2685 - -
1.1207 11700 0.2504 - -
1.1303 11800 0.2431 - -
1.1399 11900 0.2248 - -
1.1495 12000 0.2229 0.5958 0.7688
1.1590 12100 0.228 - -
1.1686 12200 0.2304 - -
1.1782 12300 0.2193 - -
1.1878 12400 0.2238 - -
1.1973 12500 0.1957 - -
1.2069 12600 0.2075 - -
1.2165 12700 0.2014 - -
1.2261 12800 0.2222 - -
1.2357 12900 0.2059 - -
1.2452 13000 0.2051 0.6077 0.7651
1.2548 13100 0.2076 - -
1.2644 13200 0.226 - -
1.2740 13300 0.1941 - -
1.2836 13400 0.2053 - -
1.2931 13500 0.2003 - -
1.3027 13600 0.1947 - -
1.3123 13700 0.1914 - -
1.3219 13800 0.1956 - -
1.3314 13900 0.1862 - -
1.3410 14000 0.1873 0.6110 0.7646
1.3506 14100 0.1812 - -
1.3602 14200 0.1828 - -
1.3698 14300 0.1696 - -
1.3793 14400 0.1705 - -
1.3889 14500 0.1746 - -
1.3985 14600 0.1756 - -
1.4081 14700 0.1682 - -
1.4177 14800 0.1769 - -
1.4272 14900 0.1795 - -
1.4368 15000 0.1736 0.6278 0.7616
1.4464 15100 0.1546 - -
1.4560 15200 0.1643 - -
1.4656 15300 0.1903 - -
1.4751 15400 0.1902 - -
1.4847 15500 0.1531 - -
1.4943 15600 0.1711 - -
1.5039 15700 0.1546 - -
1.5134 15800 0.1503 - -
1.5230 15900 0.1429 - -
1.5326 16000 0.147 0.6306 0.7623
1.5422 16100 0.1507 - -
1.5518 16200 0.152 - -
1.5613 16300 0.1602 - -
1.5709 16400 0.1541 - -
1.5805 16500 0.1491 - -
1.5901 16600 0.1378 - -
1.5997 16700 0.1505 - -
1.6092 16800 0.1334 - -
1.6188 16900 0.1288 - -
1.6284 17000 0.1168 0.6372 0.7629
1.6380 17100 0.135 - -
1.6475 17200 0.1239 - -
1.6571 17300 0.1398 - -
1.6667 17400 0.1292 - -
1.6763 17500 0.1414 - -
1.6859 17600 0.116 - -
1.6954 17700 0.1302 - -
1.7050 17800 0.1194 - -
1.7146 17900 0.1394 - -
1.7242 18000 0.1316 0.6561 0.7592
1.7338 18100 0.1246 - -
1.7433 18200 0.1277 - -
1.7529 18300 0.1055 - -
1.7625 18400 0.1211 - -
1.7721 18500 0.1107 - -
1.7817 18600 0.1145 - -
1.7912 18700 0.1162 - -
1.8008 18800 0.1114 - -
1.8104 18900 0.1182 - -
1.8200 19000 0.1152 0.6567 0.7591
1.8295 19100 0.1212 - -
1.8391 19200 0.1253 - -
1.8487 19300 0.115 - -
1.8583 19400 0.1292 - -
1.8679 19500 0.1151 - -
1.8774 19600 0.1005 - -
1.8870 19700 0.1079 - -
1.8966 19800 0.0954 - -
1.9062 19900 0.1045 - -
1.9158 20000 0.1086 0.6727 0.7554
1.9253 20100 0.1174 - -
1.9349 20200 0.1108 - -
1.9445 20300 0.0992 - -
1.9541 20400 0.1168 - -
1.9636 20500 0.1028 - -
1.9732 20600 0.1126 - -
1.9828 20700 0.1113 - -
1.9924 20800 0.1065 - -
2.0020 20900 0.078 - -
2.0115 21000 0.0921 0.6727 0.7568
2.0211 21100 0.0866 - -
2.0307 21200 0.0918 - -
2.0403 21300 0.0893 - -
2.0499 21400 0.0882 - -
2.0594 21500 0.0986 - -
2.0690 21600 0.0923 - -
2.0786 21700 0.0805 - -
2.0882 21800 0.0887 - -
2.0978 21900 0.1 - -
2.1073 22000 0.0957 0.6854 0.7539
2.1169 22100 0.0921 - -
2.1265 22200 0.0892 - -
2.1361 22300 0.0805 - -
2.1456 22400 0.0767 - -
2.1552 22500 0.0715 - -
2.1648 22600 0.083 - -
