metadata

language:
  - en
license: apache-2.0
library_name: sentence-transformers
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - dataset_size:1K<n<10K
  - loss:MatryoshkaLoss
  - loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-base-en-v1.5
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
widget:
  - source_sentence: What begins on page 105 of this report?
    sentences:
      - >-
        What sections are included alongside the Financial Statements in this
        report?
      - How did net revenues change from 2021 to 2022 on a FX-Neutral basis?
      - How much did MedTech's sales increase in 2023 compared to 2022?
  - source_sentence: When does the Company's fiscal year end?
    sentences:
      - >-
        What was the total store count for the company at the end of fiscal
        2022?
      - What was the total revenue for all UnitedHealthcare services in 2023?
      - >-
        What were the main factors contributing to the increase in net income in
        2023?
  - source_sentence: AutoZone, Inc. began operations in 1979.
    sentences:
      - When did AutoZone, Inc. begin its operations?
      - Mr. Pleas was named Senior Vice President and Controller during 2007.
      - Which item discusses Financial Statements and Supplementary Data?
  - source_sentence: Are the ESG goals guaranteed to be met?
    sentences:
      - What measures is the company implementing to support climate goals?
      - What types of diseases does Gilead Sciences, Inc. focus on treating?
      - >-
        Changes in foreign exchange rates reduced cost of sales by $254 million
        in 2023.
  - source_sentence: What was Gilead's total revenue in 2023?
    sentences:
      - What was the total revenue for the year ended December 31, 2023?
      - How much was the impairment related to the CAT loan receivable in 2023?
      - >-
        What are some of the critical accounting policies that affect financial
        statements?
pipeline_tag: sentence-similarity
model-index:
  - name: BGE base Financial Matryoshka
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: basline 768
          type: basline_768
        metrics:
          - type: cosine_accuracy@1
            value: 0.7085714285714285
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8514285714285714
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8842857142857142
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9271428571428572
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7085714285714285
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2838095238095238
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17685714285714282
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09271428571428571
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7085714285714285
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8514285714285714
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8842857142857142
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9271428571428572
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8214972164555796
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7873509070294781
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.790665594958196
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: basline 512
          type: basline_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.7114285714285714
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.85
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8828571428571429
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9228571428571428
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7114285714285714
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2833333333333333
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17657142857142855
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09228571428571428
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7114285714285714
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.85
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8828571428571429
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9228571428571428
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.820942296767774
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7878956916099771
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7915593121031292
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: basline 256
          type: basline_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.7057142857142857
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8414285714285714
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.88
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9228571428571428
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7057142857142857
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.28047619047619043
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.176
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09228571428571428
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7057142857142857
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8414285714285714
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.88
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9228571428571428
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8161680075424235
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7817953514739227
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.785367816349997
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: basline 128
          type: basline_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.7028571428571428
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8342857142857143
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8742857142857143
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9171428571428571
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7028571428571428
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.27809523809523806
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17485714285714282
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09171428571428569
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7028571428571428
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8342857142857143
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8742857142857143
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9171428571428571
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8109319521599055
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7768752834467119
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7802736634060462
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: basline 64
          type: basline_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.6728571428571428
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8171428571428572
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8614285714285714
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9014285714285715
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6728571428571428
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2723809523809524
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17228571428571426
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09014285714285714
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6728571428571428
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8171428571428572
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8614285714285714
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9014285714285715
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7900026049536226
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7539795918367346
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7582240178397145
            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("philschmid/bge-base-financial-matryoshka")
# Run inference
sentences = [
    "What was Gilead's total revenue in 2023?",
    'What was the total revenue for the year ended December 31, 2023?',
    'How much was the impairment related to the CAT loan receivable in 2023?',
]
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: basline_768
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7086
cosine_accuracy@3	0.8514
cosine_accuracy@5	0.8843
cosine_accuracy@10	0.9271
cosine_precision@1	0.7086
cosine_precision@3	0.2838
cosine_precision@5	0.1769
cosine_precision@10	0.0927
cosine_recall@1	0.7086
cosine_recall@3	0.8514
cosine_recall@5	0.8843
cosine_recall@10	0.9271
cosine_ndcg@10	0.8215
cosine_mrr@10	0.7874
cosine_map@100	0.7907

Information Retrieval

Dataset: basline_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7114
cosine_accuracy@3	0.85
cosine_accuracy@5	0.8829
cosine_accuracy@10	0.9229
cosine_precision@1	0.7114
cosine_precision@3	0.2833
cosine_precision@5	0.1766
cosine_precision@10	0.0923
cosine_recall@1	0.7114
cosine_recall@3	0.85
cosine_recall@5	0.8829
cosine_recall@10	0.9229
cosine_ndcg@10	0.8209
cosine_mrr@10	0.7879
cosine_map@100	0.7916

Information Retrieval

Dataset: basline_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7057
cosine_accuracy@3	0.8414
cosine_accuracy@5	0.88
cosine_accuracy@10	0.9229
cosine_precision@1	0.7057
cosine_precision@3	0.2805
cosine_precision@5	0.176
cosine_precision@10	0.0923
cosine_recall@1	0.7057
cosine_recall@3	0.8414
cosine_recall@5	0.88
cosine_recall@10	0.9229
cosine_ndcg@10	0.8162
cosine_mrr@10	0.7818
cosine_map@100	0.7854

