flucold-ft-v1 / README.md

Add new SentenceTransformer model

bfdcee3 verified 23 days ago

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	---
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:400
	- loss:MatryoshkaLoss
	- loss:MultipleNegativesRankingLoss
	base_model: Snowflake/snowflake-arctic-embed-l
	widget:
	- source_sentence: 'QUESTION #2: What percentage of patients in the study reported
	experiencing "chills" and "feverish discomfort"?'
	sentences:
	- "been proven superior. Annual influenza vaccination is recommended for all people\
	\ six months and older who do not have \ncontraindications. ( Am Fam Physician.\
	\ 2019; 100(12):751-758. Copyright © 2019 American Academy of Family Physicians.)\n\
	BEST PRACTICES IN INFECTIOUS DISEASE \nRecommendations from the Choosing \nWisely\
	\ Campaign\nRecommendation Sponsoring organization\nDo not routinely avoid \n\
	influenza vaccination in \negg-allergic patients.\nAmerican Academy of Allergy,\
	\ \nAsthma, and Immunology\nSource: For more information on the Choosing Wisely\
	\ Campaign,"
	- 'Review

	722 Vol 5 November 2005

	accompanied by fever and some subjects have a transient

	fall in body temperature during the early stages of

	common cold. In a study of 272 patients with sore throat

	associated with URTIs, the mean aural temperature was

	36·8ºC and around 35% of these patients said they were

	suffering from “chills” and “feverish discomfort”.49 The

	sensation of chilliness may be unrelated to any change in

	skin or body temperature. In a study of human

	volunteers, a sensation of chill still develops on

	administration of exogenous pyrogen even though the'
	- "ered when the results will modify management or when a \npatient with signs or\
	\ symptoms of influenza is hospitalized.19 \nTABLE 2\nComplications of Influenza\n\
	Cardiovascular 26\nCerebrovascular accidents\nIschemic heart disease\nMyocarditis\n\
	Hematologic 26\nHemolytic uremic syndrome\nHemophagocytic syndrome\nThrombotic\
	\ thrombocytope -\nnic purpura\nMusculoskeletal 19,26\nMyositis\nRhabdomyolysis\n\
	Neurologic 26\nAcute disseminated \nencephalomyelitis\nEncephalitis\nGuillain-Barré\
	\ syndrome\nPostinfluenza encephalopathy \n(neurologic symptoms occur -\nring\
	\ after resolution but within"
	- source_sentence: How do cytokines interact with the body's systems to influence
	the hypothalamus and affect body temperature?
	sentences:
	- 'interleukin 1, interleukin 6, and tumour necrosis factor

