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	---
	language:
	- en
	license: apache-2.0
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:800
	- loss:MultipleNegativesRankingLoss
	base_model: intfloat/e5-base-v2
	widget:
	- source_sentence: For the following multiple choice question, select one correct
	answer. Let s think step by step. Question In a postoperative patient with a urinary
	diversion, the nurse should monitor the urine volume every hour. Below how many
	ml h of urine may indicate that the patient is dehydrated or has some type of
	internal obstruction or loss ? Options A. 200 ml h. B. 100 ml h. C. 80 ml h. D.
	50 ml h. E. 30 ml h.
	sentences:
	- Our approach shows that gene expression can be explained by a modest number of
	co localized transcription factors, however, information on cell type specific
	binding is crucial for understanding combinatorial gene regulation.
	- We have developed a rapid, simple, sensitive and specific method to quantify β
	antithrombin activity using 1μL of plasma. β antithrombin significantly increases
	in patients with ischemic cerebrovascular disease during the acute event, probably
	by its release from the vasculature.
	- A postoperative patient with a urinary diversion requires close monitoring of
	urine output to ensure that the diversion is functioning properly and that the
	patient is not experiencing any complications. Monitoring urine volume every hour
	is a crucial aspect of postoperative care in this scenario. To determine the correct
	answer, let s analyze each option A. 200 ml h This is a relatively high urine
	output, and it would not typically indicate dehydration or internal obstruction.
	In fact, a urine output of 200 ml h is generally considered adequate and may even
	be higher than the average urine output for a healthy adult. B. 100 ml h This
	is also a relatively high urine output and would not typically indicate dehydration
	or internal obstruction. A urine output of 100 ml h is still within the normal
	range and would not raise concerns about dehydration or obstruction. C. 80 ml
	h While this is a slightly lower urine output, it is still within the normal range
	and would not necessarily indicate dehydration or internal obstruction. D. 50
	ml h This is a lower urine output, and it may start to raise concerns about dehydration
	or internal obstruction. However, it is still not the lowest option, and the nurse
	may need to consider other factors before determining the cause of the low urine
	output. E. 30 ml h This is the lowest urine output option, and it would likely
	indicate that the patient is dehydrated or has some type of internal obstruction
	or loss. A urine output of 30 ml h is generally considered low and would require
	immediate attention from the nurse to determine the cause and take corrective
	action. Considering the options, the correct answer is E. 30 ml h. A urine output
	of 30 ml h is a critical threshold that may indicate dehydration or internal obstruction,
	and the nurse should take immediate action to assess the patient s fluid status
	and the functioning of the urinary diversion. Answer E.
	- source_sentence: In tumor lysis syndrome all of the following are seen except
	sentences:
	- The results indicated that some polymorphic variations of drug metabolic and transporter
	genes may be potential biomarkers for clinical outcome of gemcitabine based therapy
	in patients with locally advanced pancreatic cancer.
	- Variations in the prevalence of depressive symptoms occurred between centres,
	not always related to levels of illness. There was no consistent relationship
	between proportions of symptoms in well persons and cases for all centres. Few
	symptoms were present in 60 of the older population stereotypes of old age were
	not upheld.
	- Tumor lysis syndrome Caused by destruction of large number of rapidly proliferating
	neoplastic cells. It frequently leads to ARF It is characterized by Hypocalcemia
	Hyperkalemia Lactic acidosis Hyperuricemia Hyperphosphatemia Most frequently associated
	with treatment of Burkitt lymphoma ALL CLL Solid tumors
	- source_sentence: Does prevalence of central venous occlusion in patients with chronic
	defibrillator lead?
	sentences:
