README.md

metadata

language:
  - en
license: apache-2.0
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:5822
  - loss:MatryoshkaLoss
  - loss:MultipleNegativesRankingLoss
base_model: nomic-ai/nomic-embed-text-v1.5
widget:
  - source_sentence: >-
      submitted to the CIA for each year.”  Id. at 1–2.  On July 22, 2010, the
      CIA responded to this 

      request, stating “[w]e . . . have determined that our record systems are
      not configured in a way 

      that would allow us to perform a search reasonably calculated to lead to
      the responsive record 

      without an unreasonable effort.”  First Lutz Decl. Ex. L at 1, No. 11-444,
      ECF No. 20-3.  As a
    sentences:
      - How many instances of individual's names does the plaintiff point to?
      - What date did the CIA respond to the request?
      - >-
        What phrase does the Bar propose to delete references to in the Preamble
        to Chapter 4?
  - source_sentence: |-
      City Department of Education, the self-represented plaintiff 
      submitted a filing containing hallucinations.  No. 24-cv-04232, 
       
      20 
      2024 WL 3460049, at *7 (S.D.N.Y. July 18, 2024) (unpublished 
      opinion).  The court noted that “[s]anctions may be imposed for 
      submitting false and nonexistent legal authority to the [c]ourt.”  Id.  
      However, the court declined to impose sanctions due to the
    sentences:
      - >-
        In which sections of their opposition does the plaintiff discuss the
        deliberative-process privilege?
      - Who submitted the filing containing hallucinations?
      - When did the plaintiff file a motion?
  - source_sentence: >-
      § 424 and Exemption 3; Exemption 5; and/or Exemption 6.  See Second
      Williams Decl. Ex. A. 

      120 
       
      Therefore, the Court need not decide whether the DIA has the independent
      authority to invoke 

      the National Security Act as an Exemption 3 withholding statute. 

      3. 

      ODNI 

      Finally, the plaintiff challenges the ODNI’s decision to withhold certain
      portions of e-
    sentences:
      - How many counts did EPIC bring related to the APA?
      - Which organization's decision is being challenged by the plaintiff?
      - Does the Government agree with EPIC's claim about their Answer?
  - source_sentence: >-
      confidentiality agreement/order, that remain following those discussions. 
      This is a 

      final report and notice of exceptions shall be filed within three days of
      the date of 

      this report, pursuant to Court of Chancery Rule 144(d)(2), given the
      expedited and 

      summary nature of Section 220 proceedings.  
       
       
       
       
       
       
       
      Respectfully, 
       
       
       
       
       
       
       
       
      /s/ Patricia W. Griffin
    sentences:
      - Who signed this document?
      - Did Mr. Mooney allege that the video was altered or tampered with?
      - Did the plaintiff report the defendant at that time?
  - source_sentence: >-
      such an argument, and she does not offer any case law, cites to secondary
      sources, dictionaries 

      or grammatical texts, arguments by analogy, or other citations, except for
      the mere assertion 

      that defendant failed to move in a timely fashion after he was “on notice”
      of the ex parte order. 

      A reviewing court is entitled to have issues clearly defined with relevant
      authority cited.
    sentences:
      - What page is Cross-MJAR's emphasis mentioned on?
      - What mere assertion does she make?
      - On what dates did the Commission meet in 2019?
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - cosine_accuracy@1
  - cosine_accuracy@3
  - cosine_accuracy@5
  - cosine_accuracy@10
  - cosine_precision@1
  - cosine_precision@3
  - cosine_precision@5
  - cosine_precision@10
  - cosine_recall@1
  - cosine_recall@3
  - cosine_recall@5
  - cosine_recall@10
  - cosine_ndcg@10
  - cosine_mrr@10
  - cosine_map@100
model-index:
  - name: nomic-embed-text-v1.5
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 768
          type: dim_768
        metrics:
          - type: cosine_accuracy@1
            value: 0.5486862442040186
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.5965996908809892
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.7017001545595054
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.7697063369397218
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.5486862442040186
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.5239567233384853
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.40989180834621336
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.24142194744976814
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.19049459041731065
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.5101751674394642
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.6503091190108191
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.7595311695002576
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.6615339195276682
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.6004440519123668
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.6427552042140723
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.5409582689335394
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.58887171561051
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.6924265842349304
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.7743431221020093
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.5409582689335394
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.5172591447707368
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.4034003091190108
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.24188562596599691
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.18740340030911898
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.5054095826893354
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.6411643482740855
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.7622359608449253
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.6576404555647709
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.5934416476533937
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.6355153178607286
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.508500772797527
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.5564142194744977
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.6707882534775889
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.7449768160741885
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.508500772797527
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.4873776403915508
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.38639876352395675
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.23122102009273574
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.17671303451828954
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.47707367336424517
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.6141164348274084
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.7257856774858321
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.6257588263652936
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.562961531856431
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.6091899586876254
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.45131375579598143
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.5054095826893354
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.58887171561051
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.6862442040185471
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.45131375579598143
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.437403400309119
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.3415765069551777
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.21298299845440496
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.15700669757856775
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.4282586295723854
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.5426326635754766
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.6720762493560021
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.5679548352076085
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.503881160913618
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.5511797935827811
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.35239567233384855
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.3894899536321484
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.47295208655332305
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.5641421947449768
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.35239567233384855
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.33900051519835134
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.26955177743431225
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.1723338485316847
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.12171561051004637
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.33217413704276144
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.4310922205048943
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.5446934569809376
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.45200452556542003
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.39659662422413555
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.44614347894124107
            name: Cosine Map@100