	alpha, as well as the anti-inﬂammatory cytokines

	interleukin-1 receptor antagonist and interleukin 10

	have been investigated for their pyrogenic or antipyretic

	action.17 Interleukin 1 and interleukin 6 are believed to

	be the most important cytokines that induce fever. 55

	Cytokines are believed to cross the blood–brain barrier

	or interact with the vagus nerve endings to signal the

	temperature control centre of the hypothalamus to

	increase the thermal set point.55,56 The hypothalamus

	then initiates shivering, constriction of skin blood'
	- "mended human dose; possible \nrisk of embryo-fetal toxicity with \ncontinuous\
	\ intravenous infusion \nbased on limited animal data\nBaloxavir (Xofluza), \n\
	available as oral \ntablets\nNA ($160) Adults and children 12 years \nand older:\
	\ \n88 to 174 lb (40 to 79 kg): \nsingle dose of 40 mg \n≥ 175 lb (80 kg):\
	\ single dose \nof 80 mg\nTreatment of uncom-\nplicated acute \ninfluenza in\
	\ patients \n12 years and older who \nhave been symptom -\natic for no more than\
	\ \n48 hours\nContraindicated in people with \na history of hypersensitivity to\
	\ \nbaloxavir or any component of the \nproduct"
	- "CME This clinical content conforms to AAFP criteria for con-\ntinuing medical\
	\ education (CME). See CME Quiz on page 271.\nAuthor disclosure: No relevant\
	\ financial affiliations.\nPatient information: Handouts on this topic, written\
	\ by the \nauthors of this article, are available at https:// www.aafp.org/\n\
	afp/2019/0901/p281-s1.html and https:// www.aafp.org/\nafp/2019/0901/p281-s2.html.\n\
	Acute upper respiratory tract infections are extremely common in adults and children,\
	\ but only a few safe and effective treat-"
	- source_sentence: What are the limitations of using adamantanes (amantadine and rimantadine)
	for influenza treatment according to the context?
	sentences:
	- "December 15, 2019 ◆ Volume 100, Number 12 www.aafp.org/afp American Family Physician\
	\ 755\nINFLUENZA\nClinicians caring for high-risk patients can also be consid\
	\ -\nered for treatment.28\nFour antiviral drugs have been approved for the treat\
	\ -\nment of influenza (Table 4): the NA inhibitors oseltamivir \n(Tamiflu),\
	\ zanamivir (Relenza), and peramivir (Rapivab), \nand the cap-dependent endonuclease\
	\ inhibitor baloxa -\nvir (Xofluza). 18,37 Any of these agents can be used in\
	\ age- \nappropriate, otherwise healthy outpatients with uncom -\nplicated influenza\
	\ and no contraindications. 18 Baloxavir is"
	- "756 American Family Physician www.aafp.org/afp Volume 100, Number 12 ◆ December\
	\ 15, 2019\nINFLUENZA\nthe risk of bronchospasm. 18,28 Adamantanes (amantadine\
	\ \nand rimantadine [Flumadine]) are approved for influenza \ntreatment but are\
	\ not currently recommended. These med -\nications are not active against influenza\
	\ B, and most influ -\nenza A strains have shown adamantane resistance for the\
	\ \npast 10 years.18\nThere is no demonstrated benefit to treating patients \n\
	with more than one antiviral agent or using higher than \nrecommended dosages.\
	\ 28 However, extended treatment"
	- "distress syndrome\nDiffuse alveolar \nhemorrhage\nHypoxic respiratory \nfailure\n\
	Primary viral pneumonia\nSecondary bacterial \npneumonia\nRenal 26\nAcute kidney\
	\ injury \n(e.g., acute tubulo- \ninterstitial nephritis, \nglomerulonephritis,\
	\ \nminimal change disease)\nMultiorgan failure\nInformation from references 8,\
	\ 19, and 25-27.\nSORT: KEY RECOMMENDATIONS FOR PRACTICE\nClinical recommendation\n\
	Evidence \nrating Comments\nAnnual influenza vaccination is recommended for all\
	\ people 6 months and older. 15,16 A Reports of expert committees"
	- source_sentence: Which symptoms of colds and flu are now better understood due to
	new knowledge in molecular biology?
	sentences:
	- 'mechanisms that generate the familiar symptoms is poor compared with the amount
	of knowledge available on the

	molecular biology of the viruses involved. New knowledge of the effects of cytokines
	in human beings now helps to

	explain some of the symptoms of colds and ﬂu that were previously in the realm
	of folklore rather than medicine—

	eg, fever, anorexia, malaise, chilliness, headache, and muscle aches and pains.
	The mechanisms of symptoms of

	sore throat, rhinorrhoea, sneezing, nasal congestion, cough, watery eyes, and
	sinus pain are discussed, since these'
	- 'medicines such as ipratropium. These studies have