	- Intraoperative small dose IV haloperidol is effective against post operative nausea
	and vomiting with no significant effect on overall QoR. It may also attenuate
	the analgesic effects of morphine PCA.
	- Intubation is generally done with the help of endotracheal tube ETT . The internal
	diameter of ETT used ranges between 3 and 8 mm depending on the age, sex, and
	size of nares of the patient. Potex north and south polar performed Rae tubes
	RAE right angled ETT and flexo metallic tubes are commonly used. Out of them,
	North Pole Rae tube is preferred in case of ankylosis patient due to the direction
	of the curve of ETT which favors its placement in restricted mouth opening as
	in case of ankylosis.
	- The low prevalence of subclavian vein occlusion or severe stenosis among defibrillator
	recipients found in this study suggests that the placement of additional transvenous
	leads in a patient who already has a ventricular defibrillator is feasible in
	a high percentage of patients 93 .
	- source_sentence: Is mode of presentation of B3 breast core biopsies screen detected
	or symptomatic a distinguishing factor in the final histopathologic result or
	risk of diagnosis of malignancy?
	sentences:
	- This observation may indicate a considerable difference in cardiovascular risk
	between genotype groups as a result of an increase in FVIIa after a fat rich diet.
	- Mode of patient presentation with a screen detected or symptomatic lesion was
	not a distinguishing factor for breast histopathologic subclassification or for
	the final cancer diagnosis in patients whose breast core biopsy was classified
	as B3.
	- Ans. is a i.e., Apaf 1o One of these proteins is cytochrome c, well known for
	its role in mitochondrial respiration. In the cytosol, cytochrome C binds to a
	protein called Apaf 1 apoptosis activating factor 1 , and the complex activates
	caspase 9. Bc1 2 and Bcl x may also directly inhibit Apaf 1 activation, and their
	loss from cells may permit activation of Apaf 1 .
	- source_sentence: Is the Danish National Hospital Register a valuable study base
	for epidemiologic research in febrile seizures?
	sentences:
	- Interstitial cystitis IC is a condition that causes discomfort or pain in the
	bladder and a need to urinate frequently and urgently. It is far more common in
	women than in men. The symptoms vary from person to person. Some people may have
	pain without urgency or frequency. Others have urgency and frequency without pain.
	Women s symptoms often get worse during their periods. They may also have pain
	with sexual intercourse. The cause of IC isn t known. There is no one test to
	tell if you have it. Doctors often run tests to rule out other possible causes
	of symptoms. There is no cure for IC, but treatments can help most people feel
	better. They include Distending, or inflating, the bladder Bathing the inside
	of the bladder with a drug solution Oral medicines Electrical nerve stimulation
	Physical therapy Lifestyle changes Bladder training In rare cases, surgery NIH
	National Institute of Diabetes and Digestive and Kidney Diseases
	- Ans. is c i.e., Presence of depression Good prognostic factors Acute onset late
	onset onset after 35 years of age Presence of precipitating stressor Good premorbid
	adjustment catatonic best prognosis Paranoid 2nd best sho duration 6 months Married
	Positive symptoms Presence of depression family history of mood disorder first
	episode pyknic fat physique female sex good treatment compliance good response
	to treatment good social suppo presence of confusion or perplexity normal brain
	CT Scan outpatient treatment.
	- The Danish National Hospital Register is a valuable tool for epidemiologic research
	in febrile seizures.
	pipeline_tag: sentence-similarity
	library_name: sentence-transformers
	metrics:
	- cosine_accuracy
	model-index:
	- name: MPNet base trained on AllNLI triplets
	results:
	- task:
	type: triplet
	name: Triplet
	dataset:
	name: eval dataset
	type: eval-dataset
	metrics:
	- type: cosine_accuracy
	value: 1.0
	name: Cosine Accuracy
	- task:
	type: triplet
	name: Triplet
	dataset:
	name: test dataset
	type: test-dataset
	metrics:
	- type: cosine_accuracy
	value: 0.97
	name: Cosine Accuracy
	---