nomic-embed-text-v1.5

This is a sentence-transformers model finetuned from nomic-ai/nomic-embed-text-v1.5 on the json 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: nomic-ai/nomic-embed-text-v1.5
• Maximum Sequence Length: 8192 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • json
• Language: en
• License: apache-2.0

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

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("Thejina/nomic-embed-text-finetuned")
# Run inference
sentences = [
    'such an argument, and she does not offer any case law, cites to secondary sources, dictionaries \nor grammatical texts, arguments by analogy, or other citations, except for the mere assertion \nthat defendant failed to move in a timely fashion after he was “on notice” of the ex parte order. \nA reviewing court is entitled to have issues clearly defined with relevant authority cited.',
    'What mere assertion does she make?',
    "What page is Cross-MJAR's emphasis mentioned on?",
]
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

Information Retrieval

• Dataset: dim_768
• Evaluated with InformationRetrievalEvaluator with these parameters:

  {
      "truncate_dim": 768
  }
  

Metric	Value
cosine_accuracy@1	0.5487
cosine_accuracy@3	0.5966
cosine_accuracy@5	0.7017
cosine_accuracy@10	0.7697
cosine_precision@1	0.5487
cosine_precision@3	0.524
cosine_precision@5	0.4099
cosine_precision@10	0.2414
cosine_recall@1	0.1905
cosine_recall@3	0.5102
cosine_recall@5	0.6503
cosine_recall@10	0.7595
cosine_ndcg@10	0.6615
cosine_mrr@10	0.6004
cosine_map@100	0.6428

Information Retrieval

• Dataset: dim_512
• Evaluated with InformationRetrievalEvaluator with these parameters:

  {
      "truncate_dim": 512
  }
  

Metric	Value
cosine_accuracy@1	0.541
cosine_accuracy@3	0.5889
cosine_accuracy@5	0.6924
cosine_accuracy@10	0.7743
cosine_precision@1	0.541
cosine_precision@3	0.5173
cosine_precision@5	0.4034
cosine_precision@10	0.2419
cosine_recall@1	0.1874
cosine_recall@3	0.5054
cosine_recall@5	0.6412
cosine_recall@10	0.7622
cosine_ndcg@10	0.6576
cosine_mrr@10	0.5934
cosine_map@100	0.6355

Information Retrieval

• Dataset: dim_256
• Evaluated with InformationRetrievalEvaluator with these parameters:

  {
      "truncate_dim": 256
  }
  

Metric	Value
cosine_accuracy@1	0.5085
cosine_accuracy@3	0.5564
cosine_accuracy@5	0.6708
cosine_accuracy@10	0.745
cosine_precision@1	0.5085
cosine_precision@3	0.4874
cosine_precision@5	0.3864
cosine_precision@10	0.2312
cosine_recall@1	0.1767
cosine_recall@3	0.4771
cosine_recall@5	0.6141
cosine_recall@10	0.7258
cosine_ndcg@10	0.6258
cosine_mrr@10	0.563
cosine_map@100	0.6092

Information Retrieval

• Dataset: dim_128
• Evaluated with InformationRetrievalEvaluator with these parameters:

  {
      "truncate_dim": 128
  }
  

Metric	Value
cosine_accuracy@1	0.4513
cosine_accuracy@3	0.5054
cosine_accuracy@5	0.5889
cosine_accuracy@10	0.6862
cosine_precision@1	0.4513
cosine_precision@3	0.4374
cosine_precision@5	0.3416
cosine_precision@10	0.213
cosine_recall@1	0.157
cosine_recall@3	0.4283
cosine_recall@5	0.5426
cosine_recall@10	0.6721
cosine_ndcg@10	0.568
cosine_mrr@10	0.5039
cosine_map@100	0.5512

