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---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:10000
- loss:MultipleNegativesRankingLoss
base_model: openai/clip-vit-large-patch14
widget:
- source_sentence: A man standing next to a little girl riding a horse.
sentences:
- The woman is working on her computer at the desk.
- A young man holding an umbrella next to a herd of cattle.
- 'a person sitting at a desk with a keyboard and monitor '
- source_sentence: 'A car at an intersection while a man is crossing the street. '
sentences:
- A plane that is flying in the air.
- a small girl sitting on a chair holding a white bear
- A young toddler walks across the grass in a park.
- source_sentence: A lady riding her bicycle on the side of a street.
sentences:
- Flowers hang from a small decorative post in a yard.
- Flowers in a clear vase sitting on a table.
- The toilet is near the door in the bathroom.
- source_sentence: 'A group of zebras standing beside each other in the desert. '
sentences:
- The bathroom is clean and ready for us to use.
- A woman throwing a frisbee as a child looks on.
- a bird with a pink eye is sitting on a branch in the woods.
- source_sentence: A large desk by a window is neatly arranged.
sentences:
- An old toilet sits in dirt with a helmet on top.
- A lady sitting at an enormous dining table with lots of food.
- A long hot dog on a plate on a table.
datasets:
- jxie/coco_captions
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
co2_eq_emissions:
emissions: 11.59757010411656
energy_consumed: 0.04333563796741882
source: codecarbon
training_type: fine-tuning
on_cloud: false
cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
ram_total_size: 31.777088165283203
hours_used: 0.137
hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: CLIP ViT-L/14 model trained on COCO Captions
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: coco eval
type: coco-eval
metrics:
- type: cosine_accuracy@1
value: 0.799
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.968
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.991
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.995
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.799
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.3226666666666666
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.19820000000000004
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09950000000000002
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.799
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.968
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.991
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.995
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9112246370033859
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8827011904761911
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8828050771692076
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: coco test
type: coco-test
metrics:
- type: cosine_accuracy@1
value: 0.776
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.959
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.986
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.995
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.776
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.31966666666666665
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.19720000000000004
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09950000000000003
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.776
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.959
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.986
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.995
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8996790966052481
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8674440476190487
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8678233839689722
name: Cosine Map@100
---
# CLIP ViT-L/14 model trained on COCO Captions
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [openai/clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) on the [coco_captions](https://huggingface.co/datasets/jxie/coco_captions) dataset. It maps sentences & paragraphs to a None-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:** [openai/clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) <!-- at revision 32bd64288804d66eefd0ccbe215aa642df71cc41 -->
- **Maximum Sequence Length:** None tokens
- **Output Dimensionality:** None dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [coco_captions](https://huggingface.co/datasets/jxie/coco_captions)
- **Language:** en
- **License:** apache-2.0