	demonstrated that nasal secretions in the ﬁrst 4 days of a

	common cold are inhibited by intranasal administration

	of ipratropium.25 The nasal discharge also consists of a

	protein-rich plasma exudate derived from subepithelial

	capillaries,28 which may explain why anticholinergics

	only partly inhibit nasal discharge associated with

	URTIs.27

	The colour of nasal discharge and sputum is often

	used as a clinical marker to determine whether or not to

	prescribe antibiotics but there is no evidence from the'
	- "ing diffuse alveolar hemorrhage in immunocompetent patients: a state-\nof-the-art\
	\ review. Lung. 2013; 191(1): 9-18.\n 28. Uyeki TM, Bernstein HH, Bradley JS,\
	\ et al. Clinical practice guidelines by \nthe Infectious Diseases Society of\
	\ America: 2018 update on diagnosis, \ntreatment, chemoprophylaxis, and institutional\
	\ outbreak management \nof seasonal influenza. Clin Infect Dis. 2019; 68(6): 895-902.\n\
	\ 29. Ebell MH, Afonso AM, Gonzales R, et al. Development and validation of \n\
	a clinical decision rule for the diagnosis of influenza. J Am Board Fam \nMed.\
	\ 2012; 25(1): 55-62."
	- source_sentence: 'QUESTION #2: How does the sneeze centre in the brainstem coordinate
	the actions involved in sneezing?'
	sentences:
	- "stroke, seizure disorder, dementia)\nAsthma or other chronic pulmonary disease\n\
	Chronic kidney disease\nChronic liver disease\nHeart disease (acquired or congenital)\n\
	Immunosuppression (e.g., HIV infection, cancer, transplant \nrecipients, use of\
	\ immunosuppressive medications)\nLong-term aspirin therapy in patients younger\
	\ than 19 years\nMetabolic disorders (acquired [e.g., diabetes mellitus] or \n\
	inherited [e.g., mitochondrial disorders])\nMorbid obesity\nSickle cell anemia\
	\ and other hemoglobinopathies\nSpecial groups\nAdults 65 years and older\nAmerican\
	\ Indians and Alaska Natives"
	- 'causes sneezing.23 The trigeminal nerves relay

	information to the sneeze centre in the brainstem and

	cause reﬂex activation of motor and parasympathetic

	branches of the facial nerve and activate respiratory

	muscles. A model of the sneeze reﬂex is illustrated in

	ﬁgure 1. The sneeze centre coordinates the inspiratory

	and expiratory actions of sneezing via respiratory

	muscles, and lacrimation and nasal congestion via

	parasympathetic branches of the facial nerve. The eyes

	are always closed during sneezing by activation of facial

	muscles, indicating a close relation between the'
	- 'during experimental rhinovirus infections have not

	been able to ﬁnd any morphological changes in the

	nasal epithelium of infected volunteers, apart from a

	substantial increase in polymorphonuclear leucocytes

	early in the course of the infection.11 The major cell

	monitoring the host for the invasion of pathogens is

	the macrophage, which has the ability to trigger an

	acute phase response when stimulated with

	components of viruses or bacteria—eg, viral RNA and

	bacterial cell wall components.12 The surface of the

	macrophage exhibits toll-like receptors that combine'
	pipeline_tag: sentence-similarity
	library_name: sentence-transformers
	metrics:
	- cosine_accuracy@1
	- cosine_accuracy@3
	- cosine_accuracy@5
	- cosine_accuracy@10
	- cosine_precision@1
	- cosine_precision@3
	- cosine_precision@5
	- cosine_precision@10
	- cosine_recall@1
	- cosine_recall@3
	- cosine_recall@5
	- cosine_recall@10
	- cosine_ndcg@10
	- cosine_mrr@10
	- cosine_map@100
	model-index:
	- name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-l
	results:
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: Unknown
	type: unknown
	metrics:
	- type: cosine_accuracy@1
	value: 0.6122448979591837
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.8877551020408163
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.9387755102040817
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.9897959183673469
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.6122448979591837
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.29591836734693877
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.1877551020408163
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.09897959183673469
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.6122448979591837
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.8877551020408163
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.9387755102040817
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.9897959183673469
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.8165441473931409
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.7593091998704244
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.7600380628441854
	name: Cosine Map@100
	---

	# SentenceTransformer based on Snowflake/snowflake-arctic-embed-l

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

	## Model Details

	### Model Description
	- Model Type: Sentence Transformer
	- Base model: [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l) <!-- at revision d8fb21ca8d905d2832ee8b96c894d3298964346b -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 1024 dimensions
	- Similarity Function: Cosine Similarity
	<!-- - Training Dataset: Unknown -->
	<!-- - 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': 512, 'do_lower_case': False}) with Transformer model: BertModel
	(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
	(2): Normalize()
	)
	```

	## Usage

	### Direct Usage (Sentence Transformers)

	First install the Sentence Transformers library:

	```bash
	pip install -U sentence-transformers
	```

	Then you can load this model and run inference.
	```python
	from sentence_transformers import SentenceTransformer

