Add new SentenceTransformer model.

5b15e6f verified 4 months ago

70 kB

	---
	language: []
	library_name: sentence-transformers
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:1115700
	- loss:MatryoshkaLoss
	- loss:MultipleNegativesRankingLoss
	base_model: nomic-ai/nomic-embed-text-v1.5
	datasets: []
	metrics:
	- pearson_cosine
	- spearman_cosine
	- pearson_manhattan
	- spearman_manhattan
	- pearson_euclidean
	- spearman_euclidean
	- pearson_dot
	- spearman_dot
	- pearson_max
	- spearman_max
	widget:
	- source_sentence: Ndege mwenye mdomo mrefu katikati ya ndege.
	sentences:
	- Panya anayekimbia juu ya gurudumu.
	- Mtu anashindana katika mashindano ya mbio.
	- Ndege anayeruka.
	- source_sentence: Msichana mchanga mwenye nywele nyeusi anakabili kamera na kushikilia
	mfuko wa karatasi wakati amevaa shati la machungwa na mabawa ya kipepeo yenye
	rangi nyingi.
	sentences:
	- Mwanamke mzee anakataa kupigwa picha.
	- mtu akila na mvulana mdogo kwenye kijia cha jiji
	- Msichana mchanga anakabili kamera.
	- source_sentence: Wanawake na watoto wameketi nje katika kivuli wakati kikundi cha
	watoto wadogo wameketi ndani katika kivuli.
	sentences:
	- Mwanamke na watoto na kukaa chini.
	- Mwanamke huyo anakimbia.
	- Watu wanasafiri kwa baiskeli.
	- source_sentence: Mtoto mdogo anaruka mikononi mwa mwanamke aliyevalia suti nyeusi
	ya kuogelea akiwa kwenye dimbwi.
	sentences:
	- Mtoto akiruka mikononi mwa mwanamke aliyevalia suti ya kuogelea kwenye dimbwi.
	- Someone is holding oranges and walking
	- Mama na binti wakinunua viatu.
	- source_sentence: Mwanamume na mwanamke wachanga waliovaa mikoba wanaweka au kuondoa
	kitu kutoka kwenye mti mweupe wa zamani, huku watu wengine wamesimama au wameketi
	nyuma.
	sentences:
	- tai huruka
	- mwanamume na mwanamke wenye mikoba
	- Wanaume wawili wameketi karibu na mwanamke.
	pipeline_tag: sentence-similarity
	model-index:
	- name: SentenceTransformer based on nomic-ai/nomic-embed-text-v1.5
	results:
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 768
	type: sts-test-768
	metrics:
	- type: pearson_cosine
	value: 0.6944960057464138
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.6872396378196957
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.7086043588614903
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.7136479613274518
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.7084460037709435
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.7128357831285198
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.481902874304561
	name: Pearson Dot
	- type: spearman_dot
	value: 0.46588918379526945
	name: Spearman Dot
	- type: pearson_max
	value: 0.7086043588614903
	name: Pearson Max
	- type: spearman_max
	value: 0.7136479613274518
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 512
	type: sts-test-512
	metrics:
	- type: pearson_cosine
	value: 0.6925787246105148
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.6859479129419207
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.7087290093387656
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.7127968133455542
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.7088805484816247
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.7123606046721803
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.4684333245586192
