finetuned_arctic / README.md

Add new SentenceTransformer model.

2d373f7 verified about 2 months ago

31.9 kB

	---
	base_model: Snowflake/snowflake-arctic-embed-m
	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
	- dot_accuracy@1
	- dot_accuracy@3
	- dot_accuracy@5
	- dot_accuracy@10
	- dot_precision@1
	- dot_precision@3
	- dot_precision@5
	- dot_precision@10
	- dot_recall@1
	- dot_recall@3
	- dot_recall@5
	- dot_recall@10
	- dot_ndcg@10
	- dot_mrr@10
	- dot_map@100
	pipeline_tag: sentence-similarity
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:800
	- loss:MatryoshkaLoss
	- loss:MultipleNegativesRankingLoss
	widget:
	- source_sentence: What is the importance of having a human fallback system in automated
	systems, especially for the American public?
	sentences:
	- "ing a system from use. Automated systems should not be designed \nwith an intent\
	\ or reasonably foreseeable possibility of endangering \nyour safety or the safety\
	\ of your community. They should be designed \nto proactively protect you from\
	\ harms stemming from unintended, \nyet foreseeable, uses or impacts of automated\
	\ systems. You should be \nprotected from inappropriate or irrelevant data use\
	\ in the design, de\nvelopment, and deployment of automated systems, and from\
	\ the \ncompounded harm of its reuse. Independent evaluation and report\ning\
	\ that confirms that the system is safe and effective, including re\nporting\
	\ of steps taken to mitigate potential harms, should be per\nformed and the results\
	\ made public whenever possible. \n15"
	- "with disabilities. \nIn addition to being able to opt out and use a human alternative,\
	\ the American public deserves a human fallback \nsystem in the event that an\
	\ automated system fails or causes harm. No matter how rigorously an automated\
	\ system is \ntested, there will always be situations for which the system fails.\
	\ The American public deserves protection via human \nreview against these outlying\
	\ or unexpected scenarios. In the case of time-critical systems, the public should\
	\ not have \nto wait—immediate human consideration and fallback should be available.\
	\ In many time-critical systems, such a \nremedy is already immediately available,\
	\ such as a building manager who can open a door in the case an automated \ncard\
	\ access system fails."
	- "information used to build or validate the risk assessment shall be open to public\
	\ inspection,\" and that assertions \nof trade secrets cannot be used \"to quash\
	\ discovery in a criminal matter by a party to a criminal case.\" \n22"
	- source_sentence: What type of information is required to be open to public inspection
	in relation to risk assessment?
	sentences:
	- "HOW THESE PRINCIPLES CAN MOVE INTO PRACTICE\nReal-life examples of how these\
	\ principles can become reality, through laws, policies, and practical \ntechnical\
	\ and sociotechnical approaches to protecting rights, opportunities, and access.\
	\ \nThe federal government is working to combat discrimination in mortgage lending.\
	\ The Depart\nment of Justice has launched a nationwide initiative to combat\
	\ redlining, which includes reviewing how \nlenders who may be avoiding serving\
	\ communities of color are conducting targeted marketing and advertising.51 \n\
	This initiative will draw upon strong partnerships across federal agencies, including\
	\ the Consumer Financial"
	- "reuse \nRelevant and high-quality data. Data used as part of any automated system’s\
	\ creation, evaluation, or \ndeployment should be relevant, of high quality, and\
	\ tailored to the task at hand. Relevancy should be \nestablished based on research-backed\
	\ demonstration of the causal influence of the data to the specific use case \n\
	or justified more generally based on a reasonable expectation of usefulness in\
	\ the domain and/or for the \nsystem design or ongoing development. Relevance\
	\ of data should not be established solely by appealing to \nits historical connection\
	\ to the outcome. High quality and tailored data should be representative of the\
	\ task at"
	- "information used to build or validate the risk assessment shall be open to public\
	\ inspection,\" and that assertions \nof trade secrets cannot be used \"to quash\
	\ discovery in a criminal matter by a party to a criminal case.\" \n22"
	- source_sentence: Who is the Senior Policy Advisor for Data and Democracy at the
	White House Office of Science and Technology Policy?
	sentences:
	- "products, advanced platforms and services, “Internet of Things” (IoT) devices,\
	\ and smart city products and \nservices. \nWelcome:\n•\nRashida Richardson, Senior\
	\ Policy Advisor for Data and Democracy, White House Office of Science and\nTechnology\
	\ Policy\n•\nKaren Kornbluh, Senior Fellow and Director of the Digital Innovation\
	\ and Democracy Initiative, German\nMarshall Fund\nModerator: \nDevin E. Willis,\
	\ Attorney, Division of Privacy and Identity Protection, Bureau of Consumer Protection,\
	\ Federal \nTrade Commission \nPanelists: \n•\nTamika L. Butler, Principal, Tamika\
	\ L. Butler Consulting\n•\nJennifer Clark, Professor and Head of City and Regional\
	\ Planning, Knowlton School of Engineering, Ohio\nState University\n•"
	- 'ENDNOTES

