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	---
	language:
	- en
	library_name: sentence-transformers
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- loss:AdaptiveLayerLoss
	- loss:MultipleNegativesRankingLoss
	base_model: distilbert/distilroberta-base
	metrics:
	- pearson_cosine
	- spearman_cosine
	- pearson_manhattan
	- spearman_manhattan
	- pearson_euclidean
	- spearman_euclidean
	- pearson_dot
	- spearman_dot
	- pearson_max
	- spearman_max
	widget:
	- source_sentence: Certainly.
	sentences:
	- '''Of course.'''
	- The idea is a good one.
	- the woman is asleep at home
	- source_sentence: He walked.
	sentences:
	- The man was walking.
	- The people are running.
	- The women are making pizza.
	- source_sentence: Double pig.
	sentences:
	- Ah, triple pig!
	- He had no real answer.
	- Do you not know?
	- source_sentence: Very simply.
	sentences:
	- Not complicatedly.
	- People are on a beach.
	- The man kicks the umpire.
	- source_sentence: Introduction
	sentences:
	- Analytical Perspectives.
	- A man reads the paper.
	- No one wanted Singapore.
	pipeline_tag: sentence-similarity
	co2_eq_emissions:
	emissions: 94.69690706493431
	energy_consumed: 0.24362341090329948
	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: 0.849
	hardware_used: 1 x NVIDIA GeForce RTX 3090
	model-index:
	- name: SentenceTransformer based on distilbert/distilroberta-base
	results:
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts dev
	type: sts-dev
	metrics:
	- type: pearson_cosine
	value: 0.845554152020916
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8486455482928023
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8475103134032791
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8505660318245544
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8494883021932786
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8526835635349959
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.7866563719943611
	name: Pearson Dot
	- type: spearman_dot
	value: 0.7816258810453734
	name: Spearman Dot
	- type: pearson_max
	value: 0.8494883021932786
	name: Pearson Max
	- type: spearman_max
	value: 0.8526835635349959
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test
	type: sts-test
	metrics:
	- type: pearson_cosine
	value: 0.8182808182081737
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8148039503538166
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8132463174874629
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8088248622918064
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8148200486691981
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8105059611031759
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.7499699563291125
	name: Pearson Dot
	- type: spearman_dot
	value: 0.7350068244681712
	name: Spearman Dot
	- type: pearson_max
	value: 0.8182808182081737
	name: Pearson Max
	- type: spearman_max
	value: 0.8148039503538166
	name: Spearman Max
	---

	# SentenceTransformer based on distilbert/distilroberta-base

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [distilbert/distilroberta-base](https://huggingface.co./distilbert/distilroberta-base) on the [sentence-transformers/all-nli](https://huggingface.co./datasets/sentence-transformers/all-nli) dataset. 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: [distilbert/distilroberta-base](https://huggingface.co./distilbert/distilroberta-base) <!-- at revision fb53ab8802853c8e4fbdbcd0529f21fc6f459b2b -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 768 tokens
	- Similarity Function: Cosine Similarity
	- Training Dataset:
	- [sentence-transformers/all-nli](https://huggingface.co./datasets/sentence-transformers/all-nli)
	- Language: en
	<!-- - License: Unknown -->

	### 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': False}) with Transformer model: RobertaModel
	(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
	)
	```

	## Usage

	### Direct Usage (Sentence Transformers)

	First install the Sentence Transformers library:

	```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/distilroberta-base-nli-adaptive-layer")
	# Run inference
	sentences = [
	'Introduction',
	'Analytical Perspectives.',
	'A man reads the paper.',
	]
	embeddings = model.encode(sentences)
	print(embeddings.shape)
	# [3, 768]