	name: Pearson Dot
	- type: spearman_dot
	value: 0.45257836578849003
	name: Spearman Dot
	- type: pearson_max
	value: 0.7088805484816247
	name: Pearson Max
	- type: spearman_max
	value: 0.7127968133455542
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 256
	type: sts-test-256
	metrics:
	- type: pearson_cosine
	value: 0.6876956481856266
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.6814892249857147
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.7083882582081078
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.7097524143994903
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.7094190252305796
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.7104287347206688
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.4438925722484721
	name: Pearson Dot
	- type: spearman_dot
	value: 0.4255299982188107
	name: Spearman Dot
	- type: pearson_max
	value: 0.7094190252305796
	name: Pearson Max
	- type: spearman_max
	value: 0.7104287347206688
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 128
	type: sts-test-128
	metrics:
	- type: pearson_cosine
	value: 0.6708560165075523
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.6669935075512006
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.7041961281711793
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.7000807688296651
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.7055061381768357
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.7022686907818495
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.37855771167572094
	name: Pearson Dot
	- type: spearman_dot
	value: 0.35930717422088765
	name: Spearman Dot
	- type: pearson_max
	value: 0.7055061381768357
	name: Pearson Max
	- type: spearman_max
	value: 0.7022686907818495
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 64
	type: sts-test-64
	metrics:
	- type: pearson_cosine
	value: 0.6533817775144477
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.6523997361414113
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.6919834348567717
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.6857245312336051
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.6950438027503257
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.6899151458827059
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.33502302384042637
	name: Pearson Dot
	- type: spearman_dot
	value: 0.3097469345046609
	name: Spearman Dot
	- type: pearson_max
	value: 0.6950438027503257
	name: Pearson Max
	- type: spearman_max
	value: 0.6899151458827059
	name: Spearman Max
	---

	# SentenceTransformer based on nomic-ai/nomic-embed-text-v1.5

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nomic-ai/nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) on the Mollel/swahili-n_li-triplet-swh-eng 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: [nomic-ai/nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) <!-- at revision b0753ae76394dd36bcfb912a46018088bca48be0 -->
	- Maximum Sequence Length: 8192 tokens
	- Output Dimensionality: 768 tokens
	- Similarity Function: Cosine Similarity
	- Training Dataset:
	- Mollel/swahili-n_li-triplet-swh-eng
	<!-- - Language: Unknown -->
	<!-- - 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': 8192, 'do_lower_case': False}) with Transformer model: NomicBertModel
