snoweu_v2 / README.md

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	---
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:29911
	- loss:MatryoshkaLoss
	- loss:MultipleNegativesRankingLoss
	base_model: Snowflake/snowflake-arctic-embed-m-v1.5
	widget:
	- source_sentence: What strategies can be implemented to effectively leverage private
	financing opportunities for small and medium-sized enterprises (SMEs)?
	sentences:
	- (13) While the energy savings potential remains large in all sectors, there is
	a particular challenge relating to transport, as it is responsible for more than
	30 % of final energy consumption, and to buildings, since 75 % of the Union’s
	building stock has a poor energy performance. Another increasingly important sector
	is the information and communications technology (ICT) sector, which is responsible
	for 5 to 9 % of the world’s total electricity use and more than 2 % of global
	emissions. In 2018, data centres accounted for 2,7 % of the electricity demand
	in the EU-28. In that context, the Commission, in its communication of 19 February
	2020 on ‘Shaping Europe's digital future’ (the ‘Union’s Digital Strategy’), highlighted
	the need for highly energy-efficient and sustainable data centres and transparency
	measures for telecoms operators as regards their environmental footprint. Furthermore,
	the possible increase in industry’s energy demand that may result from its decarbonisation,
	particularly for energy intensive processes, should also be taken into account.
	- SMEs in order to leverage and trigger private financing for SMEs.
	- ►M5 — ◄ K Gases (petroleum), refinery; Refinery gas (A complex combination obtained
	from various petroleum refining operations. It consists of hydrogen and hydrocarbons
	having carbon numbers predominantly in the range of C1 through C3.) 649-153-00-0
	272-338-9 68814-67-5 ►M5 — ◄ K Gases (petroleum), platformer products separator
	off; Refinery gas (A complex combination obtained from the chemical reforming
	of naphthenes to aromatics. It consists of hydrogen and saturated aliphatic hydrocarbons
	having carbon numbers predominantly in the range of C2 through C4.) 649-154-00-6
	272-343-6 68814-90-4 ►M5 — ◄ K Gases (petroleum), hydrotreated sour kerosine
	depentaniser stabiliser off; Refinery gas (The complex combination obtained from
	the
	- source_sentence: How can an undertaking identify and leverage opportunities related
	to sustainability matters within its business model and strategy?
	sentences:
	- 'i.


	focusses on specific activities, business relationships, geographies or other
	factors that give rise to heightened risk of adverse impacts;


	ii.


	considers the impacts with which the undertaking is involved through its own operations
	or as a result of its business relationships;


	iii.


	includes consultation with affected stakeholders to understand how they may be
	impacted and with external experts;


	iv.


	prioritises negative impacts based on their relative severity and likelihood,
	(see ESRS 1 section 3.4 Impact materiality) and, if applicable, positive impacts
	on their relative scale, scope and likelihood, and determines which sustainability
	matters are material for reporting purposes, including the qualitative or quantitative
	thresholds and other criteria used as prescribed by ESRS 1 section 3.4 Impact
	materiality;


	(c)


	an overview of the process used to identify, assess, prioritise and monitor risks
	and opportunities that have or may have financial effects . The disclosure shall
	include:


	i.


	how the undertaking has considered the connections of its impacts and dependencies
	with the risks and opportunities that may arise from those impacts and dependencies;


	ii.


	►C1 how the undertaking assesses the likelihood, magnitude, and nature of effects
	of the identified risk and opportunities (such as the qualitative or quantitative
	thresholds and other criteria used as prescribed by ESRS 1 section 3.5 Financial
	materiality); ◄


	iii.


	how the undertaking prioritises sustainability-related risks relative to other
	types of risks, including its use of risk-assessment tools;


	(d)


	a description of the decision-making process and the related internal control
	procedures;


	(e)


	the extent to which and how the process to identify, assess and manage impacts
	and risks is integrated into the undertaking’s overall risk management process
	and used to evaluate the undertaking’s overall risk profile and risk management
	processes;


	(f)


	the extent to which and how the process to identify, assess and manage opportunities
	is integrated into the undertaking’s overall management process where applicable;


	(g)


	the input parameters it uses (for example, data sources, the scope of operations
	covered and the detail used in assumptions); and


	(h)


	whether and how the process has changed compared to the prior reporting period,
	when the process was modified for the last time and future revision dates of the
	materiality assessment.


