metadata

base_model: nomic-ai/nomic-embed-text-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: >-
      Chevron aims to support a diverse and inclusive supply chain that reflects
      the communities where they operate, believing that a diverse supply chain
      contributes to their success and growth.
    sentences:
      - >-
        What was the renewal rate for Costco memberships in the U.S. and Canada
        at the end of 2023?
      - >-
        What is Chevron's approach towards maintaining a diverse and inclusive
        supply chain?
      - What percentage growth did LinkedIn revenue experience?
  - source_sentence: >-
      Visa Direct is part of Visa’s strategy beyond C2B payments and helps
      facilitate the delivery of funds to eligible cards, deposit accounts and
      digital wallets across more than 190 countries and territories. Visa
      Direct supports multiple use cases, such as P2P payments and
      account-to-account transfers, business and government payouts to
      individuals or small businesses, merchant settlements and refunds.
    sentences:
      - >-
        What type of situations will the company record a liability for legal
        proceedings?
      - What is the purpose of Visa Direct?
      - What benefits does Airbnb's AirCover for guests offer?
  - source_sentence: >-
      As of December 31, 2023, we had $267 million of total unrecognized
      compensation cost related to nonvested stock-based compensation awards
      granted under our plans.
    sentences:
      - >-
        How much total unrecognized compensation cost related to nonvested
        stock-based compensation awards was reported as of December 31, 2023?
      - >-
        What changes are planned for the company's reporting metrics starting in
        fiscal year 202es and how does this affect the treatment of paused
        subscriptions?
      - >-
        How much does HP expect to pay for benefit claims for its
        post-retirement benefit plans in fiscal year 2024?
  - source_sentence: >-
      Discrete tax items resulted in a (benefit) provision for income taxes of
      $(18.1) million and $(11.9) million for the years ended December 31, 2023
      and 2022, respectively.
    sentences:
      - >-
        What was the total cost of TNT Express's business realignment through
        2023?
      - >-
        What is the purpose of adding research and development expenses and
        general and administrative expenses to the loss from operations when
        calculating the contribution margin?
      - >-
        What impact did discrete tax items have on the tax provision in 2023
        compared to 2022?
  - source_sentence: >-
      The company may issue debt or equity securities occasionally to provide
      additional liquidity or pursue opportunities to enhance its long-term
      competitive position while maintaining a strong balance sheet. 
    sentences:
      - >-
        What might the company do to increase liquidity or pursue long-term
        competitive advantages while managing a strong balance sheet?
      - >-
        What types of technologies does the Mortgage Technology segment employ
        to enhance operational efficiency?
      - >-
        Which section of a financial document covers Financial Statements and
        Supplementary Data?
model-index:
  - name: Nomic Embed 1.5 Financial Matryoshka
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 768
          type: dim_768
        metrics:
          - type: cosine_accuracy@1
            value: 0.6928571428571428
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8228571428571428
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.87
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9071428571428571
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6928571428571428
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2742857142857143
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.174
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.0907142857142857
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6928571428571428
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8228571428571428
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.87
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9071428571428571
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8029973671837228
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7692715419501133
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7724352164684344
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.6914285714285714
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8271428571428572
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.87
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9085714285714286
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6914285714285714
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2757142857142857
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.174
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09085714285714284
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6914285714285714
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8271428571428572
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.87
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9085714285714286
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8029523922190992
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7687732426303853
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7717841390041892
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.6871428571428572
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8285714285714286
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8728571428571429
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8985714285714286
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6871428571428572
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.27619047619047615
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17457142857142854
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08985714285714284
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6871428571428572
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8285714285714286
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8728571428571429
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8985714285714286
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7983704009707536
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7655901360544215
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7693376855880492
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.6671428571428571
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8185714285714286
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8557142857142858
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8957142857142857
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6671428571428571
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.27285714285714285
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17114285714285712
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08957142857142855
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6671428571428571
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8185714285714286
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8557142857142858
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8957142857142857
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7849638501826605
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7491031746031743
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.752516331310788
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.6528571428571428
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.7871428571428571
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8271428571428572
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8771428571428571
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6528571428571428
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2623809523809524
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1654285714285714
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.0877142857142857
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6528571428571428
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.7871428571428571
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8271428571428572
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8771428571428571
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7639694587103518
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7279750566893419
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7317631790989764
            name: Cosine Map@100

Nomic Embed 1.5 Financial Matryoshka

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
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("venkateshmurugadas/nomic-v1.5-financial-matryoshka")
# Run inference
sentences = [
    'The company may issue debt or equity securities occasionally to provide additional liquidity or pursue opportunities to enhance its long-term competitive position while maintaining a strong balance sheet. ',
    'What might the company do to increase liquidity or pursue long-term competitive advantages while managing a strong balance sheet?',
    'What types of technologies does the Mortgage Technology segment employ to enhance operational efficiency?',
]
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	0.6929
cosine_accuracy@3	0.8229
cosine_accuracy@5	0.87
cosine_accuracy@10	0.9071
cosine_precision@1	0.6929
cosine_precision@3	0.2743
cosine_precision@5	0.174
cosine_precision@10	0.0907
cosine_recall@1	0.6929
cosine_recall@3	0.8229
cosine_recall@5	0.87
cosine_recall@10	0.9071
cosine_ndcg@10	0.803
cosine_mrr@10	0.7693
cosine_map@100	0.7724