	# Download from the 🤗 Hub
	model = SentenceTransformer("Gonalb/flucold-ft-v1")
	# Run inference
	sentences = [
	'QUESTION #2: How does the sneeze centre in the brainstem coordinate the actions involved in sneezing?',
	'causes sneezing.23 The trigeminal nerves relay\ninformation to the sneeze centre in the brainstem and\ncause reﬂex activation of motor and parasympathetic\nbranches of the facial nerve and activate respiratory\nmuscles. A model of the sneeze reﬂex is illustrated in\nﬁgure 1. The sneeze centre coordinates the inspiratory\nand expiratory actions of sneezing via respiratory\nmuscles, and lacrimation and nasal congestion via\nparasympathetic branches of the facial nerve. The eyes\nare always closed during sneezing by activation of facial\nmuscles, indicating a close relation between the',
	'stroke, seizure disorder, dementia)\nAsthma or other chronic pulmonary disease\nChronic kidney disease\nChronic liver disease\nHeart disease (acquired or congenital)\nImmunosuppression (e.g., HIV infection, cancer, transplant \nrecipients, use of immunosuppressive medications)\nLong-term aspirin therapy in patients younger than 19 years\nMetabolic disorders (acquired [e.g., diabetes mellitus] or \ninherited [e.g., mitochondrial disorders])\nMorbid obesity\nSickle cell anemia and other hemoglobinopathies\nSpecial groups\nAdults 65 years and older\nAmerican Indians and Alaska Natives',
	]
	embeddings = model.encode(sentences)
	print(embeddings.shape)
	# [3, 1024]

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

	<!--
	### 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>
	-->

	<!--
	### Out-of-Scope Use

	List how the model may foreseeably be misused and address what users ought not to do with the model.
	-->

	## Evaluation

	### Metrics

	#### Information Retrieval

	* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| cosine_accuracy@1 \| 0.6122 \|
	\| cosine_accuracy@3 \| 0.8878 \|
	\| cosine_accuracy@5 \| 0.9388 \|
	\| cosine_accuracy@10 \| 0.9898 \|
	\| cosine_precision@1 \| 0.6122 \|
	\| cosine_precision@3 \| 0.2959 \|
	\| cosine_precision@5 \| 0.1878 \|
	\| cosine_precision@10 \| 0.099 \|
	\| cosine_recall@1 \| 0.6122 \|
	\| cosine_recall@3 \| 0.8878 \|
	\| cosine_recall@5 \| 0.9388 \|
	\| cosine_recall@10 \| 0.9898 \|
	\| cosine_ndcg@10 \| 0.8165 \|
	\| cosine_mrr@10 \| 0.7593 \|
	\| cosine_map@100 \| 0.76 \|

	<!--
	## 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

	#### Unnamed Dataset

	* Size: 400 training samples
	* Columns: <code>sentence_0</code> and <code>sentence_1</code>
	* Approximate statistics based on the first 400 samples:
	\| \| sentence_0 \| sentence_1 \|
	\|:--------\|:-----------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------\|
	\| type \| string \| string \|
	\| details \| <ul><li>min: 14 tokens</li><li>mean: 24.87 tokens</li><li>max: 53 tokens</li></ul> \| <ul><li>min: 44 tokens</li><li>mean: 129.25 tokens</li><li>max: 188 tokens</li></ul> \|
	* Samples:
	\| sentence_0 \| sentence_1 \|
	\|:----------------------------------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>What is the recommended age for annual influenza vaccination according to the context?</code> \| <code>recommend annual influenza vaccination for all people six <br>months and older who do not have contraindications. 15,16 <br>Vaccination efforts should target people at increased risk of <br>complicated or severe influenza (Table 117-19) and those who <br>care for or live with high-risk individuals, including health <br>care professionals. 15 Two previous FPM articles provided <br>communication strategies and tools for increasing influenza <br>vaccination rates in practice. 20,21<br>Multiple formulations of the influenza vaccine are avail -<br>able, including inactivated influenza vaccines (IIV); a recom-</code> \|
	\| <code>Who should vaccination efforts specifically target to prevent complicated or severe influenza?</code> \| <code>recommend annual influenza vaccination for all people six <br>months and older who do not have contraindications. 15,16 <br>Vaccination efforts should target people at increased risk of <br>complicated or severe influenza (Table 117-19) and those who <br>care for or live with high-risk individuals, including health <br>care professionals. 15 Two previous FPM articles provided <br>communication strategies and tools for increasing influenza <br>vaccination rates in practice. 20,21<br>Multiple formulations of the influenza vaccine are avail -<br>able, including inactivated influenza vaccines (IIV); a recom-</code> \|
	\| <code>What types of studies were included in the search regarding influenza complications and treatment?</code> \| <code>enza complications American Indians, influenza treatment, and <br>influenza universal vaccine. The search included meta-analyses, <br>randomized controlled trials, clinical trials, and reviews. Search <br>dates: December 1, 2018, to October 5, 2019.<br>The Authors<br>DAVID Y. GAITONDE, MD, is a core clinical faculty member <br>and chief of endocrinology service at Dwight D. Eisenhower <br>Army Medical Center, Fort Gordon, Ga.<br>CPT. FAITH C. MOORE, USA, MC, is a resident in the Depart -<br>ment of Internal Medicine at Dwight D. Eisenhower Army <br>Medical Center.<br>MAJ. MACKENZIE K. MORGAN, USA, MC, is chief of infec-</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