	Disclosure Requirement IRO-2 – Disclosure Requirements in ESRS covered by the
	undertaking’s sustainability statement


	The undertaking shall report on the Disclosure Requirements complied with in its
	sustainability statements.


	The objective of this Disclosure Requirement is to provide an understanding of
	the Disclosure Requirements included in the undertaking’s sustainability statement
	and of the topics that have been omitted as not material, as a result of the materiality
	assessment.


	The undertaking shall include a list of the Disclosure Requirements complied with
	in preparing the sustainability statement , following the outcome of the materiality
	assessment (see ESRS 1 chapter 3), including the page numbers and/or paragraphs
	where the related disclosures are located in the sustainability statement. This
	may be presented as a content index. The undertaking shall also include a table
	of all the datapoints that derive from other EU legislation as listed in Appendix
	B of this standard, indicating where they can be found in the sustainability statement
	and including those that the undertaking has assessed as not material, in which
	case the undertaking shall indicate ‘Not material’ in the table in accordance
	with ESRS 1 paragraph 35.


	If the undertaking concludes that climate change is not material and therefore
	omits all disclosure requirements in ESRS E1 Climate change, it shall disclose
	a detailed explanation of the conclusions of its materiality assessment with regard
	to climate change (see ESRS 2 IRO-2 Disclosure Requirements in ESRS covered by
	the undertaking’s sustainability statement), including a forward-looking analysis
	of the conditions that could lead the undertaking to conclude that climate change
	is material in the future.


	If the undertaking concludes that a topic other than climate change is not material
	and therefore omits all the Disclosure Requirements in the corresponding topical
	ESRS, it may provide a brief explanation of the conclusions of its materiality
	assessment for that topic.'
	- '(b)


	the number and type of market participants, including the ratio of market participants
	to traded instruments in a particular product;


	(c)


	the average size of spreads, where available;


	(26)


	‘competent authority’ means the authority, designated by each Member State in
	accordance with Article 67, unless otherwise specified in this Directive;


	(27)


	‘credit institution’ means a credit institution as defined in point (1) of Article
	4(1) of Regulation (EU) No 575/2013;


	(28)


	‘UCITS management company’ means a management company as defined in point (b)
	of Article 2(1) of Directive 2009/65/EC of the European Parliament and of the
	Council ( 4 );


	(29)'
	- '(a)


	a brief description of the undertaking’s business model and strategy, including:


	(i)


	the resilience of the undertaking’s business model and strategy in relation to
	risks related to sustainability matters;


	(ii)


	the opportunities for the undertaking related to sustainability matters;


	(iii)'
	- source_sentence: What are the conditions under which an undertaking with an average
	number of 750 employees can omit certain sustainability information while still
	needing to disclose the materiality assessment of those topics?
	sentences:
	- '(c)


	impose restrictions on non-EU AIFMs relating to the management of an AIF where
	its activities potentially constitute an important source of counterparty risk
	to a credit institution or other systemically relevant institutions.


	5.


	ESMA may take a decision under paragraph 4 and subject to the requirements set
	out in paragraph 6 if both of the following conditions are met:


	(a)


	a substantial threat exists, originating or aggravated by the activities of AIFMs,
	to the orderly functioning and integrity of the financial market or to the stability
	of the whole or a part of the financial system in the Union and there are cross
	border implications; and


	(b)'
	- '▼B


	If an undertaking or group not exceeding on its balance sheet date the average
	number of 750 employees during the financial year decides to omit the information
	required by ESRS E4, ESRS S1, ESRS S2, ESRS S3 or ESRS S4 in accordance with Appendix
	C of ESRS 1, it shall nevertheless disclose whether the sustainability topics
	covered respectively by ESRS E4, ESRS S1, ESRS S2, ESRS S3 and ESRS S4 have been
	assessed to be material as a result of the undertaking’s materiality assessment.
	In addition, if one or more of these topics has been assessed to be material,
	the undertaking shall, for each material topic:


	(a)'
	- '9.