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6914
cosine_accuracy@3	0.8271
cosine_accuracy@5	0.87
cosine_accuracy@10	0.9086
cosine_precision@1	0.6914
cosine_precision@3	0.2757
cosine_precision@5	0.174
cosine_precision@10	0.0909
cosine_recall@1	0.6914
cosine_recall@3	0.8271
cosine_recall@5	0.87
cosine_recall@10	0.9086
cosine_ndcg@10	0.803
cosine_mrr@10	0.7688
cosine_map@100	0.7718

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6871
cosine_accuracy@3	0.8286
cosine_accuracy@5	0.8729
cosine_accuracy@10	0.8986
cosine_precision@1	0.6871
cosine_precision@3	0.2762
cosine_precision@5	0.1746
cosine_precision@10	0.0899
cosine_recall@1	0.6871
cosine_recall@3	0.8286
cosine_recall@5	0.8729
cosine_recall@10	0.8986
cosine_ndcg@10	0.7984
cosine_mrr@10	0.7656
cosine_map@100	0.7693

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6671
cosine_accuracy@3	0.8186
cosine_accuracy@5	0.8557
cosine_accuracy@10	0.8957
cosine_precision@1	0.6671
cosine_precision@3	0.2729
cosine_precision@5	0.1711
cosine_precision@10	0.0896
cosine_recall@1	0.6671
cosine_recall@3	0.8186
cosine_recall@5	0.8557
cosine_recall@10	0.8957
cosine_ndcg@10	0.785
cosine_mrr@10	0.7491
cosine_map@100	0.7525

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6529
cosine_accuracy@3	0.7871
cosine_accuracy@5	0.8271
cosine_accuracy@10	0.8771
cosine_precision@1	0.6529
cosine_precision@3	0.2624
cosine_precision@5	0.1654
cosine_precision@10	0.0877
cosine_recall@1	0.6529
cosine_recall@3	0.7871
cosine_recall@5	0.8271
cosine_recall@10	0.8771
cosine_ndcg@10	0.764
cosine_mrr@10	0.728
cosine_map@100	0.7318

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: 8 tokens
mean: 46.46 tokens
max: 371 tokens

min: 2 tokens
mean: 20.45 tokens
max: 41 tokens

	positive	anchor
type	string	string
details	min: 8 tokens mean: 46.46 tokens max: 371 tokens	min: 2 tokens mean: 20.45 tokens max: 41 tokens

Samples:

positive	anchor
`We evaluate uncertain tax positions periodically, considering changes in facts and circumstances, such as new regulations or recent judicial opinions, as well as the status of audit activities by taxing authorities.`	`How are changes to a company's uncertain tax positions evaluated?`
`During 2022 and 2023, our operating margin was impacted by increased wage rates. During 2022, our gross margin was impacted by higher air freight costs as a result of global supply chain disruption.`	`What effects did inflation have on the company's operating results during 2022 and 2023?`
To mitigate these developments, we are continually working to evolve our advertising systems to improve the performance of our ad products. We are developing privacy enhancing technologies to deliver relevant ads and measurement capabilities while reducing the amount of personal information we process, including by relying more on anonymized or aggregated third-party data. In addition, we are developing tools that enable marketers to share their data into our systems, as well as ad products that generate more valuable signals within our apps. More broadly, we also continue to innovate our advertising tools to help marketers prepare campaigns and connect with consumers, including developing growing formats such as Reels ads and our business messaging ad products. Across all of these efforts, we are making significant investments in artificial intelligence (AI), including generative AI, to improve our delivery, targeting, and measurement capabilities. Further, we are focused on driving onsite conversions in our business messaging ad products by developing new features and scaling existing features.	`What technological solutions is the company developing to improve ad delivery?`

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: 4
per_device_eval_batch_size: 4
gradient_accumulation_steps: 64
learning_rate: 2e-05
num_train_epochs: 4
lr_scheduler_type: cosine
warmup_ratio: 0.1
fp16: True
tf32: False
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: 4
per_device_eval_batch_size: 4
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 64
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: 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: False
fp16: True
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: False
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}
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
0.4063	10	0.1329	-	-	-	-	-
0.8127	20	0.0567	-	-	-	-	-
0.9752	24	-	0.7416	0.7604	0.7678	0.7249	0.7758
1.2190	30	0.0415	-	-	-	-	-
1.6254	40	0.0043	-	-	-	-	-
1.9911	49	-	0.7491	0.7648	0.7700	0.7315	0.7731
2.0317	50	0.0059	-	-	-	-	-
2.4381	60	0.0045	-	-	-	-	-
2.8444	70	0.0013	-	-	-	-	-
2.9663	73	-	0.7531	0.7703	0.7712	0.7327	0.7738
3.2508	80	0.0031	-	-	-	-	-
3.6571	90	0.0009	-	-	-	-	-
3.9010	96	-	0.7525	0.7693	0.7718	0.7318	0.7724

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.0.1
Transformers: 4.41.2
PyTorch: 2.1.2+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}
}