	35. Carrie Johnson. Flaws plague a tool meant to help low-risk federal prisoners
	win early release. NPR.

	Jan. 26, 2022. https://www.npr.org/2022/01/26/1075509175/flaws-plague-a-tool-meant-to-help-low

	risk-federal-prisoners-win-early-release.; Carrie Johnson. Justice Department
	works to curb racial bias

	in deciding who''s released from prison. NPR. Apr. 19, 2022. https://

	www.npr.org/2022/04/19/1093538706/justice-department-works-to-curb-racial-bias-in-deciding

	whos-released-from-pris; National Institute of Justice. 2021 Review and Revalidation
	of the First Step Act

	Risk Assessment Tool. National Institute of Justice NCJ 303859. Dec., 2021. https://www.ojp.gov/

	pdffiles1/nij/303859.pdf'
	- 'https://themarkup.org/machine-learning/2022/01/11/this-private-equity-firm-is-amassing-companies

	that-collect-data-on-americas-children

	77. Reed Albergotti. Every employee who leaves Apple becomes an ‘associate’: In
	job databases used by

	employers to verify resume information, every former Apple employee’s title gets
	erased and replaced with

	a generic title. The Washington Post. Feb. 10, 2022.

	https://www.washingtonpost.com/technology/2022/02/10/apple-associate/

	78. National Institute of Standards and Technology. Privacy Framework Perspectives
	and Success

	Stories. Accessed May 2, 2022.

	https://www.nist.gov/privacy-framework/getting-started-0/perspectives-and-success-stories'
	- source_sentence: What actions has the Consumer Financial Protection Bureau taken
	regarding black-box credit models?
	sentences:
	- 'under-ecoa-fcra/

	91. Federal Trade Commission. Using Consumer Reports for Credit Decisions: What
	to Know About

	Adverse Action and Risk-Based Pricing Notices. Accessed May 2, 2022.

	https://www.ftc.gov/business-guidance/resources/using-consumer-reports-credit-decisions-what

	know-about-adverse-action-risk-based-pricing-notices#risk

	92. Consumer Financial Protection Bureau. CFPB Acts to Protect the Public from
	Black-Box Credit

	Models Using Complex Algorithms. May 26, 2022.

	https://www.consumerfinance.gov/about-us/newsroom/cfpb-acts-to-protect-the-public-from-black

	box-credit-models-using-complex-algorithms/

	93. Anthony Zaller. California Passes Law Regulating Quotas In Warehouses – What
	Employers Need to'
	- 'https://www.nytimes.com/2020/12/29/technology/facial-recognition-misidentify-jail.html;
	Khari

	Johnson. How Wrongful Arrests Based on AI Derailed 3 Men''s Lives. Wired. Mar.
	7, 2022. https://

	www.wired.com/story/wrongful-arrests-ai-derailed-3-mens-lives/

	32. Student Borrower Protection Center. Educational Redlining. Student Borrower
	Protection Center

	Report. Feb. 2020. https://protectborrowers.org/wp-content/uploads/2020/02/Education-Redlining

