--- language: - en tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:14737 - loss:MultipleNegativesRankingLoss base_model: BAAI/bge-large-en-v1.5 widget: - source_sentence: >- Represent this sentence for searching relevant passages: What are some best practices for ensuring images in horizontal cards are visually appealing despite being cropped to fit a square format? sentences: - > Tree view Usage guidelines Horizontal scrolling: If you have a layout that doesn't allow for users to adjust the width of the container for a tree view, allow them to horizontally scroll in order to see the full depth of the hierarchy. Do: Allow horizontal scrolling in a fixed layout. - >- Cards Options Vertical or horizontal : Standard cards can be laid out vertically (components are organized in a column) or horizontally (components are organized in a row). Horizontal cards always have a square preview, and the image is cropped to fit inside the square. These can only be laid out in a tile grid where every card is the same size. - >- Alert dialog Behaviors Button group overflow: An alert dialog can have up to 3 buttons. When horizontal space is limited, button groups stack vertically. They should appear in ascending order based on importance, with the most critical action at the bottom. - source_sentence: >- Represent this sentence for searching relevant passages: Are there any guidelines for the timing and smoothness of the fading effect when hovering over a segment in a donut chart? sentences: - >- Color for data visualization Usage guidelines Categorical colors are not ordered. Use these for categorical scales. Do not use these for ordinal, interval, or ratio scales. Sequential colors are ordered. Use these for ordinal and interval scales. It’s also acceptable to use these for ratio scales. Do not use these for categorical scales. Diverging colors are ordered. Use these for ordinal and ratio scales, especially when there is a meaningful middle value. These may also be used for interval scales. Do not use these for categorical scales. - >- Action group Options Density: Action groups come in 2 densities: regular and compact. The compact density retains the same font and icon sizes, but has tighter spacing. The action buttons also become connected for non-quiet action groups. - >- Donut chart Behaviors Hover: Hovering over a segment of a donut chart causes all other segments to fade back from the view. A tooltip displays the segment name, percentage of total, and metric value. - source_sentence: >- Represent this sentence for searching relevant passages: Why is it important to orient the legend to match the chart whenever possible? sentences: - >- Breadcrumbs Options Multiline: The multiline variation places emphasis on the selected breadcrumb item as a page title, helping a user to more clearly identify their current location. - >- Cards Layout Card width: Cards are laid out in either a fluid card grid or have fixed widths. Most cards can be organized within a grid where the width of each card is fluid depending on the nature of the grid. In rare cases where cards can’t be laid out in a card grid, they’ll have a fixed width that is defined manually. - >- Legend Options Orientation: Legends can have horizontal or vertical orientation. Whenever possible, orient the legend to match the chart. - source_sentence: >- Represent this sentence for searching relevant passages: What is the primary use case for radio buttons according to the Adobe Spectrum Design Documentation? sentences: - >+ Radio group Usage guidelines Use radio buttons for mutually exclusive options: Radio buttons and [checkboxes](/page/checkbox) are not interchangeable. Radio buttons are best used for selecting a single option from a list of mutually exclusive options. Checkboxes are best used for selecting multiple options at once (or no options). - > Additional resources: - [Human Interface Guidelines: iOS Tab Bars](https://developer.apple.com/design/human-interface-guidelines/ios/bars/tab-bars/) - [Human Interface Guidelines: Accessibility](https://developer.apple.com/design/human-interface-guidelines/accessibility/overview/introduction/) - >- Picker Options Label position: Labels can be placed either on top or on the side. Top labels are the default and are recommended because they work better with long copy, localization, and responsive layouts. Side labels are most useful when vertical space is limited. - source_sentence: >- Represent this sentence for searching relevant passages: How can a designer balance the need for clear text links and the need for emphasized text in a user interface? sentences: - >- Meter Options Positive variant: The positive variant