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	---
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:46338
	- loss:MatryoshkaLoss
	- loss:MultipleNegativesRankingLoss
	base_model: Snowflake/snowflake-arctic-embed-l
	widget:
	- source_sentence: What criteria must Member States consider when establishing penalties
	for infringements of the specified Regulation, and what is the deadline for notifying
	the Commission about these rules?
	sentences:
	- 'Enforcement


	1.


	Member States shall lay down the rules on penalties applicable to infringements
	of this Regulation and shall take all measures necessary to ensure that they are
	implemented. The penalties provided for must be effective, proportionate and dissuasive
	taking into account, in particular, the nature, duration, recurrence and gravity
	of the infringement. Member States shall, by 31 December 2024, notify the Commission
	of those rules and of those measures and shall notify it without delay of any
	subsequent amendment affecting them.


	2.'
	- Within the transitional periods established, Member States shall progressively
	reduce their respective gaps with regard to the new minimum levels of taxation.
	However, where the difference between the national level and the minimum level
	does not exceed 3 % of that minimum level, the Member State concerned may wait
	until the end of the period to adjust its national level.
	- 'AR 10. ‘Indirect political contribution’ refers to those political contributions
	made through an intermediary organisation such as a lobbyist or charity, or support
	given to an organisation such as a think tank or trade association linked to or
	supporting particular political parties or causes.


	AR 11. When determining ‘comparable position’ in this standard, the undertaking
	shall consider various factors, including level of responsibility and scope of
	activities undertaken.


	AR 12. The undertaking may provide the following information on its financial
	or in-kind contributions with regard to its lobbying expenses:


	(a)


	the total monetary amount of such internal and external expenses; and


	(b)'
	- source_sentence: How does the use of AI systems impact access to essential public
	assistance benefits and services?
	sentences:
	- X. Among these substances there are ‘priority hazardous substances’ which means
	substances identified in accordance with Article 16(3) and (6) for which measures
	have to be taken in accordance with Article 16(1) and (8). --- --- 31. ‘Pollutant’means
	any substance liable to cause pollution, in particular those listed in Annex VIII.
	--- --- 32. ‘Direct discharge to groundwater’means discharge of pollutants into
	groundwater without percolation throughout the soil or subsoil. --- --- 33. ‘Pollution’means
	the direct or indirect introduction, as a result of human activity, of substances
	or heat into the air, water or land which may be harmful to human health or the
	quality of aquatic ecosystems or terrestrial ecosystems directly depending on
	- 'The competent authorities shall inform the requesting competent authorities of
	any decision taken under the first subparagraph, stating the reasons therefor.


	4.


	In order to ensure uniform application of this Article, ESMA may develop draft
	implementing technical standards to establish common procedures for competent
	authorities to cooperate in on-the-spot verifications and investigations.


	Power is conferred on the Commission to adopt the implementing technical standards
	referred to in the first subparagraph in accordance with Article 15 of Regulation
	(EU) No 1095/2010.


	Article 55


	Dispute settlement'
	- (58) Another area in which the use of AI systems deserves special consideration
	is the access to and enjoyment of certain essential private and public services
	and benefits necessary for people to fully participate in society or to improve
	one’s standard of living. In particular, natural persons applying for or receiving
	essential public assistance benefits and services from public authorities namely
	healthcare services, social security benefits, social services providing protection
	in cases such as maternity, illness, industrial accidents, dependency or old age
	and loss of employment and social and housing assistance, are typically dependent
	on those benefits and services and in a vulnerable position in relation to the
	responsible
	- source_sentence: How does the context suggest promoting vulnerable customers' active
	engagement in the energy market?
	sentences:
	- energy efficiency improvement measures as priority actions; --- --- (c) carry
	out early, forward-looking investments in energy efficiency improvement measures
	before distributional impacts from other policies and measures show their effect;
	--- --- (d) foster technical assistance and the roll-out of enabling funding and
	ﬁnancial tools, such as on-bill schemes, local loan-loss reserve, guarantee funds,
	funds targeting deep renovations and renovations with minimum energy gains; ---
	--- (e) foster technical assistance for social actors to promote vulnerable customer’s
	active engagement in the energy market, and positive changes in their energy consumption
	behaviour; --- --- (f) ensure access to ﬁnance, grants or subsidies bound to minimum
	- '4.