	Report.pdf

	33. Jeffrey Dastin. Amazon scraps secret AI recruiting tool that showed bias against
	women. Reuters. Oct.

	10, 2018. https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps

	secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G'
	- "including automated tenant background screening and facial recognition-based\
	\ controls to enter or exit \nhousing complexes. Employment-related concerning\
	\ uses included discrimination in automated hiring \nscreening and workplace surveillance.\
	\ Various panelists raised the limitations of existing privacy law as a key \n\
	concern, pointing out that students should be able to reinvent themselves and\
	\ require privacy of their student \nrecords and education-related data in order\
	\ to do so. The overarching concerns of surveillance in these \ndomains included\
	\ concerns about the chilling effects of surveillance on student expression, inappropriate"
	- source_sentence: What percentage of racy results did Google cut for searches like
	'Latina teenager' in March 2022?
	sentences:
	- "they've used drugs, or whether they've expressed interest in LGBTQI+ groups,\
	\ and then use that data to \nforecast student success.76 Parents and education\
	\ experts have expressed concern about collection of such\nsensitive data without\
	\ express parental consent, the lack of transparency in how such data is being\
	\ used, and\nthe potential for resulting discriminatory impacts.\n• Many employers\
	\ transfer employee data to third party job verification services. This information\
	\ is then used\nby potential future employers, banks, or landlords. In one case,\
	\ a former employee alleged that a\ncompany supplied false data about her job\
	\ title which resulted in a job offer being revoked.77\n37"
	- 'Software Discriminates Against Disabled Students. Center for Democracy and Technology.
	Nov. 16, 2020.

	https://cdt.org/insights/how-automated-test-proctoring-software-discriminates-against-disabled

	students/

	46. Ziad Obermeyer, et al., Dissecting racial bias in an algorithm used to manage
	the health of

	populations, 366 Science (2019), https://www.science.org/doi/10.1126/science.aax2342.

	66'
	- '2022. https://www.reuters.com/technology/google-cuts-racy-results-by-30-searches-like-latina

	teenager-2022-03-30/

	40. Safiya Umoja Noble. Algorithms of Oppression: How Search Engines Reinforce
	Racism. NYU Press.

	Feb. 2018. https://nyupress.org/9781479837243/algorithms-of-oppression/

	41. Paresh Dave. Google cuts racy results by 30% for searches like ''Latina teenager''.
	Reuters. Mar. 30,

	2022. https://www.reuters.com/technology/google-cuts-racy-results-by-30-searches-like-latina

	teenager-2022-03-30/

	42. Miranda Bogen. All the Ways Hiring Algorithms Can Introduce Bias. Harvard
	Business Review. May

	6, 2019. https://hbr.org/2019/05/all-the-ways-hiring-algorithms-can-introduce-bias'
	model-index:
	- name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-m
	results:
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: Unknown
	type: unknown
	metrics:
	- type: cosine_accuracy@1
	value: 0.815
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.935
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.95
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.965
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.815
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.31166666666666665
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.19
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.09649999999999999
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.815
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.935
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.95
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.965
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.8954135083695783
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.8723333333333333
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.8741632101558571
	name: Cosine Map@100
	- type: dot_accuracy@1
	value: 0.815
	name: Dot Accuracy@1
	- type: dot_accuracy@3
	value: 0.935
	name: Dot Accuracy@3
	- type: dot_accuracy@5
	value: 0.95
	name: Dot Accuracy@5
	- type: dot_accuracy@10
	value: 0.965
	name: Dot Accuracy@10
	- type: dot_precision@1
	value: 0.815
	name: Dot Precision@1
	- type: dot_precision@3
	value: 0.31166666666666665
	name: Dot Precision@3
	- type: dot_precision@5
	value: 0.19
	name: Dot Precision@5
	- type: dot_precision@10
	value: 0.09649999999999999
	name: Dot Precision@10
	- type: dot_recall@1
	value: 0.815
	name: Dot Recall@1
	- type: dot_recall@3
	value: 0.935
	name: Dot Recall@3
	- type: dot_recall@5
	value: 0.95
	name: Dot Recall@5
	- type: dot_recall@10
	value: 0.965
	name: Dot Recall@10
	- type: dot_ndcg@10
	value: 0.8954135083695783
	name: Dot Ndcg@10
	- type: dot_mrr@10
	value: 0.8723333333333333
	name: Dot Mrr@10
	- type: dot_map@100
	value: 0.8741632101558571
	name: Dot Map@100
	---