	(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("Mollel/MultiLinguSwahili-nomic-embed-text-v1.5-nli-matryoshka")
	# Run inference
	sentences = [
	'Mwanamume na mwanamke wachanga waliovaa mikoba wanaweka au kuondoa kitu kutoka kwenye mti mweupe wa zamani, huku watu wengine wamesimama au wameketi nyuma.',
	'mwanamume na mwanamke wenye mikoba',
	'tai huruka',
	]
	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]
	```

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	### Direct Usage (Transformers)

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	</details>
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	### 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

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	## Evaluation

	### Metrics

	#### Semantic Similarity
	* Dataset: `sts-test-768`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.6945 \|
	\| spearman_cosine \| 0.6872 \|
	\| pearson_manhattan \| 0.7086 \|
	\| spearman_manhattan \| 0.7136 \|
	\| pearson_euclidean \| 0.7084 \|
	\| spearman_euclidean \| 0.7128 \|
	\| pearson_dot \| 0.4819 \|
	\| spearman_dot \| 0.4659 \|
	\| pearson_max \| 0.7086 \|
	\| spearman_max \| 0.7136 \|

	#### Semantic Similarity
	* Dataset: `sts-test-512`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.6926 \|
	\| spearman_cosine \| 0.6859 \|
	\| pearson_manhattan \| 0.7087 \|
	\| spearman_manhattan \| 0.7128 \|
	\| pearson_euclidean \| 0.7089 \|
	\| spearman_euclidean \| 0.7124 \|
	\| pearson_dot \| 0.4684 \|
	\| spearman_dot \| 0.4526 \|
	\| pearson_max \| 0.7089 \|
	\| spearman_max \| 0.7128 \|

	#### Semantic Similarity
	* Dataset: `sts-test-256`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.6877 \|
	\| spearman_cosine \| 0.6815 \|
	\| pearson_manhattan \| 0.7084 \|
	\| spearman_manhattan \| 0.7098 \|
	\| pearson_euclidean \| 0.7094 \|
	\| spearman_euclidean \| 0.7104 \|
	\| pearson_dot \| 0.4439 \|
	\| spearman_dot \| 0.4255 \|
	\| pearson_max \| 0.7094 \|
	\| spearman_max \| 0.7104 \|

	#### Semantic Similarity
	* Dataset: `sts-test-128`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:----------\|
	\| pearson_cosine \| 0.6709 \|
	\| spearman_cosine \| 0.667 \|
	\| pearson_manhattan \| 0.7042 \|
	\| spearman_manhattan \| 0.7001 \|
	\| pearson_euclidean \| 0.7055 \|
	\| spearman_euclidean \| 0.7023 \|
	\| pearson_dot \| 0.3786 \|
	\| spearman_dot \| 0.3593 \|
	\| pearson_max \| 0.7055 \|
	\| spearman_max \| 0.7023 \|

	#### Semantic Similarity
	* Dataset: `sts-test-64`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.6534 \|
	\| spearman_cosine \| 0.6524 \|
	\| pearson_manhattan \| 0.692 \|
	\| spearman_manhattan \| 0.6857 \|
	\| pearson_euclidean \| 0.695 \|
	\| spearman_euclidean \| 0.6899 \|
	\| pearson_dot \| 0.335 \|
	\| spearman_dot \| 0.3097 \|
	\| pearson_max \| 0.695 \|
	\| spearman_max \| 0.6899 \|

	<!--
	## 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

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	## Training Details

	### Training Dataset

	#### Mollel/swahili-n_li-triplet-swh-eng

	* Dataset: Mollel/swahili-n_li-triplet-swh-eng