	To the extent that the tasks relating to the implementation of the Innovation
	Fund are not delegated to an implementing body, the Commission shall carry out
	those tasks.


	Article 18


	Tasks of the implementing body


	►M2 The implementing body designated in accordance with Article 17(1) of this
	Regulation to implement the Innovation Fund in accordance with Article 17(2) may
	be entrusted with the overall management of the calls for proposals, the disbursement
	of the Innovation Fund support and the monitoring of the implementation of selected
	projects. ◄ For that purpose, the implementing body may be entrusted with the
	following tasks:


	(a)


	organising the call for proposals;


	(b)'
	- 'Calculation


	Calculations of emissions shall be performed using the formula:


	Activity data × Emission factor × Oxidation factor


	Activity data (fuel used, production rate etc.) shall be monitored on the basis
	of supply data or measurement.'
	- source_sentence: What is the purpose of Directive 2004/109/EC of the European Parliament
	and of the Council of 15 December 2004?
	sentences:
	- '3.7. Uses advised against ►M7 (see Section 1 of the safety data sheet) ◄


	Where applicable, an indication of the uses which the registrant advises against
	and why (i.e. non-statutory recommendations by supplier). This need not be an
	exhaustive list.


	4. CLASSIFICATION AND LABELLING


	▼M3


	4.1 The hazard classification of the substance(s), resulting from the application
	of Title I and II of Regulation (EC) No 1272/2008 for all hazard classes and categories
	in that Regulation,


	In addition, for each entry, the reasons why no classification is given for a
	hazard class or differentiation of a hazard class should be provided (i.e. if
	data are lacking, inconclusive, or conclusive but not sufficient for classification),'
	- '(b)


	operations by which the user of an energy product makes its reuse possible in
	his own undertaking provided that the taxation already paid on such product is
	not less than the taxation which would be due if the reused energy product were
	again to be liable to taxation;


	(c)


	an operation consisting of mixing, outside a production establishment or a tax
	warehouse, energy products with other energy products or other materials, provided
	that:


	(i)


	taxation on the components has been paid previously; and


	(ii)


	the amount paid is not less than the amount of the tax which would be chargeable
	on the mixture.


	The condition under (i) shall not apply where the mixture is exempted for a specific
	use.


	Article 22'
	- '( 15 ) Directive 2004/109/EC of the European Parliament and of the Council of
	15 December 2004 on the harmonisation of transparency requirements in relation
	to information about issuers whose securities are admitted to trading on a regulated
	market and amending Directive 2001/34/EC (OJ L 390, 31.12.2004, p. 38).


	( 16 ) Regulation (EU) 2020/852 of the European Parliament and of the Council
	of 18 June 2020 on the establishment of a framework to facilitate sustainable
	investment, and amending Regulation (EU) 2019/2088 (OJ L 198, 22.6.2020, p. 13).


	( 17 ) OJ L 142, 30.4.2004, p. 12.


	( 18 ) OJ L 340, 22.12.2007, p. 66.'
	- source_sentence: What are the main objectives of the directives mentioned in the
	text regarding greenhouse gas emissions and carbon dioxide storage, and how do
	they relate to environmental protection and sustainability within the European
	Union?
	sentences:
	- '(24) Directive 2003/87/EC of the European Parliament and of the Council of 13
	October 2003 establishing a scheme for greenhouse gas emission allowance trading
	within the Union and amending Council Directive 96/61/EC (OJ L 275, 25.10.2003,
	p. 32).


	(25) Directive 2009/31/EC of the European Parliament and of the Council of 23
	April 2009 on the geological storage of carbon dioxide and amending Council Directive
	85/337/EEC, European Parliament and Council Directives 2000/60/EC, 2001/80/EC,
	2004/35/EC, 2006/12/EC, 2008/1/EC and Regulation (EC) No 1013/2006 (OJ L 140,
	5.6.2009, p. 114).