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

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m). 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: [Snowflake/snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m) <!-- at revision e2b128b9fa60c82b4585512b33e1544224ffff42 -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 768 tokens
	- 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': 768, '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("acpotts/finetuned_arctic")
	# Run inference
	sentences = [
	"What percentage of racy results did Google cut for searches like 'Latina teenager' in March 2022?",
	"2022. https://www.reuters.com/technology/google-cuts-racy-results-by-30-searches-like-latina\xad\nteenager-2022-03-30/\n40. Safiya Umoja Noble. Algorithms of Oppression: How Search Engines Reinforce Racism. NYU Press.\nFeb. 2018. https://nyupress.org/9781479837243/algorithms-of-oppression/\n41. Paresh Dave. Google cuts racy results by 30% for searches like 'Latina teenager'. Reuters. Mar. 30,\n2022. https://www.reuters.com/technology/google-cuts-racy-results-by-30-searches-like-latina\xad\nteenager-2022-03-30/\n42. Miranda Bogen. All the Ways Hiring Algorithms Can Introduce Bias. Harvard Business Review. May\n6, 2019. https://hbr.org/2019/05/all-the-ways-hiring-algorithms-can-introduce-bias",
	"they've used drugs, or whether they've expressed interest in LGBTQI+ groups, and then use that data to \nforecast student success.76 Parents and education experts have expressed concern about collection of such\nsensitive data without express parental consent, the lack of transparency in how such data is being used, and\nthe potential for resulting discriminatory impacts.\n• Many employers transfer employee data to third party job verification services. This information is then used\nby potential future employers, banks, or landlords. In one case, a former employee alleged that a\ncompany supplied false data about her job title which resulted in a job offer being revoked.77\n37",
	]
	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]
	```

	<!--
	### 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 \| 0.815 \|
	\| cosine_accuracy@3 \| 0.935 \|
	\| cosine_accuracy@5 \| 0.95 \|
	\| cosine_accuracy@10 \| 0.965 \|
	\| cosine_precision@1 \| 0.815 \|
	\| cosine_precision@3 \| 0.3117 \|
	\| cosine_precision@5 \| 0.19 \|
	\| cosine_precision@10 \| 0.0965 \|
	\| cosine_recall@1 \| 0.815 \|
	\| cosine_recall@3 \| 0.935 \|
	\| cosine_recall@5 \| 0.95 \|
	\| cosine_recall@10 \| 0.965 \|
	\| cosine_ndcg@10 \| 0.8954 \|
	\| cosine_mrr@10 \| 0.8723 \|
	\| cosine_map@100 \| 0.8742 \|
	\| dot_accuracy@1 \| 0.815 \|
	\| dot_accuracy@3 \| 0.935 \|
	\| dot_accuracy@5 \| 0.95 \|
	\| dot_accuracy@10 \| 0.965 \|
	\| dot_precision@1 \| 0.815 \|
	\| dot_precision@3 \| 0.3117 \|
	\| dot_precision@5 \| 0.19 \|
	\| dot_precision@10 \| 0.0965 \|
	\| dot_recall@1 \| 0.815 \|
	\| dot_recall@3 \| 0.935 \|
	\| dot_recall@5 \| 0.95 \|
	\| dot_recall@10 \| 0.965 \|
	\| dot_ndcg@10 \| 0.8954 \|
	\| dot_mrr@10 \| 0.8723 \|
	\| dot_map@100 \| 0.8742 \|

	<!--
	## 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: 800 training samples
	* Columns: <code>sentence_0</code> and <code>sentence_1</code>
	* Approximate statistics based on the first 800 samples:
	\| \| sentence_0 \| sentence_1 \|
	\|:--------\|:-----------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------\|
	\| type \| string \| string \|
	\| details \| <ul><li>min: 11 tokens</li><li>mean: 20.11 tokens</li><li>max: 36 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 127.42 tokens</li><li>max: 512 tokens</li></ul> \|