	# MPNet base trained on AllNLI triplets

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/e5-base-v2](https://huggingface.co/intfloat/e5-base-v2). 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: [intfloat/e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) <!-- at revision 1c644c92ad3ba1efdad3f1451a637716616a20e8 -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 768 dimensions
	- Similarity Function: Cosine Similarity
	<!-- - Training Dataset: Unknown -->
	- 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({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
	(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})
	(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("sentence_transformers_model_id")
	# Run inference
	sentences = [
	'Is the Danish National Hospital Register a valuable study base for epidemiologic research in febrile seizures?',
	'The Danish National Hospital Register is a valuable tool for epidemiologic research in febrile seizures.',
	'Ans. is c i.e., Presence of depression Good prognostic factors Acute onset late onset onset after 35 years of age Presence of precipitating stressor Good premorbid adjustment catatonic best prognosis Paranoid 2nd best sho duration 6 months Married Positive symptoms Presence of depression family history of mood disorder first episode pyknic fat physique female sex good treatment compliance good response to treatment good social suppo presence of confusion or perplexity normal brain CT Scan outpatient treatment.',
	]
	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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	### 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

	#### Triplet

	* Datasets: `eval-dataset` and `test-dataset`
	* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)

	\| Metric \| eval-dataset \| test-dataset \|
	\|:--------------------\|:-------------\|:-------------\|
	\| cosine_accuracy \| 1.0 \| 0.97 \|

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	## 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

	#### Unnamed Dataset


	* Size: 800 training samples
	* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
	* Approximate statistics based on the first 800 samples:
	\| \| sentence1 \| sentence2 \| label \|
	\|:--------\|:-----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|:--------------------------------------------------------------\|
	\| type \| string \| string \| float \|
	\| details \| <ul><li>min: 5 tokens</li><li>mean: 22.88 tokens</li><li>max: 205 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 81.77 tokens</li><li>max: 512 tokens</li></ul> \| <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| sentence1 \| sentence2 \| label \|
	\|:------------------------------------------------------------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:-----------------\|
	\| <code>Triad of biotin deficiency is</code> \| <code>Dermatitis, glossitis, Alopecia 407 H 314 Basic pathology 8th Biotin deficiency clinical features Adult Mental changes depression, hallucination , paresthesia, anorexia, nausea, A scaling, seborrheic and erythematous rash may occur around the eye, nose, mouth, as well as extremities 407 H Infant hypotonia, lethargy, apathy, alopecia and a characteristic rash that includes the ears.Symptoms of biotin deficiency includes Anaemia, loss of apepite dermatitis, glossitis 150 U. Satyanarayan Symptoms of biotin deficiency Dermatitis spectacle eyed appearance due to circumocular alopecia, pallor of skin membrane, depression, Lassitude, somnolence, anemia and hypercholesterolaemia 173 Rana Shinde 6th</code> \| <code>1.0</code> \|
	\| <code>Drug responsible for the below condition</code> \| <code>Thalidomide given to pregnant lady can lead to hypoplasia of limbs called as Phocomelia .</code> \| <code>1.0</code> \|
	\| <code>Is benefit from procarbazine , lomustine , and vincristine in oligodendroglial tumors associated with mutation of IDH?</code> \| <code>IDH mutational status identified patients with oligodendroglial tumors who did and did not benefit from alkylating agent chemotherapy with RT. Although patients with codeleted tumors lived longest, patients with noncodeleted IDH mutated tumors also lived longer after CRT.</code> \| <code>1.0</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"
	}
	```

	### Evaluation Dataset

	#### Unnamed Dataset


	* Size: 100 evaluation samples
	* Columns: <code>question</code>, <code>answer</code>, and <code>hard_negative</code>
	* Approximate statistics based on the first 100 samples:
	\| \| question \| answer \| hard_negative \|
	\|:--------\|:-----------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------\|:-------------------\|
	\| type \| string \| string \| NoneType \|
	\| details \| <ul><li>min: 5 tokens</li><li>mean: 22.52 tokens</li><li>max: 103 tokens</li></ul> \| <ul><li>min: 10 tokens</li><li>mean: 83.51 tokens</li><li>max: 403 tokens</li></ul> \| <ul><li></li></ul> \|
	* Samples:
	\| question \| answer \| hard_negative \|
	\|:-----------------------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:------------------\|
	\| <code>Hutchinsons secondaries In skull are due to tumors in</code> \| <code>Adrenal neuroblastomas are malig8nant neoplasms arising from sympathetic neuroblsts in Medulla of adrenal gland Neuroblastoma is a cancer that develops from immature nerve cells found in several areas of the body.Neuroblastoma most commonly arises in and around the adrenalglands, which have similar origins to nerve cells and sit atop the kidneys.</code> \| <code>None</code> \|
	\| <code>Proliferative glomerular deposits in the kidney are found in</code> \| <code>IgA nephropathy or Berger s disease immune complex mediated glomerulonephritis defined by the presence of diffuse mesangial IgA deposits often associated with mesangial hypercellularity. Male preponderance, peak incidence in the second and third decades of life.Clinical and laboratory findings Two most common presentations recurrent episodes of macroscopic hematuria during or immediately following an upper respiratory infection often accompanied by proteinuria or persistent asymptomatic microscopic hematuriaIgA deposited in the mesangium is typically polymeric and of the IgA1 subclass. IgM, IgG, C3, or immunoglobulin light chains may be codistributed with IgAPresence of elevated serum IgA levels in 20 50 of patients, IgA deposition in skin biopsies in 15 55 of patients, elevated levels of secretory IgA and IgA fibronectin complexesIgA nephropathy is a benign disease mostly, 5 30 of patients go into a complete remission, with others having hematuria but well preserved renal functionAbou...</code> \| <code>None</code> \|
	\| <code>Does meconium aspiration induce oxidative injury in the hippocampus of newborn piglets?</code> \| <code>Our data thus suggest that oxidative injury associated with pulmonary, but not systemic, hemodynamic disturbances may contribute to hippocampal damage after meconium aspiration in newborns.</code> \| <code>None</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"
	}
	```