2.1744 22700 0.0755 - -
2.1840 22800 0.075 - -
2.1935 22900 0.0724 - -
2.2031 23000 0.0822 0.6913 0.7534
2.2127 23100 0.0623 - -
2.2223 23200 0.0765 - -
2.2319 23300 0.0755 - -
2.2414 23400 0.0786 - -
2.2510 23500 0.0651 - -
2.2606 23600 0.081 - -
2.2702 23700 0.0664 - -
2.2797 23800 0.0906 - -
2.2893 23900 0.0714 - -
2.2989 24000 0.0703 0.6971 0.7536
2.3085 24100 0.0672 - -
2.3181 24200 0.0754 - -
2.3276 24300 0.0687 - -
2.3372 24400 0.0668 - -
2.3468 24500 0.0616 - -
2.3564 24600 0.0693 - -
2.3660 24700 0.0587 - -
2.3755 24800 0.0612 - -
2.3851 24900 0.0559 - -
2.3947 25000 0.0676 0.7128 0.7497
2.4043 25100 0.0607 - -
2.4139 25200 0.0727 - -
2.4234 25300 0.0573 - -
2.4330 25400 0.0717 - -
2.4426 25500 0.0493 - -
2.4522 25600 0.0558 - -
2.4617 25700 0.0676 - -
2.4713 25800 0.0757 - -
2.4809 25900 0.0735 - -
2.4905 26000 0.056 0.7044 0.7513
2.5001 26100 0.0687 - -
2.5096 26200 0.0592 - -
2.5192 26300 0.057 - -
2.5288 26400 0.0444 - -
2.5384 26500 0.0547 - -
2.5480 26600 0.0605 - -
2.5575 26700 0.066 - -
2.5671 26800 0.0631 - -
2.5767 26900 0.0634 - -
2.5863 27000 0.0537 0.7127 0.7512
2.5958 27100 0.0535 - -
2.6054 27200 0.0572 - -
2.6150 27300 0.0473 - -
2.6246 27400 0.0418 - -
2.6342 27500 0.0585 - -
2.6437 27600 0.0475 - -
2.6533 27700 0.0549 - -
2.6629 27800 0.0452 - -
2.6725 27900 0.0514 - -
2.6821 28000 0.0449 0.7337 0.7482
2.6916 28100 0.0544 - -
2.7012 28200 0.041 - -
2.7108 28300 0.0599 - -
2.7204 28400 0.057 - -
2.7300 28500 0.0503 - -
2.7395 28600 0.0487 - -
2.7491 28700 0.0503 - -
2.7587 28800 0.0446 - -
2.7683 28900 0.042 - -
2.7778 29000 0.0501 0.7422 0.7469
2.7874 29100 0.0494 - -
2.7970 29200 0.0423 - -
2.8066 29300 0.0508 - -
2.8162 29400 0.0459 - -
2.8257 29500 0.0514 - -
2.8353 29600 0.0484 - -
2.8449 29700 0.0571 - -
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2.9598 30900 0.0454 - -
2.9694 31000 0.0433 0.7580 0.745
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2.9886 31200 0.0537 - -
2.9982 31300 0.033 - -
3.0077 31400 0.0384 - -
3.0173 31500 0.0349 - -
3.0269 31600 0.0365 - -
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3.0460 31800 0.0396 - -
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3.0652 32000 0.0443 0.7592 0.7454
3.0748 32100 0.0323 - -
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3.0939 32300 0.0463 - -
3.1035 32400 0.0397 - -
3.1131 32500 0.0425 - -
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3.1323 32700 0.0454 - -
3.1418 32800 0.0287 - -
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3.1610 33000 0.0341 0.7672 0.7431
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3.3526 35000 0.0318 0.7782 0.7436
3.3621 35100 0.0283 - -
3.3717 35200 0.029 - -
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3.4005 35500 0.0326 - -