	The Commission shall establish and keep up-to-date a register of recognised schemes.
	That register shall be made publicly available on a free-access website. That
	website shall also allow for the collation of feedback from all relevant stakeholders
	concerning the implementation of recognised schemes. Such feedback shall be submitted
	to the relevant scheme owners for consideration.


	Article 31


	Environmental footprint declaration


	1.'
	- source_sentence: What are the specific roles and responsibilities of the InvestEU
	Advisory Hub in relation to project development assistance for public authorities
	and project promoters?
	sentences:
	- 'System B


	Alternative characterisation Physical and chemical factors that determine the
	characteristics of the coastal water and hence the biological community structure
	and composition Obligatory factors latitude longitude tidal range salinity Optional
	factors current velocity wave exposure mean water temperature mixing characteristics
	turbidity retention time (of enclosed bays) mean substratum composition water
	temperature range


	1.3. Establishment of type-specific reference conditions for surface water body
	types'
	- newly implemented since 31 December 2008 that continue to have an impact in 2020
	with respect to the obligation period referred to in paragraph 1, first subparagraph,
	point (a), and beyond 2020 with respect to the period referred to in point (b)(i),
	of that subparagraph, and which can be measured and verified; --- --- (e) count
	towards the amount of required energy savings, energy savings that stem from policy
	measures, provided that it can be demonstrated that those measures result in individual
	actions carried out from 1 January 2018 to 31 December 2020 which deliver savings
	after 31 December 2020; --- --- (f) exclude from the calculation of the amount
	of required energy savings pursuant to paragraph 1, first subparagraph, points
	(a) and
	- 'Advisory initiatives shall be available as a component under each policy window
	referred to in Article 8(1), covering sectors under that window. In addition,
	advisory initiatives shall be available under a cross-sectoral component.


	2.


	The InvestEU Advisory Hub shall in particular:


	(a)


	provide a central point of entry, managed and hosted by the Commission, for project
	development assistance under the InvestEU Advisory Hub for public authorities
	and for project promoters;


	(b)


	disseminate to public authorities and project promoters all available additional
	information regarding the investment guidelines, including information on their
	application or on the interpretation provided by the Commission;


	(c)'
	- source_sentence: What is the definition of a preliminary economic assessment in
	the context of evaluating projects for the recovery of critical raw materials?
	sentences:
	- 'For the purposes of the first subparagraph of this paragraph, insurance undertakings
	referred to in point (a) of the first subparagraph of Article 1(3) of this Directive
	that are part of a group, on the basis of financial relationships referred to
	in point (c)(ii) of Article 212(1) of Directive 2009/138/EC, and which are subject
	to group supervision in accordance with points (a) to (c) of Article 213(2) of
	that Directive shall be treated as subsidiary undertakings of the parent undertaking
	of that group.


	9.'
	- '(a)


	progress in the implementation of the Strategic Project, in particular with regard
	to the permit-granting process;


	(b)


	where relevant, reasons for delays compared to the timetable referred to in Article
	7(1), point (c) and a plan to overcome such delays;


	(c)


	progress in financing the Strategic Project, including information on public financial
	support.


	The Commission shall submit a copy of the report referred to in the first subparagraph
	of this paragraph to the Board in order to facilitate the discussions referred
	to in Article 36(7), point (c).


	2.


	The Commission may, where necessary, request additional information from project
	promoters relevant to the implementation of the Strategic Project to ascertain
	the continuing fulfilment of the criteria laid down in Article 6(1).


	3.


	The project promoter shall notify the Commission of:


	(a)


	changes to the Strategic Project affecting its fulfilment of the criteria laid
	down in Article 6(1);


	(b)


	changes in control of the undertakings involved in the Strategic Project on a
	lasting basis, compared to the information referred to in Article 7(1), point
	(e).


	4.


	The Commission may adopt implementing acts establishing a single template to be
	used by project promoters to provide all the information required for the reports
	referred to in paragraph 1 of this Article. The single template may indicate how
	the information referred to in paragraph 1 of this Article is to be expressed.
	Those implementing acts shall be adopted in accordance with the advisory procedure
	referred to in Article 39(2).


	The extent of documentation required to complete the single template referred
	to in the first subparagraph shall be reasonable.