Information Retrieval

Dataset: basline_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7029
cosine_accuracy@3	0.8343
cosine_accuracy@5	0.8743
cosine_accuracy@10	0.9171
cosine_precision@1	0.7029
cosine_precision@3	0.2781
cosine_precision@5	0.1749
cosine_precision@10	0.0917
cosine_recall@1	0.7029
cosine_recall@3	0.8343
cosine_recall@5	0.8743
cosine_recall@10	0.9171
cosine_ndcg@10	0.8109
cosine_mrr@10	0.7769
cosine_map@100	0.7803

Information Retrieval

Dataset: basline_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6729
cosine_accuracy@3	0.8171
cosine_accuracy@5	0.8614
cosine_accuracy@10	0.9014
cosine_precision@1	0.6729
cosine_precision@3	0.2724
cosine_precision@5	0.1723
cosine_precision@10	0.0901
cosine_recall@1	0.6729
cosine_recall@3	0.8171
cosine_recall@5	0.8614
cosine_recall@10	0.9014
cosine_ndcg@10	0.79
cosine_mrr@10	0.754
cosine_map@100	0.7582

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: 46.11 tokens
max: 289 tokens

min: 7 tokens
mean: 20.26 tokens
max: 43 tokens

	positive	anchor
type	string	string
details	min: 10 tokens mean: 46.11 tokens max: 289 tokens	min: 7 tokens mean: 20.26 tokens max: 43 tokens

Samples:

positive	anchor
`Fiscal 2023 total gross profit margin of 35.1% represents an increase of 1.7 percentage points as compared to the respective prior year period.`	`What was the total gross profit margin for Hewlett Packard Enterprise in fiscal 2023?`
`Noninterest expense increased to $65.8 billion in 2023, primarily due to higher investments in people and technology and higher FDIC expense, including $2.1 billion for the estimated special assessment amount arising from the closure of Silicon Valley Bank and Signature Bank.`	`What was the total noninterest expense for the company in 2023?`
`As of May 31, 2022, FedEx Office had approximately 12,000 employees.`	`How many employees did FedEx Office have as of May 31, 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: 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
sanity_evaluation: False
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	basline_128_cosine_map@100	basline_256_cosine_map@100	basline_512_cosine_map@100	basline_64_cosine_map@100	basline_768_cosine_map@100
0.8122	10	1.5259	-	-	-	-	-
0.9746	12	-	0.7502	0.7737	0.7827	0.7185	0.7806
1.6244	20	0.6545	-	-	-	-	-
1.9492	24	-	0.7689	0.7844	0.7869	0.7447	0.7909
2.4365	30	0.4784	-	-	-	-	-
2.9239	36	-	0.7733	0.7916	0.7904	0.7491	0.7930
3.2487	40	0.3827	-	-	-	-	-
3.8985	48	-	0.7739	0.7907	0.7900	0.7479	0.7948
0.8122	10	0.2685	-	-	-	-	-
0.9746	12	-	0.7779	0.7932	0.7948	0.7517	0.7943
1.6244	20	0.183	-	-	-	-	-
1.9492	24	-	0.7784	0.7929	0.7963	0.7575	0.7957
2.4365	30	0.1877	-	-	-	-	-
2.9239	36	-	0.7814	0.7914	0.7992	0.7570	0.7974
3.2487	40	0.1826	-	-	-	-	-
3.8985	48	-	0.7818	0.7916	0.7976	0.7580	0.7960
0.8122	10	0.071	-	-	-	-	-
0.9746	12	-	0.7810	0.7935	0.7954	0.7550	0.7949
1.6244	20	0.0629	-	-	-	-	-
1.9492	24	-	0.7855	0.7914	0.7989	0.7559	0.7981
2.4365	30	0.0827	-	-	-	-	-
2.9239	36	-	0.7893	0.7927	0.7987	0.7539	0.7961
3.2487	40	0.1003	-	-	-	-	-
3.8985	48	-	0.7903	0.7915	0.7980	0.7530	0.7951
0.8122	10	0.0213	-	-	-	-	-
0.9746	12	-	0.7786	0.7869	0.7885	0.7566	0.7908
1.6244	20	0.0234	-	-	-	-	-
1.9492	24	-	0.783	0.7882	0.793	0.7551	0.7946
2.4365	30	0.0357	-	-	-	-	-
2.9239	36	-	0.7838	0.7892	0.7922	0.7579	0.7907
3.2487	40	0.0563	-	-	-	-	-
3.8985	48	-	0.7846	0.7887	0.7912	0.7582	0.7901
0.8122	10	0.0075	-	-	-	-	-
0.9746	12	-	0.7730	0.7816	0.7818	0.7550	0.7868
1.6244	20	0.01	-	-	-	-	-
1.9492	24	-	0.7827	0.785	0.7896	0.7551	0.7915
2.4365	30	0.0154	-	-	-	-	-
2.9239	36	-	0.7808	0.7838	0.7921	0.7584	0.7916
3.2487	40	0.0312	-	-	-	-	-
3.8985	48	-	0.7803	0.7854	0.7916	0.7582	0.7907

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.13
Sentence Transformers: 3.0.0
Transformers: 4.42.0.dev0
PyTorch: 2.1.2+cu121
Accelerate: 0.29.2
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}
}