README.md

metadata

base_model: BAAI/bge-base-en-v1.5
datasets: []
language:
  - en
library_name: sentence-transformers
license: apache-2.0
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
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:6300
  - loss:MatryoshkaLoss
  - loss:MultipleNegativesRankingLoss
widget:
  - source_sentence: >-
      A number of factors may impact ESKD growth rates, including mortality
      rates for dialysis patients or CKD patients, the aging of the U.S.
      population, transplant rates, incidence rates for diseases that cause
      kidney failure such as diabetes and hypertension, growth rates of minority
      populations with higher than average incidence rates of ESKD.
    sentences:
      - >-
        By how much did the company increase its quarterly cash dividend in
        February 2023?
      - What factors may impact the growth rates of the ESKD patient population?
      - >-
        What percentage increase did salaries and related costs experience at
        Delta Air Lines from 2022 to 2023?
  - source_sentence: HIV product sales increased 6% to $18.2 billion in 2023, compared to 2022.
    sentences:
      - >-
        What were the present values of lease liabilities for operating and
        finance leases as of December 31, 2023?
      - >-
        By what percentage did HIV product sales increase in 2023 compared to
        the previous year?
      - >-
        How is interest income not attributable to the Card Member loan
        portfolio primarily represented in financial documents?
  - source_sentence: >-
      If a violation is found, a broad range of remedies is potentially
      available to the Commission and/or CMA, including imposing a fine and/or
      the prohibition or restriction of certain business practices.
    sentences:
      - >-
        What are the potential remedies if a violation is found by the European
        Commission or the U.K. Competition and Markets Authority in their
        investigation of automotive companies?
      - >-
        By which auditing standards were the consolidated financial statements
        of Salesforce, Inc. audited?
      - >-
        What is the main role of Kroger's Chief Executive Officer in the
        company?
  - source_sentence: >-
      The discussion in Hewlett Packard Enterprise's Form 10-K highlights
      factors impacting costs and revenues, including easing supply chain
      constraints, foreign exchange pressures, inflationary trends, and recent
      tax developments potentially affecting their financial outcomes.
    sentences:
      - >-
        Is the outcome of the investigation into Tesla's waste segregation
        practices currently determinable?
      - >-
        How does Hewlett Packard Enterprise justify the exclusion of
        transformation costs from its non-GAAP financial measures?
      - >-
        In the context of Hewlett Packard Enterprise's recent financial
        discussions, what factors are expected to impact their operational costs
        and revenue growth moving forward?
  - source_sentence: >-
      Our Records Management and Data Management service revenue growth is being
      negatively impacted by declining activity rates as stored records and
      tapes are becoming less active and more archival.
    sentences:
      - >-
        How is Iron Mountain addressing the decline in activity rates in their
        Records and Data Management services?
      - >-
        What services do companies that build fiber-based networks provide in
        the Connectivity & Platforms markets?
      - >-
        What business outcomes is HPE focused on accelerating with its
        technological solutions?
model-index:
  - name: BGE base Financial Matryoshka
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 768
          type: dim_768
        metrics:
          - type: cosine_accuracy@1
            value: 0.7057142857142857
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8457142857142858
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8785714285714286
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9114285714285715
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7057142857142857
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2819047619047619
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17571428571428568
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09114285714285714
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7057142857142857
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8457142857142858
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8785714285714286
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9114285714285715
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8125296344519609
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7804263038548749
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7839408125709297
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.7071428571428572
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8428571428571429
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8742857142857143
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9114285714285715
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7071428571428572
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.28095238095238095
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17485714285714282
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09114285714285714
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7071428571428572
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8428571428571429
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8742857142857143
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9114285714285715
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8126517351231356
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7807267573696143
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7841188299664252
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.7028571428571428
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8357142857142857
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8685714285714285
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9071428571428571
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7028571428571428
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2785714285714286
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1737142857142857
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09071428571428572
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7028571428571428
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8357142857142857
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8685714285714285
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9071428571428571
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8086618947757659
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7768820861678005
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7806177775944575
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.6914285714285714
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.82
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8557142857142858
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9014285714285715
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6914285714285714
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2733333333333334
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17114285714285712
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09014285714285714
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6914285714285714
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.82
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8557142857142858
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9014285714285715
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7980982703041672
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7650045351473919
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7688564414027702
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.6542857142857142
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.7885714285714286
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8328571428571429
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8828571428571429
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6542857142857142
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.26285714285714284
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16657142857142856
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08828571428571427
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6542857142857142
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.7885714285714286
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8328571428571429
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8828571428571429
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7689665884678363
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7325351473922898
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7369423610264151
            name: Cosine Map@100

BGE base Financial Matryoshka

This is a sentence-transformers model finetuned from BAAI/bge-base-en-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: BAAI/bge-base-en-v1.5
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 768 tokens
• Similarity Function: Cosine Similarity
• 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': 512, 'do_lower_case': True}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)

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("NickyNicky/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'Our Records Management and Data Management service revenue growth is being negatively impacted by declining activity rates as stored records and tapes are becoming less active and more archival.',
    'How is Iron Mountain addressing the decline in activity rates in their Records and Data Management services?',
    'What services do companies that build fiber-based networks provide in the Connectivity & Platforms markets?',
]
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

Metric	Value
cosine_accuracy@1	0.7057
cosine_accuracy@3	0.8457
cosine_accuracy@5	0.8786
cosine_accuracy@10	0.9114
cosine_precision@1	0.7057
cosine_precision@3	0.2819
cosine_precision@5	0.1757
cosine_precision@10	0.0911
cosine_recall@1	0.7057
cosine_recall@3	0.8457
cosine_recall@5	0.8786
cosine_recall@10	0.9114
cosine_ndcg@10	0.8125
cosine_mrr@10	0.7804
cosine_map@100	0.7839