	5.'
	- '(39)


	‘preliminary economic assessment’ means an early-stage, conceptual assessment
	of the potential economic viability of a project for the recovery of critical
	raw materials from extractive waste;


	(40)


	‘magnetic resonance imaging device’ means a non-invasive medical device that uses
	magnetic fields to make anatomical images or any other device that uses magnetic
	fields to make images of the inside of object;


	(41)


	‘wind energy generator’ means the part of an onshore or offshore wind turbine
	that converts the mechanical energy of the rotor into electrical energy;


	(42)'
	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-m-v1.5
	results:
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: Unknown
	type: unknown
	metrics:
	- type: cosine_accuracy@1
	value: 0.822517355870812
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.9526109266525807
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.9725324479323876
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.9873226682764865
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.822517355870812
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.31753697555086025
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.1945064895864775
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.09873226682764866
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.822517355870812
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.9526109266525807
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.9725324479323876
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.9873226682764865
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.9140763784801484
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.8895886335216252
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.8902791958273809
	name: Cosine Map@100
	---

	# SentenceTransformer based on Snowflake/snowflake-arctic-embed-m-v1.5

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-m-v1.5](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v1.5). 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-v1.5](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v1.5) <!-- at revision 8e4eaca09c27ad3d501908636ec7c8bc3561b6de -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 768 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': 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("sentence_transformers_model_id")
	# Run inference
	sentences = [
	'What is the definition of a preliminary economic assessment in the context of evaluating projects for the recovery of critical raw materials?',
	'(39)\n\n‘preliminary economic assessment’ means an early-stage, conceptual assessment of the potential economic viability of a project for the recovery of critical raw materials from extractive waste;\n\n(40)\n\n‘magnetic resonance imaging device’ means a non-invasive medical device that uses magnetic fields to make anatomical images or any other device that uses magnetic fields to make images of the inside of object;\n\n(41)\n\n‘wind energy generator’ means the part of an onshore or offshore wind turbine that converts the mechanical energy of the rotor into electrical energy;\n\n(42)',
	'For the purposes of the first subparagraph of this paragraph, insurance undertakings referred to in point (a) of the first subparagraph of Article 1(3) of this Directive that are part of a group, on the basis of financial relationships referred to in point (c)(ii) of Article 212(1) of Directive 2009/138/EC, and which are subject to group supervision in accordance with points (a) to (c) of Article 213(2) of that Directive shall be treated as subsidiary undertakings of the parent undertaking of that group.\n\n9.',
	]
	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 \| 0.8225 \|
	\| cosine_accuracy@3 \| 0.9526 \|
	\| cosine_accuracy@5 \| 0.9725 \|
	\| cosine_accuracy@10 \| 0.9873 \|
	\| cosine_precision@1 \| 0.8225 \|
	\| cosine_precision@3 \| 0.3175 \|
	\| cosine_precision@5 \| 0.1945 \|
	\| cosine_precision@10 \| 0.0987 \|
	\| cosine_recall@1 \| 0.8225 \|
	\| cosine_recall@3 \| 0.9526 \|
	\| cosine_recall@5 \| 0.9725 \|
	\| cosine_recall@10 \| 0.9873 \|
	\| cosine_ndcg@10 \| 0.9141 \|
	\| cosine_mrr@10 \| 0.8896 \|
	\| cosine_map@100 \| 0.8903 \|