3.4100 35600 0.031 - -
3.4196 35700 0.0304 - -
3.4292 35800 0.0314 - -
3.4388 35900 0.0286 - -
3.4484 36000 0.0229 0.7978 0.7428
3.4579 36100 0.0258 - -
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3.5154 36700 0.0307 - -
3.5250 36800 0.0251 - -
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3.5920 37500 0.026 - -
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3.6112 37700 0.0268 - -
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3.6304 37900 0.0259 - -
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3.7070 38700 0.0305 - -
3.7166 38800 0.0368 - -
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3.7549 39200 0.0257 - -
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3.7740 39400 0.0209 - -
3.7836 39500 0.0298 - -
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3.8124 39800 0.0279 - -
3.8219 39900 0.0283 - -
3.8315 40000 0.0239 0.7982 0.7424
3.8411 40100 0.0378 - -
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3.8602 40300 0.0321 - -
3.8698 40400 0.0289 - -
3.8794 40500 0.027 - -
3.8890 40600 0.0224 - -
3.8986 40700 0.0236 - -
3.9081 40800 0.0267 - -
3.9177 40900 0.0228 - -
3.9273 41000 0.0322 0.8101 0.7415
3.9369 41100 0.0262 - -
3.9465 41200 0.0276 - -
3.9560 41300 0.0292 - -
3.9656 41400 0.0278 - -
3.9752 41500 0.0262 - -
3.9848 41600 0.0306 - -
3.9943 41700 0.0238 - -
4.0039 41800 0.0165 - -
4.0135 41900 0.0241 - -
4.0231 42000 0.0211 0.8092 0.742
4.0327 42100 0.0257 - -
4.0422 42200 0.0236 - -
4.0518 42300 0.0254 - -
4.0614 42400 0.0248 - -
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4.0806 42600 0.0245 - -
4.0901 42700 0.0325 - -
4.0997 42800 0.0209 - -
4.1093 42900 0.033 - -
4.1189 43000 0.0265 0.8105 0.7412
4.1285 43100 0.027 - -
4.1380 43200 0.0208 - -
4.1476 43300 0.0179 - -
4.1572 43400 0.0194 - -
4.1668 43500 0.0217 - -
4.1763 43600 0.0212 - -
4.1859 43700 0.0226 - -
4.1955 43800 0.0252 - -
4.2051 43900 0.0293 - -
4.2147 44000 0.0216 0.8029 0.7414
4.2242 44100 0.029 - -
4.2338 44200 0.0216 - -
4.2434 44300 0.0251 - -
4.2530 44400 0.018 - -
4.2626 44500 0.025 - -
4.2721 44600 0.0225 - -
4.2817 44700 0.0303 - -
4.2913 44800 0.028 - -
4.3009 44900 0.0203 - -
4.3104 45000 0.026 0.8081 0.7405

Framework Versions

  • Python: 3.10.12
  • Sentence Transformers: 3.0.0
  • Transformers: 4.38.2
  • PyTorch: 2.1.2+cu121
  • Accelerate: 0.27.2
  • Datasets: 2.19.1
  • Tokenizers: 0.15.2

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",
}

CachedMultipleNegativesRankingLoss 

@misc{gao2021scaling,
    title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup}, 
    author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
    year={2021},
    eprint={2101.06983},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}