	* Samples:
	\| sentence_0 \| sentence_1 \|
	\|:--------------------------------------------------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>What are some of the principles proposed for the ethical use of AI and automated systems?</code> \| <code>lems with legislation, and some courts extending longstanding statutory protections to new and emerging tech<br>nologies. There are companies working to incorporate additional protections in their design and use of auto<br>mated systems, and researchers developing innovative guardrails. Advocates, researchers, and government <br>organizations have proposed principles for the ethical use of AI and other automated systems. These include <br>the Organization for Economic Co-operation and Development’s (OECD’s) 2019 Recommendation on Artificial <br>Intelligence, which includes principles for responsible stewardship of trustworthy AI and which the United</code> \|
	\| <code>How are companies and researchers addressing the challenges posed by new and emerging technologies in relation to legislation?</code> \| <code>lems with legislation, and some courts extending longstanding statutory protections to new and emerging tech<br>nologies. There are companies working to incorporate additional protections in their design and use of auto<br>mated systems, and researchers developing innovative guardrails. Advocates, researchers, and government <br>organizations have proposed principles for the ethical use of AI and other automated systems. These include <br>the Organization for Economic Co-operation and Development’s (OECD’s) 2019 Recommendation on Artificial <br>Intelligence, which includes principles for responsible stewardship of trustworthy AI and which the United</code> \|
	\| <code>What is the purpose of reporting summary information about automated systems in plain language?</code> \| <code>any operators or others who need to understand the system, and calibrated to the level of risk based on the <br>context. Reporting that includes summary information about these automated systems in plain language and <br>assessments of the clarity and quality of the notice and explanations should be made public whenever possible. <br>6</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`: 20
	- `per_device_eval_batch_size`: 20
	- `num_train_epochs`: 5
	- `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`: 20
	- `per_device_eval_batch_size`: 20
	- `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`: 5
	- `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`: 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_eval_metrics`: False
	- `eval_on_start`: False
	- `eval_use_gather_object`: False
	- `batch_sampler`: batch_sampler
	- `multi_dataset_batch_sampler`: round_robin

	</details>

	### Training Logs
	\| Epoch \| Step \| cosine_map@100 \|
	\|:-----:\|:----:\|:--------------:\|
	\| 1.0 \| 40 \| 0.8676 \|
	\| 1.25 \| 50 \| 0.8670 \|
	\| 2.0 \| 80 \| 0.8731 \|
	\| 2.5 \| 100 \| 0.8722 \|
	\| 1.0 \| 40 \| 0.8641 \|
	\| 1.25 \| 50 \| 0.8654 \|
	\| 2.0 \| 80 \| 0.8674 \|
	\| 2.5 \| 100 \| 0.8706 \|
	\| 3.0 \| 120 \| 0.8659 \|
	\| 3.75 \| 150 \| 0.8697 \|
	\| 4.0 \| 160 \| 0.8706 \|
	\| 5.0 \| 200 \| 0.8742 \|


	### Framework Versions
	- Python: 3.10.12
	- Sentence Transformers: 3.1.1
	- Transformers: 4.44.2
	- PyTorch: 2.4.1+cu121
	- Accelerate: 0.34.2
	- Datasets: 3.0.0
	- 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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