	* Size: 1,115,700 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: 15.18 tokens</li><li>max: 80 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 18.53 tokens</li><li>max: 52 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 17.8 tokens</li><li>max: 53 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>Mtu aliyepanda farasi anaruka juu ya ndege iliyovunjika.</code> \| <code>Mtu yuko nje, juu ya farasi.</code> \| <code>Mtu yuko kwenye mkahawa, akiagiza omelette.</code> \|
	\| <code>Children smiling and waving at camera</code> \| <code>There are children present</code> \| <code>The kids are frowning</code> \|
	* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
	```json
	{
	"loss": "MultipleNegativesRankingLoss",
	"matryoshka_dims": [
	768,
	512,
	256,
	128,
	64
	],
	"matryoshka_weights": [
	1,
	1,
	1,
	1,
	1
	],
	"n_dims_per_step": -1
	}
	```

	### Evaluation Dataset

	#### Mollel/swahili-n_li-triplet-swh-eng

	* Dataset: Mollel/swahili-n_li-triplet-swh-eng
	* Size: 13,168 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: 26.43 tokens</li><li>max: 94 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 13.37 tokens</li><li>max: 65 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 14.7 tokens</li><li>max: 54 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>Wanawake wawili wanakumbatiana huku wakishikilia vifurushi vya kwenda.</code> \| <code>Wanawake wawili wanashikilia vifurushi.</code> \| <code>Wanaume hao wanapigana nje ya duka la vyakula vitamu.</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> \|
	* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
	```json
	{
	"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

	- `per_device_train_batch_size`: 24
	- `per_device_eval_batch_size`: 24
	- `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
	- `prediction_loss_only`: True
	- `per_device_train_batch_size`: 24
	- `per_device_eval_batch_size`: 24
	- `per_gpu_train_batch_size`: None
	- `per_gpu_eval_batch_size`: None
	- `gradient_accumulation_steps`: 1
	- `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`: 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`: True
	- `fp16`: False
	- `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, '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`: 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_sampler`: no_duplicates
	- `multi_dataset_batch_sampler`: proportional

	</details>

	### Training Logs
	<details><summary>Click to expand</summary>

	\| Epoch \| Step \| Training Loss \| sts-test-128_spearman_cosine \| sts-test-256_spearman_cosine \| sts-test-512_spearman_cosine \| sts-test-64_spearman_cosine \| sts-test-768_spearman_cosine \|
	\|:------:\|:-----:\|:-------------:\|:----------------------------:\|:----------------------------:\|:----------------------------:\|:---------------------------:\|:----------------------------:\|
	\| 0.0043 \| 100 \| 10.0627 \| - \| - \| - \| - \| - \|
	\| 0.0086 \| 200 \| 8.2355 \| - \| - \| - \| - \| - \|
	\| 0.0129 \| 300 \| 6.7233 \| - \| - \| - \| - \| - \|
	\| 0.0172 \| 400 \| 6.5832 \| - \| - \| - \| - \| - \|
	\| 0.0215 \| 500 \| 6.7512 \| - \| - \| - \| - \| - \|
	\| 0.0258 \| 600 \| 6.7634 \| - \| - \| - \| - \| - \|
	\| 0.0301 \| 700 \| 6.5592 \| - \| - \| - \| - \| - \|
	\| 0.0344 \| 800 \| 5.0689 \| - \| - \| - \| - \| - \|
	\| 0.0387 \| 900 \| 4.7079 \| - \| - \| - \| - \| - \|
	\| 0.0430 \| 1000 \| 4.6359 \| - \| - \| - \| - \| - \|
	\| 0.0473 \| 1100 \| 4.4513 \| - \| - \| - \| - \| - \|