	# Get the similarity scores for the embeddings
	similarities = model.similarity(embeddings)
	print(similarities.shape)
	# [3, 3]
	```

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	### Downstream Usage (Sentence Transformers)

	You can finetune this model on your own dataset.

	<details><summary>Click to expand</summary>

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	## Evaluation

	### Metrics

	#### Semantic Similarity
	* Dataset: `sts-dev`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.8456 \|
	\| spearman_cosine \| 0.8486 \|
	\| pearson_manhattan \| 0.8475 \|
	\| spearman_manhattan \| 0.8506 \|
	\| pearson_euclidean \| 0.8495 \|
	\| spearman_euclidean \| 0.8527 \|
	\| pearson_dot \| 0.7867 \|
	\| spearman_dot \| 0.7816 \|
	\| pearson_max \| 0.8495 \|
	\| spearman_max \| 0.8527 \|

	#### Semantic Similarity
	* Dataset: `sts-test`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.8183 \|
	\| spearman_cosine \| 0.8148 \|
	\| pearson_manhattan \| 0.8132 \|
	\| spearman_manhattan \| 0.8088 \|
	\| pearson_euclidean \| 0.8148 \|
	\| spearman_euclidean \| 0.8105 \|
	\| pearson_dot \| 0.75 \|
	\| spearman_dot \| 0.735 \|
	\| pearson_max \| 0.8183 \|
	\| spearman_max \| 0.8148 \|

	<!--
	## 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.
	-->

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	### Recommendations

	What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.
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	## Training Details

	### Training Dataset

	#### sentence-transformers/all-nli

	* Dataset: [sentence-transformers/all-nli](https://huggingface.co./datasets/sentence-transformers/all-nli) at [e587f0c](https://huggingface.co./datasets/sentence-transformers/all-nli/tree/e587f0c494c20fb9a1853cdfb43d42576d60a7e5)
	* Size: 557,850 training samples
	* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| anchor \| positive \| negative \|
	\|:--------\|:----------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\|
	\| type \| string \| string \| string \|
	\| details \| <ul><li>min: 7 tokens</li><li>mean: 10.38 tokens</li><li>max: 45 tokens</li></ul> \| <ul><li>min: 6 tokens</li><li>mean: 12.8 tokens</li><li>max: 39 tokens</li></ul> \| <ul><li>min: 6 tokens</li><li>mean: 13.4 tokens</li><li>max: 50 tokens</li></ul> \|
	* Samples:
	\| anchor \| positive \| negative \|
	\|:---------------------------------------------------------------------------\|:-------------------------------------------------\|:-----------------------------------------------------------\|
	\| <code>A person on a horse jumps over a broken down airplane.</code> \| <code>A person is outdoors, on a horse.</code> \| <code>A person is at a diner, ordering an omelette.</code> \|
	\| <code>Children smiling and waving at camera</code> \| <code>There are children present</code> \| <code>The kids are frowning</code> \|
	\| <code>A boy is jumping on skateboard in the middle of a red bridge.</code> \| <code>The boy does a skateboarding trick.</code> \| <code>The boy skates down the sidewalk.</code> \|
	* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/losses.html#adaptivelayerloss) with these parameters:
	```json
	{
	"loss": "MultipleNegativesRankingLoss",
	"n_layers_per_step": 1,
	"last_layer_weight": 1.0,
	"prior_layers_weight": 1.0,