	<!--
	## 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: 29,911 training samples
	* Columns: <code>sentence_0</code> and <code>sentence_1</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence_0 \| sentence_1 \|
	\|:--------\|:------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------\|
	\| type \| string \| string \|
	\| details \| <ul><li>min: 13 tokens</li><li>mean: 41.63 tokens</li><li>max: 252 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 233.72 tokens</li><li>max: 512 tokens</li></ul> \|
	* Samples:
	\| sentence_0 \| sentence_1 \|
	\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>What measures must Member States take to ensure that workers who believe they have been discriminated against in terms of equal pay can establish their case before a competent authority or national court?</code> \| <code>Article 18<br><br>Shift of burden of proof<br><br>1. Member States shall take the appropriate measures, in accordance with their national judicial systems, to ensure that, when workers who consider themselves wronged because the principle of equal pay has not been applied to them establish before a competent authority or national court facts from which it may be presumed that there has been direct or indirect discrimination, it shall be for the respondent to prove that there has been no direct or indirect discrimination in relation to pay.<br><br>2. Member States shall ensure that, in administrative procedures or court proceedings regarding alleged direct or indirect discrimination in relation to pay, where an employer has not implemented the pay transparency obligations set out in Articles 5, 6, 7, 9 and 10, it is for the employer to prove that there has been no such discrimination.<br><br>The first subparagraph of this paragraph shall not apply where the employer proves that the infringement of the obligati...</code> \|
	\| <code>What are the key considerations for recognizing and addressing discrimination in the context of compensation and penalties, particularly in relation to the gender pay gap?</code> \| <code>discrimination, in particular for substantive and procedural purposes, including to recognise the existence of discrimination, to decide on the appropriate comparator, to assess the proportionality, and to determine, where relevant, the level of compensation awarded or penalties imposed. An intersectional approach is important for understanding and addressing the gender pay gap. This clarification should not change the scope of employers’ obligations in regard to the pay transparency measures under this Directive. In particular, employers should not be required to gather data related to protected grounds other than sex.</code> \|
	\| <code>What is the process for aircraft operators and shipping companies regarding the surrendering of allowances in relation to their total emissions from the previous calendar year?</code> \| <code>(b)<br><br>each aircraft operator surrenders a number of allowances that is equal to its total emissions during the preceding calendar year, as verified in accordance with Article 15;<br><br>(c)<br><br>each shipping company surrenders a number of allowances that is equal to its total emissions during the preceding calendar year, as verified in accordance with Article 3ge.<br><br>Member States, administering Member States and administering authorities in respect of a shipping company shall ensure that allowances surrendered in accordance with the first subparagraph are subsequently cancelled.<br><br>▼M15<br><br>3-e.</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`: 6
	- `per_device_eval_batch_size`: 6
	- `num_train_epochs`: 4
	- `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`: 6
	- `per_device_eval_batch_size`: 6
	- `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`: 4
	- `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
	<details><summary>Click to expand</summary>