	\| 0.0516 \| 1200 \| 4.2328 \| - \| - \| - \| - \| - \|
	\| 0.0559 \| 1300 \| 3.7454 \| - \| - \| - \| - \| - \|
	\| 0.0602 \| 1400 \| 3.9198 \| - \| - \| - \| - \| - \|
	\| 0.0645 \| 1500 \| 4.0727 \| - \| - \| - \| - \| - \|
	\| 0.0688 \| 1600 \| 3.8923 \| - \| - \| - \| - \| - \|
	\| 0.0731 \| 1700 \| 3.8137 \| - \| - \| - \| - \| - \|
	\| 0.0774 \| 1800 \| 4.1512 \| - \| - \| - \| - \| - \|
	\| 0.0817 \| 1900 \| 4.1304 \| - \| - \| - \| - \| - \|
	\| 0.0860 \| 2000 \| 4.0195 \| - \| - \| - \| - \| - \|
	\| 0.0903 \| 2100 \| 3.6836 \| - \| - \| - \| - \| - \|
	\| 0.0946 \| 2200 \| 2.9968 \| - \| - \| - \| - \| - \|
	\| 0.0990 \| 2300 \| 2.8909 \| - \| - \| - \| - \| - \|
	\| 0.1033 \| 2400 \| 3.0884 \| - \| - \| - \| - \| - \|
	\| 0.1076 \| 2500 \| 3.3081 \| - \| - \| - \| - \| - \|
	\| 0.1119 \| 2600 \| 3.6266 \| - \| - \| - \| - \| - \|
	\| 0.1162 \| 2700 \| 4.3754 \| - \| - \| - \| - \| - \|
	\| 0.1205 \| 2800 \| 4.0218 \| - \| - \| - \| - \| - \|
	\| 0.1248 \| 2900 \| 3.7167 \| - \| - \| - \| - \| - \|
	\| 0.1291 \| 3000 \| 3.4815 \| - \| - \| - \| - \| - \|
	\| 0.1334 \| 3100 \| 3.6446 \| - \| - \| - \| - \| - \|
	\| 0.1377 \| 3200 \| 3.44 \| - \| - \| - \| - \| - \|
	\| 0.1420 \| 3300 \| 3.6725 \| - \| - \| - \| - \| - \|
	\| 0.1463 \| 3400 \| 3.4699 \| - \| - \| - \| - \| - \|
	\| 0.1506 \| 3500 \| 3.076 \| - \| - \| - \| - \| - \|
	\| 0.1549 \| 3600 \| 3.1179 \| - \| - \| - \| - \| - \|
	\| 0.1592 \| 3700 \| 3.1704 \| - \| - \| - \| - \| - \|
	\| 0.1635 \| 3800 \| 3.4614 \| - \| - \| - \| - \| - \|
	\| 0.1678 \| 3900 \| 4.1157 \| - \| - \| - \| - \| - \|
	\| 0.1721 \| 4000 \| 4.1584 \| - \| - \| - \| - \| - \|
	\| 0.1764 \| 4100 \| 4.5602 \| - \| - \| - \| - \| - \|
	\| 0.1807 \| 4200 \| 3.6875 \| - \| - \| - \| - \| - \|
	\| 0.1850 \| 4300 \| 4.1521 \| - \| - \| - \| - \| - \|
	\| 0.1893 \| 4400 \| 3.5475 \| - \| - \| - \| - \| - \|
	\| 0.1936 \| 4500 \| 3.4036 \| - \| - \| - \| - \| - \|
	\| 0.1979 \| 4600 \| 3.0564 \| - \| - \| - \| - \| - \|
	\| 0.2022 \| 4700 \| 3.7761 \| - \| - \| - \| - \| - \|
	\| 0.2065 \| 4800 \| 3.6857 \| - \| - \| - \| - \| - \|
	\| 0.2108 \| 4900 \| 3.3534 \| - \| - \| - \| - \| - \|
	\| 0.2151 \| 5000 \| 4.1137 \| - \| - \| - \| - \| - \|
	\| 0.2194 \| 5100 \| 3.5239 \| - \| - \| - \| - \| - \|
	\| 0.2237 \| 5200 \| 4.1297 \| - \| - \| - \| - \| - \|
	\| 0.2280 \| 5300 \| 3.5339 \| - \| - \| - \| - \| - \|
	\| 0.2323 \| 5400 \| 3.9294 \| - \| - \| - \| - \| - \|
	\| 0.2366 \| 5500 \| 3.717 \| - \| - \| - \| - \| - \|
	\| 0.2409 \| 5600 \| 3.3346 \| - \| - \| - \| - \| - \|
	\| 0.2452 \| 5700 \| 4.0495 \| - \| - \| - \| - \| - \|
	\| 0.2495 \| 5800 \| 3.7869 \| - \| - \| - \| - \| - \|
	\| 0.2538 \| 5900 \| 3.9533 \| - \| - \| - \| - \| - \|
	\| 0.2581 \| 6000 \| 4.1135 \| - \| - \| - \| - \| - \|
	\| 0.2624 \| 6100 \| 3.6655 \| - \| - \| - \| - \| - \|
	\| 0.2667 \| 6200 \| 3.9111 \| - \| - \| - \| - \| - \|
	\| 0.2710 \| 6300 \| 3.8582 \| - \| - \| - \| - \| - \|
	\| 0.2753 \| 6400 \| 3.7712 \| - \| - \| - \| - \| - \|
	\| 0.2796 \| 6500 \| 3.6536 \| - \| - \| - \| - \| - \|
	\| 0.2839 \| 6600 \| 3.4516 \| - \| - \| - \| - \| - \|
	\| 0.2882 \| 6700 \| 3.7151 \| - \| - \| - \| - \| - \|
	\| 0.2925 \| 6800 \| 3.7659 \| - \| - \| - \| - \| - \|
	\| 0.2969 \| 6900 \| 3.3159 \| - \| - \| - \| - \| - \|