has a green fill to show the value. This can be used to represent a positive semantic value, such as when there’s a lot of space remaining. - >- Badge Options Size: Badges come in four different sizes: small, medium, large, and extra-large. The small size is the default and most frequently used option. Use the other sizes sparingly to create a hierarchy of importance on a page. - >+ Typography Usage guidelines Don't use underlines for adding emphasis: Underlines are reserved for text links only. They should not be used as a way for adding emphasis to words. datasets: - JianLiao/spectrum-design-docs pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 model-index: - name: SentenceTransformer based on BAAI/bge-large-en-v1.5 results: - task: type: information-retrieval name: Information Retrieval dataset: name: sds type: sds metrics: - type: cosine_accuracy@1 value: 0.007462686567164179 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.015603799185888738 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.04748982360922659 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.7815468113975577 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.007462686567164179 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.005201266395296246 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.009497964721845319 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.07815468113975575 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.007462686567164179 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.015603799185888738 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.04748982360922659 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.7815468113975577 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.25440066233238845 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.10778547737502948 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.11639203259428242 name: Cosine Map@100 license: mit --- # SentenceTransformer based on BAAI/bge-large-en-v1.5 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) on the [spectrum-design-docs](https://huggingface.co/datasets/JianLiao/spectrum-design-docs) dataset. It maps sentences & paragraphs to a 1024-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-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 1024 dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - [spectrum-design-docs](https://huggingface.co/datasets/JianLiao/spectrum-design-docs) - **Language:** en ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, '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: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("JianLiao/spectrum-doc-fine-tuned") # Run inference sentences = [ 'Represent this sentence for searching relevant passages: How can a designer balance the need for clear text links and the need for emphasized text in a user interface?', "Typography\nUsage guidelines\nDon't use underlines for adding emphasis: Underlines are reserved for text links only. They should not be used as a way for adding emphasis to words.\n\n", 'Meter\nOptions\nPositive variant: The positive variant has a green fill to show the value. This can be used to represent a positive semantic value, such as when there’s a lot of space remaining.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Information Retrieval * Dataset: `sds` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.0075 | | cosine_accuracy@3 | 0.0156 | | cosine_accuracy@5 | 0.0475 | | cosine_accuracy@10 | 0.7815 | | cosine_precision@1 | 0.0075 | | cosine_precision@3 | 0.0052 | | cosine_precision@5 | 0.0095 | | cosine_precision@10 | 0.0782 | | cosine_recall@1 | 0.0075 | | cosine_recall@3 | 0.0156 | | cosine_recall@5 | 0.0475 | | cosine_recall@10 | 0.7815 | | **cosine_ndcg@10** | **0.2544** | | cosine_mrr@10 | 0.1078 | | cosine_map@100 | 0.1164 | ## Training Details ### Training Dataset #### spectrum-design-docs * Dataset: [spectrum-design-docs](https://huggingface.co/datasets/JianLiao/spectrum-design-docs) at [23f5565](https://huggingface.co/datasets/JianLiao/spectrum-design-docs/tree/23f5565f9fc1cfe31d1245ca9e5368f00fcaec00) * Size: 14,737 training samples * Columns: anchor and positive * Approximate statistics based on the first 1000 samples: | | anchor | positive | |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | anchor | positive | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Represent this sentence for searching relevant passages: Are there any specific guidelines or best practices provided by the Spectrum team for integrating Spectrum CSS into a new or existing project? | Spectrum CSS: An open source CSS-only implementation of Spectrum, maintained by the Spectrum team.