Information Retrieval

• Dataset: dim_64
• Evaluated with InformationRetrievalEvaluator with these parameters:

  {
      "truncate_dim": 64
  }
  

Metric	Value
cosine_accuracy@1	0.3524
cosine_accuracy@3	0.3895
cosine_accuracy@5	0.473
cosine_accuracy@10	0.5641
cosine_precision@1	0.3524
cosine_precision@3	0.339
cosine_precision@5	0.2696
cosine_precision@10	0.1723
cosine_recall@1	0.1217
cosine_recall@3	0.3322
cosine_recall@5	0.4311
cosine_recall@10	0.5447
cosine_ndcg@10	0.452
cosine_mrr@10	0.3966
cosine_map@100	0.4461

Training Details

Training Dataset

json

• Dataset: json
• Size: 5,822 training samples
• Columns: positive and anchor
• Approximate statistics based on the first 1000 samples:

  	positive	anchor
  type	string	string
  details	min: 46 tokens mean: 91.09 tokens max: 324 tokens	min: 7 tokens mean: 16.89 tokens max: 43 tokens

• Samples:

  positive	anchor
  functional test, too. Id. at 89–90. Still, the Court made clear that this functional test was “not relevant.” Id. at 90. So, just as in Energy Research, its application of the functional test was dicta. And because this discussion relied on the dicta from Energy Research, this was dicta upon dicta. The Government is thus imprecise when it asserts as the “law of the case” that the	What page is the functional test mentioned as 'not relevant'?
  authenticated through his testimony under Maryland Rule 5-901(b)(1) as a witness with personal knowledge of the events. - 6 - The part of the video depicting the shooting was properly authenticated through circumstantial evidence under Maryland Rule 5-901(b)(4), as there was sufficient circumstantial evidence from which a reasonable juror could have inferred that the video	Which part of the video was authenticated?
  KLAN202300916 9 Los derechos morales, a su vez, están fundamentalmente protegidos por la legislación estatal. Esta reconoce los derechos de los autores como exclusivos de estos y los protege no solo en beneficio propio, sino también de la sociedad por la contribución social y cultural que históricamente se le ha reconocido a la	¿En beneficio de quién se protegen los derechos de los autores?

• 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

• eval_strategy: epoch
• per_device_train_batch_size: 32
• per_device_eval_batch_size: 16
• gradient_accumulation_steps: 16
• learning_rate: 2e-05
• num_train_epochs: 4
• lr_scheduler_type: cosine
• warmup_ratio: 0.1
• bf16: True
• load_best_model_at_end: True
• optim: adamw_torch_fused
• batch_sampler: no_duplicates

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: epoch
• prediction_loss_only: True
• per_device_train_batch_size: 32
• per_device_eval_batch_size: 16
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 16
• eval_accumulation_steps: None
• torch_empty_cache_steps: None
• learning_rate: 2e-05
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1.0
• num_train_epochs: 4
• max_steps: -1
• lr_scheduler_type: cosine
• 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: 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}
• tp_size: 0
• 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_fused
• 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
• include_tokens_per_second: False
• include_num_input_tokens_seen: False
• neftune_noise_alpha: None
• optim_target_modules: None
• batch_eval_metrics: False
• eval_on_start: False
• use_liger_kernel: False
• eval_use_gather_object: False
• average_tokens_across_devices: False
• prompts: None
• batch_sampler: no_duplicates
• multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	dim_768_cosine_ndcg@10	dim_512_cosine_ndcg@10	dim_256_cosine_ndcg@10	dim_128_cosine_ndcg@10	dim_64_cosine_ndcg@10
0.8791	10	69.7578	-	-	-	-	-
1.0	12	-	0.6178	0.6069	0.5742	0.5088	0.4115
1.7033	20	28.4334	-	-	-	-	-
2.0	24	-	0.6589	0.6509	0.6268	0.5616	0.4494
2.5275	30	20.1123	-	-	-	-	-
3.0	36	-	0.6621	0.6573	0.6263	0.5677	0.4508
3.3516	40	16.5444	-	-	-	-	-
3.7033	44	-	0.6615	0.6576	0.6258	0.568	0.452

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.11.12
• Sentence Transformers: 4.1.0
• Transformers: 4.51.3
• PyTorch: 2.6.0+cu124
• Accelerate: 1.6.0
• Datasets: 3.6.0
• Tokenizers: 0.21.1

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}
}