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

	- `do_predict`: True
	- `eval_strategy`: steps
	- `per_device_train_batch_size`: 16
	- `per_device_eval_batch_size`: 16
	- `num_train_epochs`: 1
	- `warmup_ratio`: 0.1
	- `fp16`: True
	- `load_best_model_at_end`: True
	- `batch_sampler`: no_duplicates

	#### All Hyperparameters
	<details><summary>Click to expand</summary>

	- `overwrite_output_dir`: False
	- `do_predict`: True
	- `eval_strategy`: steps
	- `prediction_loss_only`: True
	- `per_device_train_batch_size`: 16
	- `per_device_eval_batch_size`: 16
	- `per_gpu_train_batch_size`: None
	- `per_gpu_eval_batch_size`: None
	- `gradient_accumulation_steps`: 1
	- `eval_accumulation_steps`: None
	- `torch_empty_cache_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
	- `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`: 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`: 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
	- `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
	- `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`: None
	- `batch_sampler`: no_duplicates
	- `multi_dataset_batch_sampler`: proportional

	</details>

	### Training Logs
	\| Epoch \| Step \| eval-dataset_cosine_accuracy \| test-dataset_cosine_accuracy \|
	\|:-----:\|:----:\|:----------------------------:\|:----------------------------:\|
	\| 0 \| 0 \| 1.0 \| - \|
	\| 1.0 \| 25 \| - \| 0.97 \|


	### Framework Versions
	- Python: 3.11.10
	- Sentence Transformers: 3.3.0
	- Transformers: 4.46.2
	- PyTorch: 2.5.1+cu124
	- Accelerate: 1.1.1
	- Datasets: 3.1.0
	- Tokenizers: 0.20.3

	## 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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