Dependency chain: Spectrum DNA → Spectrum CSS


[GitHub repository](https://github.com/adobe/spectrum-css/)
[Website](https://opensource.adobe.com/spectrum-css/)
[#spectrum_css](https://adobe.slack.com/archives/C5N154FEY) | | Represent this sentence for searching relevant passages: How does the default setting for progress circles affect their behavior in a UI? | Progress circle
Options
Indeterminate: A progress circle can be either determinate or indeterminate. By default, progress circles are determinate. Use a determinate progress circle when progress can be calculated against a specific goal (e.g., downloading a file of a known size). Use an indeterminate progress circle when progress is happening but the time or effort to completion can’t be determined (e.g., attempting to reconnect to a server). | | Represent this sentence for searching relevant passages: What tools or methods can designers use to test the effectiveness of wrapped legends in their designs? | Legend
Behaviors
Wrapping: When there isn’t enough space, wrap legends to ensure that dimension values are shown. | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_train_batch_size`: 22 - `per_device_eval_batch_size`: 16 - `gradient_accumulation_steps`: 16 - `learning_rate`: 2e-05 - `num_train_epochs`: 100 - `lr_scheduler_type`: cosine - `warmup_ratio`: 0.1 - `bf16`: True - `tf32`: True - `load_best_model_at_end`: True - `optim`: adamw_torch_fused - `prompts`: {'anchor': 'Represent this sentence for searching relevant passages: '} - `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`: 22 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 16 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 100 - `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`: True - `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`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `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 - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: {'anchor': 'Represent this sentence for searching relevant passages: '} - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs
Click to expand | Epoch | Step | Training Loss | sds_cosine_ndcg@10 | |:--------:|:-------:|:-------------:|:------------------:| | 1.0 | 7 | - | 0.2255 | | 1.48 | 10 | 0.2646 | - | | 2.0 | 14 | - | 0.2282 | | 2.96 | 20 | 0.1412 | - | | 3.0 | 21 | - | 0.2358 | | 4.0 | 28 | - | 0.2397 | | 4.32 | 30 | 0.0638 | - | | 5.0 | 35 | - | 0.2430 | | 5.8 | 40 | 0.0425 | - | | 6.0 | 42 | - | 0.2449 | | 7.0 | 49 | - | 0.2462 | | 7.16 | 50 | 0.0237 | - | | 8.0 | 56 | - | 0.2428 | | 8.64 | 60 | 0.015 | - | | 9.0 | 63 | - | 0.2456 | | 10.0 | 70 | 0.0082 | 0.2456 | | 11.0 | 77 | - | 0.2498 | | 11.48 | 80 | 0.0052 | - | | 12.0 | 84 | - | 0.2474 | | 12.96 | 90 | 0.0035 | - | | 13.0 | 91 | - | 0.2455 | | 14.0 | 98 | - | 0.2475 | | 14.32 | 100 | 0.0022 | - | | 15.0 | 105 | - | 0.2472 | | 15.8 | 110 | 0.002 | - | | 16.0 | 112 | - | 0.2486 | | 17.0 | 119 | - | 0.2506 | | 17.16 | 120 | 0.0015 | - | | 18.0 | 126 | - | 0.2490 | | 18.64 | 130 | 0.0013 | - | | 19.0 | 133 | - | 0.2489 | | 20.0 | 140 | 0.0012 | 0.2491 | | 21.0 | 147 | - | 0.2493 | | 21.48 | 150 | 0.0011 | - | | 22.0 | 154 | - | 0.2487 | | 22.96 | 160 | 0.001 | - | | 23.0 | 161 | - | 0.2486 | | 24.0 | 168 | - | 0.2490 | | 