	- `eval_strategy`: steps
	- `per_device_train_batch_size`: 10
	- `per_device_eval_batch_size`: 10
	- `num_train_epochs`: 10
	- `multi_dataset_batch_sampler`: round_robin

	#### 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`: 10
	- `per_device_eval_batch_size`: 10
	- `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
	- `num_train_epochs`: 10
	- `max_steps`: -1
	- `lr_scheduler_type`: linear
	- `lr_scheduler_kwargs`: {}
	- `warmup_ratio`: 0.0
	- `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`: 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, '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`: None
	- `hub_always_push`: False
	- `gradient_checkpointing`: False
	- `gradient_checkpointing_kwargs`: None
	- `include_inputs_for_metrics`: False
	- `include_for_metrics`: []
	- `eval_do_concat_batches`: True
	- `fp16_backend`: auto
	- `push_to_hub_model_id`: None
	- `push_to_hub_organization`: None
	- `mp_parameters`:
	- `auto_find_batch_size`: False
	- `full_determinism`: False
	- `torchdynamo`: None
	- `ray_scope`: last
	- `ddp_timeout`: 1800
	- `torch_compile`: False
	- `torch_compile_backend`: None
	- `torch_compile_mode`: None
	- `dispatch_batches`: None
	- `split_batches`: None
	- `include_tokens_per_second`: False
	- `include_num_input_tokens_seen`: False
	- `neftune_noise_alpha`: None
	- `optim_target_modules`: None
	- `batch_eval_metrics`: False
	- `eval_on_start`: False
	- `use_liger_kernel`: False
	- `eval_use_gather_object`: False
	- `average_tokens_across_devices`: False
	- `prompts`: None
	- `batch_sampler`: batch_sampler
	- `multi_dataset_batch_sampler`: round_robin

	</details>

	### Training Logs
	\| Epoch \| Step \| cosine_ndcg@10 \|
	\|:-----:\|:----:\|:--------------:\|
	\| 1.0 \| 40 \| 0.8359 \|
	\| 1.25 \| 50 \| 0.8312 \|
	\| 2.0 \| 80 \| 0.8304 \|
	\| 2.5 \| 100 \| 0.8156 \|
	\| 3.0 \| 120 \| 0.8016 \|
	\| 3.75 \| 150 \| 0.7952 \|
	\| 4.0 \| 160 \| 0.7880 \|
	\| 5.0 \| 200 \| 0.8021 \|
	\| 6.0 \| 240 \| 0.8215 \|
	\| 6.25 \| 250 \| 0.8286 \|
	\| 7.0 \| 280 \| 0.8079 \|
	\| 7.5 \| 300 \| 0.8043 \|
	\| 8.0 \| 320 \| 0.8126 \|
	\| 8.75 \| 350 \| 0.8099 \|
	\| 9.0 \| 360 \| 0.8126 \|
	\| 10.0 \| 400 \| 0.8165 \|


	### Framework Versions
	- Python: 3.11.11
	- Sentence Transformers: 3.4.1
	- Transformers: 4.48.3
	- PyTorch: 2.5.1+cu124
	- Accelerate: 1.3.0
	- Datasets: 3.3.2
	- Tokenizers: 0.21.0

	## Citation

	### BibTeX

	#### Sentence Transformers
	```bibtex
	@inproceedings{reimers-2019-sentence-bert,
	title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
	author = "Reimers, Nils and Gurevych, Iryna",
	booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
	month = "11",
	year = "2019",
	publisher = "Association for Computational Linguistics",
	url = "https://arxiv.org/abs/1908.10084",
	}
	```

	#### 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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