	\| 0.3012 \| 7000 \| 3.5753 \| - \| - \| - \| - \| - \|
	\| 0.3055 \| 7100 \| 4.2095 \| - \| - \| - \| - \| - \|
	\| 0.3098 \| 7200 \| 3.718 \| - \| - \| - \| - \| - \|
	\| 0.3141 \| 7300 \| 4.0709 \| - \| - \| - \| - \| - \|
	\| 0.3184 \| 7400 \| 3.8079 \| - \| - \| - \| - \| - \|
	\| 0.3227 \| 7500 \| 3.3735 \| - \| - \| - \| - \| - \|
	\| 0.3270 \| 7600 \| 3.7303 \| - \| - \| - \| - \| - \|
	\| 0.3313 \| 7700 \| 3.2693 \| - \| - \| - \| - \| - \|
	\| 0.3356 \| 7800 \| 3.6564 \| - \| - \| - \| - \| - \|
	\| 0.3399 \| 7900 \| 3.6702 \| - \| - \| - \| - \| - \|
	\| 0.3442 \| 8000 \| 3.7274 \| - \| - \| - \| - \| - \|
	\| 0.3485 \| 8100 \| 3.8536 \| - \| - \| - \| - \| - \|
	\| 0.3528 \| 8200 \| 3.9516 \| - \| - \| - \| - \| - \|
	\| 0.3571 \| 8300 \| 3.7351 \| - \| - \| - \| - \| - \|
	\| 0.3614 \| 8400 \| 3.649 \| - \| - \| - \| - \| - \|
	\| 0.3657 \| 8500 \| 3.5913 \| - \| - \| - \| - \| - \|
	\| 0.3700 \| 8600 \| 3.7733 \| - \| - \| - \| - \| - \|
	\| 0.3743 \| 8700 \| 3.6359 \| - \| - \| - \| - \| - \|
	\| 0.3786 \| 8800 \| 4.2983 \| - \| - \| - \| - \| - \|
	\| 0.3829 \| 8900 \| 3.6692 \| - \| - \| - \| - \| - \|
	\| 0.3872 \| 9000 \| 3.7309 \| - \| - \| - \| - \| - \|
	\| 0.3915 \| 9100 \| 3.8886 \| - \| - \| - \| - \| - \|
	\| 0.3958 \| 9200 \| 3.8999 \| - \| - \| - \| - \| - \|
	\| 0.4001 \| 9300 \| 3.5528 \| - \| - \| - \| - \| - \|
	\| 0.4044 \| 9400 \| 3.6309 \| - \| - \| - \| - \| - \|
	\| 0.4087 \| 9500 \| 4.2475 \| - \| - \| - \| - \| - \|
	\| 0.4130 \| 9600 \| 3.793 \| - \| - \| - \| - \| - \|
	\| 0.4173 \| 9700 \| 3.6575 \| - \| - \| - \| - \| - \|
	\| 0.4216 \| 9800 \| 3.84 \| - \| - \| - \| - \| - \|
	\| 0.4259 \| 9900 \| 3.3721 \| - \| - \| - \| - \| - \|
	\| 0.4302 \| 10000 \| 4.3743 \| - \| - \| - \| - \| - \|
	\| 0.4345 \| 10100 \| 3.5054 \| - \| - \| - \| - \| - \|
	\| 0.4388 \| 10200 \| 3.54 \| - \| - \| - \| - \| - \|
	\| 0.4431 \| 10300 \| 3.6197 \| - \| - \| - \| - \| - \|
	\| 0.4474 \| 10400 \| 3.7567 \| - \| - \| - \| - \| - \|
	\| 0.4517 \| 10500 \| 3.9814 \| - \| - \| - \| - \| - \|
	\| 0.4560 \| 10600 \| 3.6277 \| - \| - \| - \| - \| - \|
	\| 0.4603 \| 10700 \| 3.5071 \| - \| - \| - \| - \| - \|
	\| 0.4646 \| 10800 \| 3.8348 \| - \| - \| - \| - \| - \|
	\| 0.4689 \| 10900 \| 3.8674 \| - \| - \| - \| - \| - \|
	\| 0.4732 \| 11000 \| 3.0325 \| - \| - \| - \| - \| - \|
	\| 0.4775 \| 11100 \| 3.7262 \| - \| - \| - \| - \| - \|
	\| 0.4818 \| 11200 \| 3.6921 \| - \| - \| - \| - \| - \|
	\| 0.4861 \| 11300 \| 3.4946 \| - \| - \| - \| - \| - \|
	\| 0.4904 \| 11400 \| 3.7541 \| - \| - \| - \| - \| - \|
	\| 0.4948 \| 11500 \| 3.6751 \| - \| - \| - \| - \| - \|
	\| 0.4991 \| 11600 \| 3.8765 \| - \| - \| - \| - \| - \|
	\| 0.5034 \| 11700 \| 3.5058 \| - \| - \| - \| - \| - \|
	\| 0.5077 \| 11800 \| 3.5135 \| - \| - \| - \| - \| - \|
	\| 0.5120 \| 11900 \| 3.8052 \| - \| - \| - \| - \| - \|
	\| 0.5163 \| 12000 \| 3.3015 \| - \| - \| - \| - \| - \|
	\| 0.5206 \| 12100 \| 3.5389 \| - \| - \| - \| - \| - \|
	\| 0.5249 \| 12200 \| 3.5226 \| - \| - \| - \| - \| - \|
	\| 0.5292 \| 12300 \| 3.6715 \| - \| - \| - \| - \| - \|
	\| 0.5335 \| 12400 \| 3.2256 \| - \| - \| - \| - \| - \|
	\| 0.5378 \| 12500 \| 3.3447 \| - \| - \| - \| - \| - \|
	\| 0.5421 \| 12600 \| 3.6315 \| - \| - \| - \| - \| - \|
	\| 0.5464 \| 12700 \| 3.8674 \| - \| - \| - \| - \| - \|