24.32 | 170 | 0.0008 | - | | 25.0 | 175 | - | 0.2502 | | 25.8 | 180 | 0.0008 | - | | 26.0 | 182 | - | 0.2505 | | 27.0 | 189 | - | 0.2523 | | 27.16 | 190 | 0.0008 | - | | 28.0 | 196 | - | 0.2516 | | 28.64 | 200 | 0.0007 | - | | 29.0 | 203 | - | 0.2509 | | 30.0 | 210 | 0.0007 | 0.2522 | | 31.0 | 217 | - | 0.2522 | | 31.48 | 220 | 0.0006 | - | | 32.0 | 224 | - | 0.2534 | | 32.96 | 230 | 0.0007 | - | | 33.0 | 231 | - | 0.2523 | | 34.0 | 238 | - | 0.2524 | | 34.32 | 240 | 0.0006 | - | | 35.0 | 245 | - | 0.2518 | | 35.8 | 250 | 0.0006 | - | | 36.0 | 252 | - | 0.2529 | | 37.0 | 259 | - | 0.2524 | | 37.16 | 260 | 0.0006 | - | | 38.0 | 266 | - | 0.2530 | | 38.64 | 270 | 0.0005 | - | | 39.0 | 273 | - | 0.2526 | | 40.0 | 280 | 0.0006 | 0.2539 | | 41.0 | 287 | - | 0.2529 | | 41.48 | 290 | 0.0005 | - | | 42.0 | 294 | - | 0.2545 | | 42.96 | 300 | 0.0006 | - | | 43.0 | 301 | - | 0.2534 | | 44.0 | 308 | - | 0.2536 | | 44.32 | 310 | 0.0004 | - | | 45.0 | 315 | - | 0.2521 | | 45.8 | 320 | 0.0005 | - | | 46.0 | 322 | - | 0.2532 | | 47.0 | 329 | - | 0.2519 | | 47.16 | 330 | 0.0005 | - | | 48.0 | 336 | - | 0.2525 | | 48.64 | 340 | 0.0004 | - | | 49.0 | 343 | - | 0.2535 | | 50.0 | 350 | 0.0005 | 0.2542 | | 51.0 | 357 | - | 0.2540 | | 51.48 | 360 | 0.0004 | - | | 52.0 | 364 | - | 0.2542 | | 52.96 | 370 | 0.0005 | - | | 53.0 | 371 | - | 0.2538 | | 54.0 | 378 | - | 0.2533 | | 54.32 | 380 | 0.0004 | - | | 55.0 | 385 | - | 0.2544 | | 55.8 | 390 | 0.0004 | - | | 56.0 | 392 | - | 0.2539 | | 57.0 | 399 | - | 0.2541 | | 57.16 | 400 | 0.0005 | - | | 58.0 | 406 | - | 0.2532 | | 58.64 | 410 | 0.0004 | - | | 59.0 | 413 | - | 0.2543 | | 60.0 | 420 | 0.0004 | 0.2532 | | 61.0 | 427 | - | 0.2541 | | 61.48 | 430 | 0.0004 | - | | 62.0 | 434 | - | 0.2542 | | 62.96 | 440 | 0.0005 | - | | 63.0 | 441 | - | 0.2546 | | 64.0 | 448 | - | 0.2549 | | 64.32 | 450 | 0.0003 | - | | **65.0** | **455** | **-** | **0.2557** | | 65.8 | 460 | 0.0004 | - | | 66.0 | 462 | - | 0.2557 | | 67.0 | 469 | - | 0.2539 | | 67.16 | 470 | 0.0004 | - | | 68.0 | 476 | - | 0.2538 | | 68.64 | 480 | 0.0004 | - | | 69.0 | 483 | - | 0.2538 | | 70.0 | 490 | 0.0004 | 0.2542 | | 71.0 | 497 | - | 0.2532 | | 71.48 | 500 | 0.0004 | - | | 72.0 | 504 | - | 0.2538 | | 72.96 | 510 | 0.0004 | - | | 73.0 | 511 | - | 0.2545 | | 74.0 | 518 | - | 0.2531 | | 74.32 | 520 | 0.0003 | - | | 75.0 | 525 | - | 0.2534 | | 75.8 | 530 | 0.0004 | - | | 76.0 | 532 | - | 0.2541 | | 77.0 | 539 | - | 0.2545 | | 77.16 | 540 | 0.0004 | - | | 78.0 | 546 | - | 0.2536 | | 78.64 | 550 | 0.0004 | - | | 79.0 | 553 | - | 0.2545 | | 80.0 | 560 | 0.0004 | 0.2540 | | 81.0 | 567 | - | 0.2545 | | 81.48 | 570 | 0.0004 | - | | 82.0 | 574 | - | 0.2541 | | 82.96 | 580 | 0.0004 | - | | 83.0 | 581 | - | 0.2545 | | 84.0 | 588 | - | 0.2538 | | 84.32 | 590 | 0.0004 | - | | 85.0 | 595 | - | 0.2546 | | 85.8 | 600 | 0.0004 | 0.2544 | * The bold row denotes the saved checkpoint.
### Framework Versions - Python: 3.12.8 - Sentence Transformers: 3.3.1 - Transformers: 4.47.1 - PyTorch: 2.5.1+cu124 - Accelerate: 1.2.1 - Datasets: 3.2.0 - Tokenizers: 0.21.0 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @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", } ``` #### MultipleNegativesRankingLoss ```bibtex @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} } ```