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 change in key assumptions such as the discount rate or projected future
      revenues, expenses and cash flows could materially affect the
      determination of fair values.
    sentences:
      - >-
        How many shares of common stock were sold in fiscal 2021 under GameStop
        Corp.'s at-the-market equity offering programs?
      - >-
        How does a change in key assumptions potentially affect the
        determination of fair values of assets?
      - What is the primary revenue source for Comcast's Theme Parks segment?
  - source_sentence: >-
      In January 2023, we announced our intention to implement a cost reduction
      program to reduce automotive fixed costs by $2.0 billion on an annual run
      rate basis by the end of 2024. This goal includes the impact of higher
      expected depreciation and amortization expense and inflationary cost
      increases on fixed cost but excludes changes in our pension income. In
      addition to people costs, we are reducing our marketing and advertising
      expenses, streamlining our engineering expense by reducing complexity
      across the vehicle portfolio, adjusting the cadivers-SafieiaıcıUrbanıcık,
      prioritizing growth initiatives, and reducing our overall overhead and
      discretionary costs.
    sentences:
      - >-
        What method does AbbVie primarily use to record investments in equity
        securities with readily determinable fair values?
      - >-
        What measures is General Motors taking to reduce costs and streamline
        operations?
      - >-
        As of December 31, 2023, what is the total balance of acquisitions,
        foreign currency translation and other adjustments?
  - source_sentence: >-
      AutoZone utilizes a computerized proprietary Point-of-Sale System
      including bar code scanning and terminals to enhance customer service by
      efficiently processing transactions and assisting in administrative tasks.
    sentences:
      - How does AutoZone's Point-of-Sale System enhance customer service?
      - What unique feature did fiscal year 2021 have compared to 2023 and 2022?
      - >-
        What was the primary source of the increase in premiums written by
        Berkshire Hathaway's Property/Casualty reinsurance in 2023?
  - source_sentence: >-
      In 2023, capital expenditures for aircraft and related equipment by FedEx
      Express saw a decrease of 26% compared to 2022.
    sentences:
      - >-
        What was the increase in earnings from operations for Optum from 2022 to
        2023?
      - >-
        What did the FCA require regarding the continued publication of certain
        LIBOR settings after 2021?
      - >-
        What was the percentage decrease in FedEx's aircraft and related
        equipment capital expenditures in 2023 compared to 2022?
  - source_sentence: >-
      In 1983, Walmart opened its first Sam's Club, and in 1988, it opened its
      first supercenter.
    sentences:
      - When did Walmart open its first Sam's Club and supercenter?
      - >-
        Which standards and guidelines does the company use for informing its
        sustainability disclosures?
      - >-
        What accounting treatment does the Company apply to refunds issued to
        customers?
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: 0.7028571428571428
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8371428571428572
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8728571428571429
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9185714285714286
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7028571428571428
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.27904761904761904
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17457142857142854
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09185714285714283
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7028571428571428
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8371428571428572
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8728571428571429
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9185714285714286
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.81196519287814
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7777465986394556
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7809887604595412
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.6985714285714286
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8328571428571429
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8642857142857143
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9242857142857143
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6985714285714286
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2776190476190476
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17285714285714285
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09242857142857142
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6985714285714286
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8328571428571429
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8642857142857143
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9242857142857143
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8104528945408784
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7743191609977326
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7771143041520369
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.6942857142857143
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8271428571428572
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8585714285714285
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9085714285714286
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6942857142857143
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2757142857142857
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1717142857142857
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09085714285714284
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6942857142857143
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8271428571428572
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8585714285714285
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9085714285714286
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8026074561436641
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7686825396825395
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7726124326414546
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.6885714285714286
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8157142857142857
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8571428571428571
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9071428571428571
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6885714285714286
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.27190476190476187
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1714285714285714
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09071428571428569
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6885714285714286
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8157142857142857
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8571428571428571
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9071428571428571
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7972617985734928
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7622108843537415
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.765720886169324
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.66
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.7985714285714286
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8357142857142857
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8828571428571429
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.66
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2661904761904762
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1671428571428571
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08828571428571427
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.66
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.7985714285714286
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8357142857142857
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8828571428571429
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7715751288332002
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7360753968253966
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7400601081956545
            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("anishareddyalla/bge-base-financial-matryoshka-anisha")
# Run inference
sentences = [
    "In 1983, Walmart opened its first Sam's Club, and in 1988, it opened its first supercenter.",
    "When did Walmart open its first Sam's Club and supercenter?",
    'Which standards and guidelines does the company use for informing its sustainability disclosures?',
]
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	0.7029
cosine_accuracy@3	0.8371
cosine_accuracy@5	0.8729
cosine_accuracy@10	0.9186
cosine_precision@1	0.7029
cosine_precision@3	0.279
cosine_precision@5	0.1746
cosine_precision@10	0.0919
cosine_recall@1	0.7029
cosine_recall@3	0.8371
cosine_recall@5	0.8729
cosine_recall@10	0.9186
cosine_ndcg@10	0.812
cosine_mrr@10	0.7777
cosine_map@100	0.781