### 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({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'get_text_features', 'method_output_name': None}, 'image': {'method': 'get_image_features', 'method_output_name': None}}, 'module_output_name': 'sentence_embedding', 'architecture': 'CLIPModel'})
)
```
## 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("tomaarsen/clip-vit-L14-coco")
# Run inference
sentences = [
'A large desk by a window is neatly arranged.',
'A long hot dog on a plate on a table.',
'A lady sitting at an enormous dining table with lots of food.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000, -0.0302, 0.1619],
# [-0.0302, 1.0000, 0.1578],
# [ 0.1619, 0.1578, 1.0000]])
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
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### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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## Evaluation
### Metrics
#### Information Retrieval
* Datasets: `coco-eval` and `coco-test`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | coco-eval | coco-test |
|:--------------------|:-----------|:-----------|
| cosine_accuracy@1 | 0.799 | 0.776 |
| cosine_accuracy@3 | 0.968 | 0.959 |
| cosine_accuracy@5 | 0.991 | 0.986 |
| cosine_accuracy@10 | 0.995 | 0.995 |
| cosine_precision@1 | 0.799 | 0.776 |
| cosine_precision@3 | 0.3227 | 0.3197 |
| cosine_precision@5 | 0.1982 | 0.1972 |
| cosine_precision@10 | 0.0995 | 0.0995 |
| cosine_recall@1 | 0.799 | 0.776 |
| cosine_recall@3 | 0.968 | 0.959 |
| cosine_recall@5 | 0.991 | 0.986 |
| cosine_recall@10 | 0.995 | 0.995 |
| **cosine_ndcg@10** | **0.9112** | **0.8997** |
| cosine_mrr@10 | 0.8827 | 0.8674 |
| cosine_map@100 | 0.8828 | 0.8678 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### coco_captions
* Dataset: [coco_captions](https://huggingface.co/datasets/jxie/coco_captions) at [a2ed90d](https://huggingface.co/datasets/jxie/coco_captions/tree/a2ed90d49b61dd13dd71f399c70f5feb897f8bec)
* Size: 10,000 training samples
* Columns: <code>image</code> and <code>caption</code>
* Approximate statistics based on the first 1000 samples:
| | image | caption |
|:--------|:----------------------------------|:------------------------------------------------------------------------------------------------|
| type | PIL.JpegImagePlugin.JpegImageFile | string |
| details | <ul><li></li></ul> | <ul><li>min: 28 characters</li><li>mean: 52.56 characters</li><li>max: 156 characters</li></ul> |
* Samples:
| image | caption |
|:----------------------------------------------------------------------------------------------|:--------------------------------------------------------------------|
| <code><PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480 at 0x1E8E850FDD0></code> | <code>A woman wearing a net on her head cutting a cake. </code> |
| <code><PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480 at 0x1E8E8550590></code> | <code>A woman cutting a large white sheet cake.</code> |
| <code><PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480 at 0x1E8E8563F50></code> | <code>A woman wearing a hair net cutting a large sheet cake.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"gather_across_devices": false
}
```
### Evaluation Dataset
#### coco_captions
* Dataset: [coco_captions](https://huggingface.co/datasets/jxie/coco_captions) at [a2ed90d](https://huggingface.co/datasets/jxie/coco_captions/tree/a2ed90d49b61dd13dd71f399c70f5feb897f8bec)
* Size: 1,000 evaluation samples
* Columns: <code>image</code> and <code>caption</code>
* Approximate statistics based on the first 1000 samples:
| | image | caption |
|:--------|:----------------------------------|:------------------------------------------------------------------------------------------------|
| type | PIL.JpegImagePlugin.JpegImageFile | string |
| details | <ul><li></li></ul> | <ul><li>min: 27 characters</li><li>mean: 52.45 characters</li><li>max: 151 characters</li></ul> |
* Samples:
| image | caption |
|:----------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| <code><PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x336 at 0x1E8E82F5710></code> | <code>A child holding a flowered umbrella and petting a yak.</code> |
| <code><PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x336 at 0x1E8E8532B10></code> | <code>A young man holding an umbrella next to a herd of cattle.</code> |
| <code><PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x336 at 0x1E8E833CFD0></code> | <code>a young boy barefoot holding an umbrella touching the horn of a cow</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"gather_across_devices": false
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `bf16`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 1
- `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`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `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