	"kl_div_weight": 1.0,
	"kl_temperature": 0.3
	}
	```

	### Evaluation Dataset

	#### sentence-transformers/all-nli

	* Dataset: [sentence-transformers/all-nli](https://huggingface.co./datasets/sentence-transformers/all-nli) at [e587f0c](https://huggingface.co./datasets/sentence-transformers/all-nli/tree/e587f0c494c20fb9a1853cdfb43d42576d60a7e5)
	* Size: 6,584 evaluation samples
	* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| anchor \| positive \| negative \|
	\|:--------\|:----------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|
	\| type \| string \| string \| string \|
	\| details \| <ul><li>min: 6 tokens</li><li>mean: 18.02 tokens</li><li>max: 66 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 9.81 tokens</li><li>max: 29 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 10.37 tokens</li><li>max: 29 tokens</li></ul> \|
	* Samples:
	\| anchor \| positive \| negative \|
	\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:------------------------------------------------------------\|:--------------------------------------------------------\|
	\| <code>Two women are embracing while holding to go packages.</code> \| <code>Two woman are holding packages.</code> \| <code>The men are fighting outside a deli.</code> \|
	\| <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> \| <code>Two kids in numbered jerseys wash their hands.</code> \| <code>Two kids in jackets walk to school.</code> \|
	\| <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code> \| <code>A man selling donuts to a customer.</code> \| <code>A woman drinks her coffee in a small cafe.</code> \|
	* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/losses.html#adaptivelayerloss) with these parameters:
	```json
	{
	"loss": "MultipleNegativesRankingLoss",
	"n_layers_per_step": 1,
	"last_layer_weight": 1.0,
	"prior_layers_weight": 1.0,
	"kl_div_weight": 1.0,
	"kl_temperature": 0.3
	}
	```

	### Training Hyperparameters
	#### Non-Default Hyperparameters

	- `eval_strategy`: steps
	- `per_device_train_batch_size`: 128
	- `per_device_eval_batch_size`: 128
	- `num_train_epochs`: 1
	- `warmup_ratio`: 0.1
	- `fp16`: 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`: False
	- `per_device_train_batch_size`: 128
	- `per_device_eval_batch_size`: 128
	- `per_gpu_train_batch_size`: None
	- `per_gpu_eval_batch_size`: None
	- `gradient_accumulation_steps`: 1
	- `eval_accumulation_steps`: None
	- `learning_rate`: 5e-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
	- `no_cuda`: False
	- `use_cpu`: False
	- `use_mps_device`: False
	- `seed`: 42
	- `data_seed`: None
	- `jit_mode_eval`: False
	- `use_ipex`: False
	- `bf16`: False
	- `fp16`: True
	- `fp16_opt_level`: O1
	- `half_precision_backend`: auto
	- `bf16_full_eval`: False
	- `fp16_full_eval`: False
	- `tf32`: None
	- `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`: False
	- `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
	- `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`: None
	- `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_sampler`: no_duplicates
	- `multi_dataset_batch_sampler`: proportional

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \| loss \| sts-dev_spearman_cosine \| sts-test_spearman_cosine \|
	\|:------:\|:----:\|:-------------:\|:------:\|:-----------------------:\|:------------------------:\|