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6986
cosine_accuracy@3	0.8329
cosine_accuracy@5	0.8643
cosine_accuracy@10	0.9243
cosine_precision@1	0.6986
cosine_precision@3	0.2776
cosine_precision@5	0.1729
cosine_precision@10	0.0924
cosine_recall@1	0.6986
cosine_recall@3	0.8329
cosine_recall@5	0.8643
cosine_recall@10	0.9243
cosine_ndcg@10	0.8105
cosine_mrr@10	0.7743
cosine_map@100	0.7771

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6943
cosine_accuracy@3	0.8271
cosine_accuracy@5	0.8586
cosine_accuracy@10	0.9086
cosine_precision@1	0.6943
cosine_precision@3	0.2757
cosine_precision@5	0.1717
cosine_precision@10	0.0909
cosine_recall@1	0.6943
cosine_recall@3	0.8271
cosine_recall@5	0.8586
cosine_recall@10	0.9086
cosine_ndcg@10	0.8026
cosine_mrr@10	0.7687
cosine_map@100	0.7726

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6886
cosine_accuracy@3	0.8157
cosine_accuracy@5	0.8571
cosine_accuracy@10	0.9071
cosine_precision@1	0.6886
cosine_precision@3	0.2719
cosine_precision@5	0.1714
cosine_precision@10	0.0907
cosine_recall@1	0.6886
cosine_recall@3	0.8157
cosine_recall@5	0.8571
cosine_recall@10	0.9071
cosine_ndcg@10	0.7973
cosine_mrr@10	0.7622
cosine_map@100	0.7657

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.66
cosine_accuracy@3	0.7986
cosine_accuracy@5	0.8357
cosine_accuracy@10	0.8829
cosine_precision@1	0.66
cosine_precision@3	0.2662
cosine_precision@5	0.1671
cosine_precision@10	0.0883
cosine_recall@1	0.66
cosine_recall@3	0.7986
cosine_recall@5	0.8357
cosine_recall@10	0.8829
cosine_ndcg@10	0.7716
cosine_mrr@10	0.7361
cosine_map@100	0.7401

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: 46.43 tokens
max: 439 tokens

min: 8 tokens
mean: 20.76 tokens
max: 43 tokens

	positive	anchor
type	string	string
details	min: 8 tokens mean: 46.43 tokens max: 439 tokens	min: 8 tokens mean: 20.76 tokens max: 43 tokens

Samples:

positive	anchor
`The Company’s human capital management strategy is built on three fundamental focus areas: Attracting and recruiting the best talent, Developing and retaining talent, Empowering and inspiring talent.`	`What strategies are outlined in the Company's human capital management?`
Opinion on the Consolidated Financial Statements We have audited the accompanying consolidated balance sheets of Costco Wholesale Corporation and subsidiaries (the Company) as of September 3, 2023, and August 28, 2022, the related consolidated statements of income, comprehensive income, equity, and cash flows for the 53-week period ended September 3, 2023, and the 52-week periods ended August 28, 2022, and August 29, 2021, and the related notes (collectively, the consolidated financial statements). In our opinion, the consolidated financial statements present fairly, in all material respects, the financial position of the Company as of September 3, 2023, and August 28, 2022, and the results of its operations and its cash flows for each of the 53-week period ended September 3, 2023, and the 52-week periods ended August 28, 2022, and August 29, 2021, in conformity with U.S. generally accepted accounting principles.	`What was the opinion of the independent registered public accounting firm on Costco Wholesale Corporation's consolidated financial statements for the year ended September 3, 2023?`
`Nonperforming loans and leases are generally those that have been placed on nonaccrual status, such as when they are 90 days past due or have confirmed cases of fraud or bankruptcy. Additionally, specific types of loans like consumer real estate-secured loans are classified as nonperforming at 90 days past due unless they are fully insured, and commercial loans and leases are classified as nonperforming when past due 90 days or more unless well-secured and in the process of collection.`	`What criteria are used to classify loans and leases as nonperforming according to the described credit policy?`

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
tf32: 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
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: 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: 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
eval_on_start: 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
0.8122	10	1.5488	-	-	-	-	-
0.9746	12	-	0.7540	0.7565	0.7660	0.7176	0.7693
1.6244	20	0.674	-	-	-	-	-
1.9492	24	-	0.7622	0.7715	0.7781	0.7352	0.7790
2.4365	30	0.4592	-	-	-	-	-
2.9239	36	-	0.7648	0.7729	0.7778	0.7384	0.7799
3.2487	40	0.4113	-	-	-	-	-
3.8985	48	-	0.7657	0.7726	0.7771	0.7401	0.7810

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.0.1
Transformers: 4.42.4
PyTorch: 2.3.1+cu121
Accelerate: 0.32.1
Datasets: 2.20.0
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}
}