- `use_cpu`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `bf16`: True
- `fp16`: False
- `half_precision_backend`: None
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `parallelism_config`: None
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `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
- `hub_revision`: None
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: no
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: True
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | coco-eval_cosine_ndcg@10 | coco-test_cosine_ndcg@10 |
|:------:|:----:|:-------------:|:---------------:|:------------------------:|:------------------------:|
| -1 | -1 | - | - | 0.8902 | - |
| 0.0112 | 7 | 0.4782 | - | - | - |
| 0.0224 | 14 | 0.3108 | - | - | - |
| 0.0336 | 21 | 0.2212 | - | - | - |
| 0.0448 | 28 | 0.1612 | - | - | - |
| 0.056 | 35 | 0.1853 | - | - | - |
| 0.0672 | 42 | 0.0811 | - | - | - |
| 0.0784 | 49 | 0.0785 | - | - | - |
| 0.0896 | 56 | 0.1022 | - | - | - |
| 0.1008 | 63 | 0.0927 | 0.1433 | 0.9189 | - |
| 0.112 | 70 | 0.112 | - | - | - |
| 0.1232 | 77 | 0.1072 | - | - | - |
| 0.1344 | 84 | 0.1272 | - | - | - |
| 0.1456 | 91 | 0.1176 | - | - | - |
| 0.1568 | 98 | 0.1361 | - | - | - |
| 0.168 | 105 | 0.1281 | - | - | - |
| 0.1792 | 112 | 0.0961 | - | - | - |
| 0.1904 | 119 | 0.1038 | - | - | - |
| 0.2016 | 126 | 0.1019 | 0.1506 | 0.8929 | - |
| 0.2128 | 133 | 0.0657 | - | - | - |
| 0.224 | 140 | 0.1187 | - | - | - |
| 0.2352 | 147 | 0.0752 | - | - | - |
| 0.2464 | 154 | 0.2314 | - | - | - |
| 0.2576 | 161 | 0.0806 | - | - | - |
| 0.2688 | 168 | 0.1243 | - | - | - |
| 0.28 | 175 | 0.1179 | - | - | - |
| 0.2912 | 182 | 0.1174 | - | - | - |
| 0.3024 | 189 | 0.0926 | 0.1604 | 0.8907 | - |
| 0.3136 | 196 | 0.1327 | - | - | - |
| 0.3248 | 203 | 0.0861 | - | - | - |
| 0.336 | 210 | 0.0677 | - | - | - |
| 0.3472 | 217 | 0.1296 | - | - | - |
| 0.3584 | 224 | 0.1322 | - | - | - |
| 0.3696 | 231 | 0.1555 | - | - | - |
| 0.3808 | 238 | 0.0807 | - | - | - |
| 0.392 | 245 | 0.1134 | - | - | - |
| 0.4032 | 252 | 0.1826 | 0.1712 | 0.8840 | - |
| 0.4144 | 259 | 0.1796 | - | - | - |
| 0.4256 | 266 | 0.186 | - | - | - |
| 0.4368 | 273 | 0.0971 | - | - | - |
| 0.448 | 280 | 0.063 | - | - | - |
| 0.4592 | 287 | 0.1344 | - | - | - |
| 0.4704 | 294 | 0.072 | - | - | - |
| 0.4816 | 301 | 0.1233 | - | - | - |
| 0.4928 | 308 | 0.1152 | - | - | - |
| 0.504 | 315 | 0.148 | 0.1565 | 0.8960 | - |
| 0.5152 | 322 | 0.0836 | - | - | - |
| 0.5264 | 329 | 0.1171 | - | - | - |
| 0.5376 | 336 | 0.1433 | - | - | - |
| 0.5488 | 343 | 0.0494 | - | - | - |
| 0.56 | 350 | 0.1533 | - | - | - |
| 0.5712 | 357 | 0.0773 | - | - | - |
| 0.5824 | 364 | 0.0921 | - | - | - |
| 0.5936 | 371 | 0.0546 | - | - | - |
| 0.6048 | 378 | 0.1444 | 0.1496 | 0.9001 | - |
| 0.616 | 385 | 0.0956 | - | - | - |
| 0.6272 | 392 | 0.0445 | - | - | - |
| 0.6384 | 399 | 0.0939 | - | - | - |
| 0.6496 | 406 | 0.1109 | - | - | - |
| 0.6608 | 413 | 0.0466 | - | - | - |
| 0.672 | 420 | 0.0627 | - | - | - |
| 0.6832 | 427 | 0.0857 | - | - | - |
| 0.6944 | 434 | 0.058 | - | - | - |
| 0.7056 | 441 | 0.1542 | 0.1443 | 0.9031 | - |
| 0.7168 | 448 | 0.0972 | - | - | - |
| 0.728 | 455 | 0.0892 | - | - | - |
| 0.7392 | 462 | 0.0819 | - | - | - |
| 0.7504 | 469 | 0.0838 | - | - | - |
| 0.7616 | 476 | 0.0754 | - | - | - |
| 0.7728 | 483 | 0.0754 | - | - | - |
| 0.784 | 490 | 0.0638 | - | - | - |
| 0.7952 | 497 | 0.1006 | - | - | - |
| 0.8064 | 504 | 0.0398 | 0.1429 | 0.9122 | - |
| 0.8176 | 511 | 0.1562 | - | - | - |
| 0.8288 | 518 | 0.1039 | - | - | - |
| 0.84 | 525 | 0.0342 | - | - | - |
| 0.8512 | 532 | 0.0467 | - | - | - |
| 0.8624 | 539 | 0.0703 | - | - | - |
| 0.8736 | 546 | 0.0655 | - | - | - |
| 0.8848 | 553 | 0.0216 | - | - | - |
| 0.896 | 560 | 0.029 | - | - | - |
| 0.9072 | 567 | 0.0588 | 0.1530 | 0.9112 | - |
| 0.9184 | 574 | 0.1145 | - | - | - |
| 0.9296 | 581 | 0.0652 | - | - | - |
| 0.9408 | 588 | 0.0556 | - | - | - |
| 0.952 | 595 | 0.0458 | - | - | - |
| 0.9632 | 602 | 0.0085 | - | - | - |
| 0.9744 | 609 | 0.0572 | - | - | - |
| 0.9856 | 616 | 0.0942 | - | - | - |
| 0.9968 | 623 | 0.109 | - | - | - |
| -1 | -1 | - | - | - | 0.8997 |
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.043 kWh
- **Carbon Emitted**: 0.012 kg of CO2
- **Hours Used**: 0.137 hours
### Training Hardware
- **On Cloud**: No
- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
- **RAM Size**: 31.78 GB
### Framework Versions
- Python: 3.11.6
- Sentence Transformers: 5.2.0.dev0
- Transformers: 4.57.0.dev0
- PyTorch: 2.8.0+cu128
- Accelerate: 1.6.0
- Datasets: 3.6.0
- Tokenizers: 0.22.1
## 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}
}
```
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