Information Retrieval

• Dataset: dim_512
• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7071
cosine_accuracy@3	0.8429
cosine_accuracy@5	0.8743
cosine_accuracy@10	0.9114
cosine_precision@1	0.7071
cosine_precision@3	0.281
cosine_precision@5	0.1749
cosine_precision@10	0.0911
cosine_recall@1	0.7071
cosine_recall@3	0.8429
cosine_recall@5	0.8743
cosine_recall@10	0.9114
cosine_ndcg@10	0.8127
cosine_mrr@10	0.7807
cosine_map@100	0.7841

Information Retrieval

• Dataset: dim_256
• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7029
cosine_accuracy@3	0.8357
cosine_accuracy@5	0.8686
cosine_accuracy@10	0.9071
cosine_precision@1	0.7029
cosine_precision@3	0.2786
cosine_precision@5	0.1737
cosine_precision@10	0.0907
cosine_recall@1	0.7029
cosine_recall@3	0.8357
cosine_recall@5	0.8686
cosine_recall@10	0.9071
cosine_ndcg@10	0.8087
cosine_mrr@10	0.7769
cosine_map@100	0.7806

Information Retrieval

• Dataset: dim_128
• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6914
cosine_accuracy@3	0.82
cosine_accuracy@5	0.8557
cosine_accuracy@10	0.9014
cosine_precision@1	0.6914
cosine_precision@3	0.2733
cosine_precision@5	0.1711
cosine_precision@10	0.0901
cosine_recall@1	0.6914
cosine_recall@3	0.82
cosine_recall@5	0.8557
cosine_recall@10	0.9014
cosine_ndcg@10	0.7981
cosine_mrr@10	0.765
cosine_map@100	0.7689

Information Retrieval

• Dataset: dim_64
• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6543
cosine_accuracy@3	0.7886
cosine_accuracy@5	0.8329
cosine_accuracy@10	0.8829
cosine_precision@1	0.6543
cosine_precision@3	0.2629
cosine_precision@5	0.1666
cosine_precision@10	0.0883
cosine_recall@1	0.6543
cosine_recall@3	0.7886
cosine_recall@5	0.8329
cosine_recall@10	0.8829
cosine_ndcg@10	0.769
cosine_mrr@10	0.7325
cosine_map@100	0.7369

Training Details

Training Dataset

Unnamed Dataset

• Size: 6,300 training samples
• Columns: positive and anchor
• Approximate statistics based on the first 1000 samples:

  	positive	anchor
  type	string	string
  details	min: 10 tokens mean: 46.55 tokens max: 512 tokens	min: 7 tokens mean: 20.56 tokens max: 42 tokens

• Samples:

  positive	anchor
  Internationally, Visa Inc.'s commercial payments volume grew by 23% from $407 billion in 2021 to $500 billion in 2022.	What was the growth rate of Visa Inc.'s commercial payments volume internationally between 2021 and 2022?
  The consolidated financial statements and accompanying notes listed in Part IV, Item 15(a)(1) of this Annual Report on Form 10-K are included immediately following Part IV hereof.	Where can one find the consolidated financial statements and accompanying notes in the Annual Report on Form 10-K?
  The additional paid-in capital at the end of 2023 was recorded as $114,519 million.	What was the amount recorded for additional paid-in capital at the end of 2023?

• 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: 80
• per_device_eval_batch_size: 16
• gradient_accumulation_steps: 16
• learning_rate: 2e-05
• num_train_epochs: 15
• lr_scheduler_type: cosine
• warmup_ratio: 0.1
• bf16: True
• tf32: 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: 80
• 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
• 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: 15
• 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: True
• 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_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: 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_eval_metrics: False
• batch_sampler: no_duplicates
• multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	dim_128_cosine_map@100	dim_256_cosine_map@100	dim_512_cosine_map@100	dim_64_cosine_map@100	dim_768_cosine_map@100
0.8101	4	-	0.7066	0.7309	0.7390	0.6462	0.7441
1.8228	9	-	0.7394	0.7497	0.7630	0.6922	0.7650
2.0253	10	2.768	-	-	-	-	-
2.8354	14	-	0.7502	0.7625	0.7767	0.7208	0.7787
3.8481	19	-	0.7553	0.7714	0.7804	0.7234	0.7802
4.0506	20	1.1294	-	-	-	-	-
4.8608	24	-	0.7577	0.7769	0.7831	0.7327	0.7858
5.8734	29	-	0.7616	0.7775	0.7832	0.7335	0.7876
6.0759	30	0.7536	-	-	-	-	-
6.8861	34	-	0.7624	0.7788	0.7832	0.7352	0.7882
7.8987	39	-	0.7665	0.7795	0.7814	0.7359	0.7861
8.1013	40	0.5846	-	-	-	-	-
8.9114	44	-	0.7688	0.7801	0.7828	0.7360	0.7857
9.9241	49	-	0.7698	0.7804	0.7836	0.7367	0.7840
10.1266	50	0.5187	-	-	-	-	-
10.9367	54	-	0.7692	0.7801	0.7827	0.7383	0.7837
11.9494	59	-	0.7698	0.7801	0.7834	0.7377	0.7849
12.1519	60	0.4949	0.7689	0.7806	0.7841	0.7369	0.7839

Framework Versions

• Python: 3.10.12
• Sentence Transformers: 3.0.1
• Transformers: 4.41.2
• PyTorch: 2.2.0+cu121
• Accelerate: 0.31.0
• Datasets: 2.19.1
• 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}
}