	\| Epoch \| Step \| Training Loss \| cosine_ndcg@10 \|
	\|:------:\|:-----:\|:-------------:\|:--------------:\|
	\| 0.0201 \| 100 \| - \| 0.6629 \|
	\| 0.0401 \| 200 \| - \| 0.7746 \|
	\| 0.0602 \| 300 \| - \| 0.8233 \|
	\| 0.0802 \| 400 \| - \| 0.8515 \|
	\| 0.1003 \| 500 \| 0.4694 \| 0.8621 \|
	\| 0.1203 \| 600 \| - \| 0.8680 \|
	\| 0.1404 \| 700 \| - \| 0.8733 \|
	\| 0.1604 \| 800 \| - \| 0.8774 \|
	\| 0.1805 \| 900 \| - \| 0.8757 \|
	\| 0.2006 \| 1000 \| 0.1568 \| 0.8795 \|
	\| 0.2206 \| 1100 \| - \| 0.8808 \|
	\| 0.2407 \| 1200 \| - \| 0.8789 \|
	\| 0.2607 \| 1300 \| - \| 0.8796 \|
	\| 0.2808 \| 1400 \| - \| 0.8822 \|
	\| 0.3008 \| 1500 \| 0.1015 \| 0.8821 \|
	\| 0.3209 \| 1600 \| - \| 0.8814 \|
	\| 0.3410 \| 1700 \| - \| 0.8756 \|
	\| 0.3610 \| 1800 \| - \| 0.8822 \|
	\| 0.3811 \| 1900 \| - \| 0.8848 \|
	\| 0.4011 \| 2000 \| 0.0836 \| 0.8843 \|
	\| 0.4212 \| 2100 \| - \| 0.8841 \|
	\| 0.4412 \| 2200 \| - \| 0.8803 \|
	\| 0.4613 \| 2300 \| - \| 0.8851 \|
	\| 0.4813 \| 2400 \| - \| 0.8818 \|
	\| 0.5014 \| 2500 \| 0.0865 \| 0.8849 \|
	\| 0.5215 \| 2600 \| - \| 0.8877 \|
	\| 0.5415 \| 2700 \| - \| 0.8806 \|
	\| 0.5616 \| 2800 \| - \| 0.8832 \|
	\| 0.5816 \| 2900 \| - \| 0.8930 \|
	\| 0.6017 \| 3000 \| 0.0842 \| 0.8928 \|
	\| 0.6217 \| 3100 \| - \| 0.8882 \|
	\| 0.6418 \| 3200 \| - \| 0.8858 \|
	\| 0.6619 \| 3300 \| - \| 0.8863 \|
	\| 0.6819 \| 3400 \| - \| 0.8828 \|
	\| 0.7020 \| 3500 \| 0.0669 \| 0.8839 \|
	\| 0.7220 \| 3600 \| - \| 0.8835 \|
	\| 0.7421 \| 3700 \| - \| 0.8854 \|
	\| 0.7621 \| 3800 \| - \| 0.8839 \|
	\| 0.7822 \| 3900 \| - \| 0.8882 \|
	\| 0.8022 \| 4000 \| 0.0695 \| 0.8871 \|
	\| 0.8223 \| 4100 \| - \| 0.8854 \|
	\| 0.8424 \| 4200 \| - \| 0.8822 \|
	\| 0.8624 \| 4300 \| - \| 0.8847 \|
	\| 0.8825 \| 4400 \| - \| 0.8863 \|
	\| 0.9025 \| 4500 \| 0.0575 \| 0.8819 \|
	\| 0.9226 \| 4600 \| - \| 0.8815 \|
	\| 0.9426 \| 4700 \| - \| 0.8836 \|
	\| 0.9627 \| 4800 \| - \| 0.8862 \|
	\| 0.9828 \| 4900 \| - \| 0.8889 \|
	\| 1.0 \| 4986 \| - \| 0.8927 \|
	\| 1.0028 \| 5000 \| 0.0712 \| 0.8935 \|
	\| 1.0229 \| 5100 \| - \| 0.8890 \|
	\| 1.0429 \| 5200 \| - \| 0.8919 \|
	\| 1.0630 \| 5300 \| - \| 0.8949 \|
	\| 1.0830 \| 5400 \| - \| 0.8950 \|
	\| 1.1031 \| 5500 \| 0.0485 \| 0.8934 \|
	\| 1.1231 \| 5600 \| - \| 0.8964 \|
	\| 1.1432 \| 5700 \| - \| 0.8953 \|
	\| 1.1633 \| 5800 \| - \| 0.8942 \|
	\| 1.1833 \| 5900 \| - \| 0.8929 \|
	\| 1.2034 \| 6000 \| 0.0465 \| 0.8912 \|
	\| 1.2234 \| 6100 \| - \| 0.8890 \|
	\| 1.2435 \| 6200 \| - \| 0.8914 \|
	\| 1.2635 \| 6300 \| - \| 0.8847 \|
	\| 1.2836 \| 6400 \| - \| 0.8873 \|