	(26) Directive 2014/23/EU of the European Parliament and of the Council of 26
	February 2014 on the award of concession contracts (OJ L 94, 28.3.2014, p. 1).'
	- 'Article 33


	Responsibility and liability for drawing up and publishing the financial statements
	and the management report


	▼M4


	1.'
	- '(b)


	risks related to the undertaking’s dependencies on consumers and/or end-users
	may include the loss of business continuity where an economic crisis makes consumers
	unable to afford certain products or services;


	(c)


	►C1 opportunities related to the undertaking’s impacts on consumers and/or end-
	users may include market differentiation and greater customer appeal from offering
	safe products or privacy-respecting services; and ◄


	(d)'
	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: 0.6659761781460383
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.8841705506645952
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.9312963921974797
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.9672017952701536
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.6659761781460383
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.29472351688819837
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.1862592784394959
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.09672017952701535
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.6659761781460383
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.8841705506645952
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.9312963921974797
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.9672017952701536
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.8278291318026204
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.7818480980055302
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.783515504381956
	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("sentence_transformers_model_id")
	# Run inference
	sentences = [
	'What are the main objectives of the directives mentioned in the text regarding greenhouse gas emissions and carbon dioxide storage, and how do they relate to environmental protection and sustainability within the European Union?',
	'(24) Directive 2003/87/EC of the European Parliament and of the Council of 13 October 2003 establishing a scheme for greenhouse gas emission allowance trading within the Union and amending Council Directive 96/61/EC (OJ L 275, 25.10.2003, p. 32).\n\n(25) Directive 2009/31/EC of the European Parliament and of the Council of 23 April 2009 on the geological storage of carbon dioxide and amending Council Directive 85/337/EEC, European Parliament and Council Directives 2000/60/EC, 2001/80/EC, 2004/35/EC, 2006/12/EC, 2008/1/EC and Regulation (EC) No 1013/2006 (OJ L 140, 5.6.2009, p. 114).\n\n(26) Directive 2014/23/EU of the European Parliament and of the Council of 26 February 2014 on the award of concession contracts (OJ L 94, 28.3.2014, p. 1).',
	'Article 33\n\nResponsibility and liability for drawing up and publishing the financial statements and the management report\n\n▼M4\n\n1.',
	]
	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 \| 0.666 \|
	\| cosine_accuracy@3 \| 0.8842 \|
	\| cosine_accuracy@5 \| 0.9313 \|
	\| cosine_accuracy@10 \| 0.9672 \|
	\| cosine_precision@1 \| 0.666 \|
	\| cosine_precision@3 \| 0.2947 \|
	\| cosine_precision@5 \| 0.1863 \|
	\| cosine_precision@10 \| 0.0967 \|
	\| cosine_recall@1 \| 0.666 \|
	\| cosine_recall@3 \| 0.8842 \|
	\| cosine_recall@5 \| 0.9313 \|
	\| cosine_recall@10 \| 0.9672 \|
	\| cosine_ndcg@10 \| 0.8278 \|
	\| cosine_mrr@10 \| 0.7818 \|
	\| cosine_map@100 \| 0.7835 \|