	\| 0.5507 \| 12800 \| 3.4066 \| - \| - \| - \| - \| - \|
	\| 0.5550 \| 12900 \| 3.7356 \| - \| - \| - \| - \| - \|
	\| 0.5593 \| 13000 \| 3.5742 \| - \| - \| - \| - \| - \|
	\| 0.5636 \| 13100 \| 3.7676 \| - \| - \| - \| - \| - \|
	\| 0.5679 \| 13200 \| 3.7907 \| - \| - \| - \| - \| - \|
	\| 0.5722 \| 13300 \| 3.8089 \| - \| - \| - \| - \| - \|
	\| 0.5765 \| 13400 \| 3.4742 \| - \| - \| - \| - \| - \|
	\| 0.5808 \| 13500 \| 3.6536 \| - \| - \| - \| - \| - \|
	\| 0.5851 \| 13600 \| 3.7736 \| - \| - \| - \| - \| - \|
	\| 0.5894 \| 13700 \| 3.9072 \| - \| - \| - \| - \| - \|
	\| 0.5937 \| 13800 \| 3.7386 \| - \| - \| - \| - \| - \|
	\| 0.5980 \| 13900 \| 3.3387 \| - \| - \| - \| - \| - \|
	\| 0.6023 \| 14000 \| 3.5509 \| - \| - \| - \| - \| - \|
	\| 0.6066 \| 14100 \| 3.7056 \| - \| - \| - \| - \| - \|
	\| 0.6109 \| 14200 \| 3.7283 \| - \| - \| - \| - \| - \|
	\| 0.6152 \| 14300 \| 3.7301 \| - \| - \| - \| - \| - \|
	\| 0.6195 \| 14400 \| 3.8027 \| - \| - \| - \| - \| - \|
	\| 0.6238 \| 14500 \| 3.5606 \| - \| - \| - \| - \| - \|
	\| 0.6281 \| 14600 \| 3.9467 \| - \| - \| - \| - \| - \|
	\| 0.6324 \| 14700 \| 3.3394 \| - \| - \| - \| - \| - \|
	\| 0.6367 \| 14800 \| 4.1254 \| - \| - \| - \| - \| - \|
	\| 0.6410 \| 14900 \| 3.7121 \| - \| - \| - \| - \| - \|
	\| 0.6453 \| 15000 \| 3.9167 \| - \| - \| - \| - \| - \|
	\| 0.6496 \| 15100 \| 3.8084 \| - \| - \| - \| - \| - \|
	\| 0.6539 \| 15200 \| 3.7794 \| - \| - \| - \| - \| - \|
	\| 0.6582 \| 15300 \| 3.7664 \| - \| - \| - \| - \| - \|
	\| 0.6625 \| 15400 \| 3.4378 \| - \| - \| - \| - \| - \|
	\| 0.6668 \| 15500 \| 3.6632 \| - \| - \| - \| - \| - \|
	\| 0.6711 \| 15600 \| 3.8493 \| - \| - \| - \| - \| - \|
	\| 0.6754 \| 15700 \| 4.1475 \| - \| - \| - \| - \| - \|
	\| 0.6797 \| 15800 \| 3.5782 \| - \| - \| - \| - \| - \|
	\| 0.6840 \| 15900 \| 3.4341 \| - \| - \| - \| - \| - \|
	\| 0.6883 \| 16000 \| 3.3295 \| - \| - \| - \| - \| - \|
	\| 0.6927 \| 16100 \| 3.8165 \| - \| - \| - \| - \| - \|
	\| 0.6970 \| 16200 \| 3.9702 \| - \| - \| - \| - \| - \|
	\| 0.7013 \| 16300 \| 3.6555 \| - \| - \| - \| - \| - \|
	\| 0.7056 \| 16400 \| 3.6946 \| - \| - \| - \| - \| - \|
	\| 0.7099 \| 16500 \| 3.8027 \| - \| - \| - \| - \| - \|
	\| 0.7142 \| 16600 \| 3.4523 \| - \| - \| - \| - \| - \|
	\| 0.7185 \| 16700 \| 3.461 \| - \| - \| - \| - \| - \|
	\| 0.7228 \| 16800 \| 3.4403 \| - \| - \| - \| - \| - \|
	\| 0.7271 \| 16900 \| 3.6398 \| - \| - \| - \| - \| - \|
	\| 0.7314 \| 17000 \| 3.8443 \| - \| - \| - \| - \| - \|
	\| 0.7357 \| 17100 \| 3.6012 \| - \| - \| - \| - \| - \|
	\| 0.7400 \| 17200 \| 3.6645 \| - \| - \| - \| - \| - \|
	\| 0.7443 \| 17300 \| 3.4899 \| - \| - \| - \| - \| - \|
	\| 0.7486 \| 17400 \| 3.7186 \| - \| - \| - \| - \| - \|
	\| 0.7529 \| 17500 \| 3.6199 \| - \| - \| - \| - \| - \|
	\| 0.7572 \| 17600 \| 4.4274 \| - \| - \| - \| - \| - \|
	\| 0.7615 \| 17700 \| 4.0262 \| - \| - \| - \| - \| - \|
	\| 0.7658 \| 17800 \| 3.9325 \| - \| - \| - \| - \| - \|
	\| 0.7701 \| 17900 \| 3.6338 \| - \| - \| - \| - \| - \|
	\| 0.7744 \| 18000 \| 3.6136 \| - \| - \| - \| - \| - \|
	\| 0.7787 \| 18100 \| 3.4514 \| - \| - \| - \| - \| - \|
	\| 0.7830 \| 18200 \| 3.4427 \| - \| - \| - \| - \| - \|
	\| 0.7873 \| 18300 \| 3.3601 \| - \| - \| - \| - \| - \|
	\| 0.7916 \| 18400 \| 3.313 \| - \| - \| - \| - \| - \|