	\| 1.3037 \| 6500 \| 0.0324 \| 0.8912 \|
	\| 1.3237 \| 6600 \| - \| 0.8956 \|
	\| 1.3438 \| 6700 \| - \| 0.8954 \|
	\| 1.3638 \| 6800 \| - \| 0.8946 \|
	\| 1.3839 \| 6900 \| - \| 0.8931 \|
	\| 1.4039 \| 7000 \| 0.0205 \| 0.8951 \|
	\| 1.4240 \| 7100 \| - \| 0.8967 \|
	\| 1.4440 \| 7200 \| - \| 0.8960 \|
	\| 1.4641 \| 7300 \| - \| 0.8943 \|
	\| 1.4842 \| 7400 \| - \| 0.9003 \|
	\| 1.5042 \| 7500 \| 0.0489 \| 0.8946 \|
	\| 1.5243 \| 7600 \| - \| 0.8986 \|
	\| 1.5443 \| 7700 \| - \| 0.8945 \|
	\| 1.5644 \| 7800 \| - \| 0.8960 \|
	\| 1.5844 \| 7900 \| - \| 0.8987 \|
	\| 1.6045 \| 8000 \| 0.039 \| 0.8991 \|
	\| 1.6245 \| 8100 \| - \| 0.8959 \|
	\| 1.6446 \| 8200 \| - \| 0.8948 \|
	\| 1.6647 \| 8300 \| - \| 0.8933 \|
	\| 1.6847 \| 8400 \| - \| 0.8926 \|
	\| 1.7048 \| 8500 \| 0.0297 \| 0.8937 \|
	\| 1.7248 \| 8600 \| - \| 0.8974 \|
	\| 1.7449 \| 8700 \| - \| 0.8977 \|
	\| 1.7649 \| 8800 \| - \| 0.8973 \|
	\| 1.7850 \| 8900 \| - \| 0.8989 \|
	\| 1.8051 \| 9000 \| 0.0248 \| 0.8974 \|
	\| 1.8251 \| 9100 \| - \| 0.8980 \|
	\| 1.8452 \| 9200 \| - \| 0.8970 \|
	\| 1.8652 \| 9300 \| - \| 0.8997 \|
	\| 1.8853 \| 9400 \| - \| 0.9007 \|
	\| 1.9053 \| 9500 \| 0.0534 \| 0.9009 \|
	\| 1.9254 \| 9600 \| - \| 0.9015 \|
	\| 1.9454 \| 9700 \| - \| 0.9014 \|
	\| 1.9655 \| 9800 \| - \| 0.9008 \|
	\| 1.9856 \| 9900 \| - \| 0.9024 \|
	\| 2.0 \| 9972 \| - \| 0.9052 \|
	\| 2.0056 \| 10000 \| 0.0295 \| 0.9041 \|
	\| 2.0257 \| 10100 \| - \| 0.9009 \|
	\| 2.0457 \| 10200 \| - \| 0.9030 \|
	\| 2.0658 \| 10300 \| - \| 0.9028 \|
	\| 2.0858 \| 10400 \| - \| 0.9051 \|
	\| 2.1059 \| 10500 \| 0.027 \| 0.9063 \|
	\| 2.1260 \| 10600 \| - \| 0.9059 \|
	\| 2.1460 \| 10700 \| - \| 0.9044 \|
	\| 2.1661 \| 10800 \| - \| 0.9024 \|
	\| 2.1861 \| 10900 \| - \| 0.9005 \|
	\| 2.2062 \| 11000 \| 0.0201 \| 0.8996 \|
	\| 2.2262 \| 11100 \| - \| 0.9037 \|
	\| 2.2463 \| 11200 \| - \| 0.9029 \|
	\| 2.2663 \| 11300 \| - \| 0.9047 \|
	\| 2.2864 \| 11400 \| - \| 0.9030 \|
	\| 2.3065 \| 11500 \| 0.0097 \| 0.9041 \|
	\| 2.3265 \| 11600 \| - \| 0.9011 \|
	\| 2.3466 \| 11700 \| - \| 0.9000 \|
	\| 2.3666 \| 11800 \| - \| 0.8972 \|
	\| 2.3867 \| 11900 \| - \| 0.8985 \|
	\| 2.4067 \| 12000 \| 0.0165 \| 0.8979 \|
	\| 2.4268 \| 12100 \| - \| 0.8996 \|
	\| 2.4469 \| 12200 \| - \| 0.9026 \|
	\| 2.4669 \| 12300 \| - \| 0.9034 \|
	\| 2.4870 \| 12400 \| - \| 0.9054 \|
	\| 2.5070 \| 12500 \| 0.0165 \| 0.9029 \|
	\| 2.5271 \| 12600 \| - \| 0.9052 \|
	\| 2.5471 \| 12700 \| - \| 0.9057 \|
	\| 2.5672 \| 12800 \| - \| 0.9059 \|
	\| 2.5872 \| 12900 \| - \| 0.9092 \|
	\| 2.6073 \| 13000 \| 0.0144 \| 0.9081 \|