	\| 0.0229 \| 100 \| 7.0517 \| 3.9378 \| 0.7889 \| - \|
	\| 0.0459 \| 200 \| 4.4877 \| 3.8105 \| 0.7906 \| - \|
	\| 0.0688 \| 300 \| 4.0315 \| 3.6401 \| 0.7966 \| - \|
	\| 0.0918 \| 400 \| 3.822 \| 3.3537 \| 0.7883 \| - \|
	\| 0.1147 \| 500 \| 3.0608 \| 2.5975 \| 0.7973 \| - \|
	\| 0.1376 \| 600 \| 2.6304 \| 2.3956 \| 0.7943 \| - \|
	\| 0.1606 \| 700 \| 2.7723 \| 2.0379 \| 0.8009 \| - \|
	\| 0.1835 \| 800 \| 2.3556 \| 1.9645 \| 0.7984 \| - \|
	\| 0.2065 \| 900 \| 2.4998 \| 1.9086 \| 0.8017 \| - \|
	\| 0.2294 \| 1000 \| 2.1834 \| 1.8400 \| 0.7973 \| - \|
	\| 0.2524 \| 1100 \| 2.2793 \| 1.5831 \| 0.8102 \| - \|
	\| 0.2753 \| 1200 \| 2.1042 \| 1.6485 \| 0.8004 \| - \|
	\| 0.2982 \| 1300 \| 2.1365 \| 1.7084 \| 0.8013 \| - \|
	\| 0.3212 \| 1400 \| 2.0096 \| 1.5520 \| 0.8064 \| - \|
	\| 0.3441 \| 1500 \| 2.0492 \| 1.4917 \| 0.8084 \| - \|
	\| 0.3671 \| 1600 \| 1.8764 \| 1.5447 \| 0.8018 \| - \|
	\| 0.3900 \| 1700 \| 1.8611 \| 1.5480 \| 0.8046 \| - \|
	\| 0.4129 \| 1800 \| 1.972 \| 1.5353 \| 0.8075 \| - \|
	\| 0.4359 \| 1900 \| 1.8062 \| 1.4633 \| 0.8039 \| - \|
	\| 0.4588 \| 2000 \| 1.8565 \| 1.4213 \| 0.8027 \| - \|
	\| 0.4818 \| 2100 \| 1.8852 \| 1.3860 \| 0.8002 \| - \|
	\| 0.5047 \| 2200 \| 1.7939 \| 1.5468 \| 0.7910 \| - \|
	\| 0.5276 \| 2300 \| 1.7398 \| 1.6041 \| 0.7888 \| - \|
	\| 0.5506 \| 2400 \| 1.8535 \| 1.5791 \| 0.7949 \| - \|
	\| 0.5735 \| 2500 \| 1.8486 \| 1.4871 \| 0.7951 \| - \|
	\| 0.5965 \| 2600 \| 1.7379 \| 1.5427 \| 0.8019 \| - \|
	\| 0.6194 \| 2700 \| 1.7325 \| 1.4585 \| 0.8087 \| - \|
	\| 0.6423 \| 2800 \| 1.7664 \| 1.5264 \| 0.7965 \| - \|
	\| 0.6653 \| 2900 \| 1.7517 \| 1.6344 \| 0.7930 \| - \|
	\| 0.6882 \| 3000 \| 1.8329 \| 1.4947 \| 0.8008 \| - \|
	\| 0.7112 \| 3100 \| 1.7206 \| 1.4917 \| 0.8089 \| - \|
	\| 0.7341 \| 3200 \| 1.7138 \| 1.4185 \| 0.8065 \| - \|
	\| 0.7571 \| 3300 \| 1.3705 \| 1.2040 \| 0.8446 \| - \|
	\| 0.7800 \| 3400 \| 1.1289 \| 1.1363 \| 0.8447 \| - \|
	\| 0.8029 \| 3500 \| 1.0174 \| 1.1049 \| 0.8464 \| - \|
	\| 0.8259 \| 3600 \| 1.0188 \| 1.0362 \| 0.8466 \| - \|
	\| 0.8488 \| 3700 \| 0.9841 \| 1.1391 \| 0.8470 \| - \|
	\| 0.8718 \| 3800 \| 0.8466 \| 1.0116 \| 0.8485 \| - \|
	\| 0.8947 \| 3900 \| 0.9268 \| 1.1323 \| 0.8488 \| - \|
	\| 0.9176 \| 4000 \| 0.8686 \| 1.0296 \| 0.8495 \| - \|
	\| 0.9406 \| 4100 \| 0.9255 \| 1.1737 \| 0.8484 \| - \|
	\| 0.9635 \| 4200 \| 0.7991 \| 1.0609 \| 0.8486 \| - \|
	\| 0.9865 \| 4300 \| 0.8431 \| 0.9976 \| 0.8486 \| - \|
	\| 1.0 \| 4359 \| - \| - \| - \| 0.8148 \|


	### Environmental Impact
	Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
	- Energy Consumed: 0.244 kWh
	- Carbon Emitted: 0.095 kg of CO2
	- Hours Used: 0.849 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: 3.0.0.dev0
	- Transformers: 4.41.0.dev0
	- PyTorch: 2.3.0+cu121
	- Accelerate: 0.26.1
	- Datasets: 2.18.0
	- Tokenizers: 0.19.1

	## 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",
	}
	```

	#### AdaptiveLayerLoss
	```bibtex
	@misc{li20242d,
	title={2D Matryoshka Sentence Embeddings},
	author={Xianming Li and Zongxi Li and Jing Li and Haoran Xie and Qing Li},
	year={2024},
	eprint={2402.14776},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
	}
	```

	#### 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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