	\| 0.7959 \| 18500 \| 3.4062 \| - \| - \| - \| - \| - \|
	\| 0.8002 \| 18600 \| 3.098 \| - \| - \| - \| - \| - \|
	\| 0.8045 \| 18700 \| 3.183 \| - \| - \| - \| - \| - \|
	\| 0.8088 \| 18800 \| 3.1482 \| - \| - \| - \| - \| - \|
	\| 0.8131 \| 18900 \| 3.0122 \| - \| - \| - \| - \| - \|
	\| 0.8174 \| 19000 \| 3.0828 \| - \| - \| - \| - \| - \|
	\| 0.8217 \| 19100 \| 3.063 \| - \| - \| - \| - \| - \|
	\| 0.8260 \| 19200 \| 2.9688 \| - \| - \| - \| - \| - \|
	\| 0.8303 \| 19300 \| 3.0425 \| - \| - \| - \| - \| - \|
	\| 0.8346 \| 19400 \| 3.2018 \| - \| - \| - \| - \| - \|
	\| 0.8389 \| 19500 \| 2.9111 \| - \| - \| - \| - \| - \|
	\| 0.8432 \| 19600 \| 2.9516 \| - \| - \| - \| - \| - \|
	\| 0.8475 \| 19700 \| 2.9115 \| - \| - \| - \| - \| - \|
	\| 0.8518 \| 19800 \| 2.9323 \| - \| - \| - \| - \| - \|
	\| 0.8561 \| 19900 \| 2.8753 \| - \| - \| - \| - \| - \|
	\| 0.8604 \| 20000 \| 2.8344 \| - \| - \| - \| - \| - \|
	\| 0.8647 \| 20100 \| 2.7665 \| - \| - \| - \| - \| - \|
	\| 0.8690 \| 20200 \| 2.7732 \| - \| - \| - \| - \| - \|
	\| 0.8733 \| 20300 \| 2.8622 \| - \| - \| - \| - \| - \|
	\| 0.8776 \| 20400 \| 2.8749 \| - \| - \| - \| - \| - \|
	\| 0.8819 \| 20500 \| 2.8534 \| - \| - \| - \| - \| - \|
	\| 0.8863 \| 20600 \| 2.9254 \| - \| - \| - \| - \| - \|
	\| 0.8906 \| 20700 \| 2.7366 \| - \| - \| - \| - \| - \|
	\| 0.8949 \| 20800 \| 2.7287 \| - \| - \| - \| - \| - \|
	\| 0.8992 \| 20900 \| 2.9469 \| - \| - \| - \| - \| - \|
	\| 0.9035 \| 21000 \| 2.9052 \| - \| - \| - \| - \| - \|
	\| 0.9078 \| 21100 \| 2.7256 \| - \| - \| - \| - \| - \|
	\| 0.9121 \| 21200 \| 2.8469 \| - \| - \| - \| - \| - \|
	\| 0.9164 \| 21300 \| 2.6626 \| - \| - \| - \| - \| - \|
	\| 0.9207 \| 21400 \| 2.6796 \| - \| - \| - \| - \| - \|
	\| 0.9250 \| 21500 \| 2.6927 \| - \| - \| - \| - \| - \|
	\| 0.9293 \| 21600 \| 2.7125 \| - \| - \| - \| - \| - \|
	\| 0.9336 \| 21700 \| 2.6734 \| - \| - \| - \| - \| - \|
	\| 0.9379 \| 21800 \| 2.7199 \| - \| - \| - \| - \| - \|
	\| 0.9422 \| 21900 \| 2.6635 \| - \| - \| - \| - \| - \|
	\| 0.9465 \| 22000 \| 2.5218 \| - \| - \| - \| - \| - \|
	\| 0.9508 \| 22100 \| 2.7595 \| - \| - \| - \| - \| - \|
	\| 0.9551 \| 22200 \| 2.6821 \| - \| - \| - \| - \| - \|
	\| 0.9594 \| 22300 \| 2.6578 \| - \| - \| - \| - \| - \|
	\| 0.9637 \| 22400 \| 2.568 \| - \| - \| - \| - \| - \|
	\| 0.9680 \| 22500 \| 2.5527 \| - \| - \| - \| - \| - \|
	\| 0.9723 \| 22600 \| 2.6857 \| - \| - \| - \| - \| - \|
	\| 0.9766 \| 22700 \| 2.6637 \| - \| - \| - \| - \| - \|
	\| 0.9809 \| 22800 \| 2.6311 \| - \| - \| - \| - \| - \|
	\| 0.9852 \| 22900 \| 2.4635 \| - \| - \| - \| - \| - \|
	\| 0.9895 \| 23000 \| 2.6239 \| - \| - \| - \| - \| - \|
	\| 0.9938 \| 23100 \| 2.6873 \| - \| - \| - \| - \| - \|
	\| 0.9981 \| 23200 \| 2.5138 \| - \| - \| - \| - \| - \|
	\| 1.0 \| 23244 \| - \| 0.6670 \| 0.6815 \| 0.6859 \| 0.6524 \| 0.6872 \|

	</details>

	### Framework Versions
	- Python: 3.11.9
	- Sentence Transformers: 3.0.1
	- Transformers: 4.40.1
	- PyTorch: 2.3.0+cu121
	- Accelerate: 0.29.3
	- Datasets: 2.19.0
	- 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",
	}
	```

	#### MatryoshkaLoss
	```bibtex
	@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
	```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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