	\| 2.6274 \| 13100 \| - \| 0.9095 \|
	\| 2.6474 \| 13200 \| - \| 0.9102 \|
	\| 2.6675 \| 13300 \| - \| 0.9113 \|
	\| 2.6875 \| 13400 \| - \| 0.9103 \|
	\| 2.7076 \| 13500 \| 0.0159 \| 0.9105 \|
	\| 2.7276 \| 13600 \| - \| 0.9073 \|
	\| 2.7477 \| 13700 \| - \| 0.9084 \|
	\| 2.7677 \| 13800 \| - \| 0.9080 \|
	\| 2.7878 \| 13900 \| - \| 0.9083 \|
	\| 2.8079 \| 14000 \| 0.0183 \| 0.9083 \|
	\| 2.8279 \| 14100 \| - \| 0.9070 \|
	\| 2.8480 \| 14200 \| - \| 0.9085 \|
	\| 2.8680 \| 14300 \| - \| 0.9078 \|
	\| 2.8881 \| 14400 \| - \| 0.9075 \|
	\| 2.9081 \| 14500 \| 0.0257 \| 0.9073 \|
	\| 2.9282 \| 14600 \| - \| 0.9098 \|
	\| 2.9483 \| 14700 \| - \| 0.9089 \|
	\| 2.9683 \| 14800 \| - \| 0.9097 \|
	\| 2.9884 \| 14900 \| - \| 0.9079 \|
	\| 3.0 \| 14958 \| - \| 0.9081 \|
	\| 3.0084 \| 15000 \| 0.0144 \| 0.9084 \|
	\| 3.0285 \| 15100 \| - \| 0.9083 \|
	\| 3.0485 \| 15200 \| - \| 0.9078 \|
	\| 3.0686 \| 15300 \| - \| 0.9079 \|
	\| 3.0886 \| 15400 \| - \| 0.9089 \|
	\| 3.1087 \| 15500 \| 0.0082 \| 0.9093 \|
	\| 3.1288 \| 15600 \| - \| 0.9098 \|
	\| 3.1488 \| 15700 \| - \| 0.9106 \|
	\| 3.1689 \| 15800 \| - \| 0.9103 \|
	\| 3.1889 \| 15900 \| - \| 0.9110 \|
	\| 3.2090 \| 16000 \| 0.0185 \| 0.9117 \|
	\| 3.2290 \| 16100 \| - \| 0.9116 \|
	\| 3.2491 \| 16200 \| - \| 0.9125 \|
	\| 3.2692 \| 16300 \| - \| 0.9111 \|
	\| 3.2892 \| 16400 \| - \| 0.9109 \|
	\| 3.3093 \| 16500 \| 0.0105 \| 0.9125 \|
	\| 3.3293 \| 16600 \| - \| 0.9117 \|
	\| 3.3494 \| 16700 \| - \| 0.9118 \|
	\| 3.3694 \| 16800 \| - \| 0.9117 \|
	\| 3.3895 \| 16900 \| - \| 0.9137 \|
	\| 3.4095 \| 17000 \| 0.019 \| 0.9134 \|
	\| 3.4296 \| 17100 \| - \| 0.9129 \|
	\| 3.4497 \| 17200 \| - \| 0.9126 \|
	\| 3.4697 \| 17300 \| - \| 0.9133 \|
	\| 3.4898 \| 17400 \| - \| 0.9136 \|
	\| 3.5098 \| 17500 \| 0.0109 \| 0.9120 \|
	\| 3.5299 \| 17600 \| - \| 0.9124 \|
	\| 3.5499 \| 17700 \| - \| 0.9122 \|
	\| 3.5700 \| 17800 \| - \| 0.9129 \|
	\| 3.5901 \| 17900 \| - \| 0.9132 \|
	\| 3.6101 \| 18000 \| 0.0207 \| 0.9139 \|
	\| 3.6302 \| 18100 \| - \| 0.9134 \|
	\| 3.6502 \| 18200 \| - \| 0.9135 \|
	\| 3.6703 \| 18300 \| - \| 0.9139 \|
	\| 3.6903 \| 18400 \| - \| 0.9141 \|
	\| 3.7104 \| 18500 \| 0.0105 \| 0.9139 \|
	\| 3.7304 \| 18600 \| - \| 0.9138 \|
	\| 3.7505 \| 18700 \| - \| 0.9136 \|
	\| 3.7706 \| 18800 \| - \| 0.9141 \|

	</details>

	### Framework Versions
	- Python: 3.10.11
	- Sentence Transformers: 3.4.1
	- Transformers: 4.48.1
	- PyTorch: 2.4.0+cu121
	- Accelerate: 1.4.0
	- 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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