	<!--
	## 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: 46,338 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: 11 tokens</li><li>mean: 35.24 tokens</li><li>max: 206 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 193.39 tokens</li><li>max: 512 tokens</li></ul> \|
	* Samples:
	\| sentence_0 \| sentence_1 \|
	\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>How is materiality defined in the context of an entity's sustainability reporting as per QC 4?</code> \| <code>QC 4. Materiality is an entity-specific aspect of relevance based on the nature or magnitude, or both, of the items to which the information relates, as assessed in the context of the undertaking’s sustainability reporting (see chapter 3 of this Standard).<br><br>Faithful representation<br><br>QC 5. To be useful, the information must not only represent relevant phenomena, it must also faithfully represent the substance of the phenomena that it purports to represent. Faithful representation requires information to be (i) complete, (ii) neutral and (iii) accurate.</code> \|
	\| <code>What procedure must be followed for the adoption of implementing acts as mentioned in the text?</code> \| <code>Those implementing acts shall be adopted in accordance with the examination procedure referred to in Article 22a(2).<br><br>3.<br><br>Articles 9, 9a and 10 shall apply to maritime transport activities in the same manner as they apply to other activities covered by the EU ETS with the following exception with regard to the application of Article 10.</code> \|
	\| <code>How should monitoring points be distributed for groundwater bodies that flow across Member State boundaries to effectively estimate groundwater flow?</code> \| <code>The network shall include sufficient representative monitoring points to estimate the groundwater level in each groundwater body or group of bodies taking into account short and long-term variations in recharge and in particular:<br><br>— for groundwater bodies identified as being at risk of failing to achieve environmental objectives under Article 4, ensure sufficient density of monitoring points to assess the impact of abstractions and discharges on the groundwater level,<br><br>— for groundwater bodies within which groundwater flows across a Member State boundary, ensure sufficient monitoring points are provided to estimate the direction and rate of groundwater flow across the Member State boundary.<br><br>2.2.3. Monitoring frequency</code> \|
	* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
	```json
	{
	"loss": "MultipleNegativesRankingLoss",
	"matryoshka_dims": [
	1024,
	768,
	512,
	256,
	128,
	64
	],
	"matryoshka_weights": [
	1,
	1,
	1,
	1,
	1,
	1
	],
	"n_dims_per_step": -1
	}
	```

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

	- `eval_strategy`: steps
	- `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`: 8
	- `per_device_eval_batch_size`: 8
	- `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`: 3
	- `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 \| Training Loss \| cosine_ndcg@10 \|
	\|:------:\|:-----:\|:-------------:\|:--------------:\|
	\| 0.0863 \| 500 \| 0.938 \| - \|
	\| 0.1726 \| 1000 \| 0.2188 \| - \|
	\| 0.2589 \| 1500 \| 0.1998 \| - \|
	\| 0.3452 \| 2000 \| 0.2162 \| 0.7843 \|
	\| 0.4316 \| 2500 \| 0.1921 \| - \|
	\| 0.5179 \| 3000 \| 0.1749 \| - \|
	\| 0.6042 \| 3500 \| 0.1741 \| - \|
	\| 0.6905 \| 4000 \| 0.2007 \| 0.7779 \|
	\| 0.7768 \| 4500 \| 0.1456 \| - \|
	\| 0.8631 \| 5000 \| 0.1034 \| - \|
	\| 0.9494 \| 5500 \| 0.1285 \| - \|
	\| 1.0 \| 5793 \| - \| 0.7806 \|
	\| 1.0357 \| 6000 \| 0.1011 \| 0.7879 \|
	\| 1.1220 \| 6500 \| 0.065 \| - \|
	\| 1.2084 \| 7000 \| 0.0754 \| - \|
	\| 1.2947 \| 7500 \| 0.067 \| - \|
	\| 1.3810 \| 8000 \| 0.059 \| 0.7953 \|
	\| 1.4673 \| 8500 \| 0.0644 \| - \|
	\| 1.5536 \| 9000 \| 0.0705 \| - \|
	\| 1.6399 \| 9500 \| 0.0425 \| - \|
	\| 1.7262 \| 10000 \| 0.0515 \| 0.8171 \|
	\| 1.8125 \| 10500 \| 0.0358 \| - \|
	\| 1.8988 \| 11000 \| 0.0515 \| - \|
	\| 1.9852 \| 11500 \| 0.043 \| - \|
	\| 2.0 \| 11586 \| - \| 0.8201 \|
	\| 2.0715 \| 12000 \| 0.0257 \| 0.8208 \|
	\| 2.1578 \| 12500 \| 0.0343 \| - \|
	\| 2.2441 \| 13000 \| 0.0307 \| - \|
	\| 2.3304 \| 13500 \| 0.0324 \| - \|
	\| 2.4167 \| 14000 \| 0.0225 \| 0.8236 \|
	\| 2.5030 \| 14500 \| 0.0362 \| - \|
	\| 2.5893 \| 15000 \| 0.0255 \| - \|
	\| 2.6756 \| 15500 \| 0.0203 \| - \|
	\| 2.7620 \| 16000 \| 0.0244 \| 0.8240 \|
	\| 2.8483 \| 16500 \| 0.0461 \| - \|
	\| 2.9346 \| 17000 \| 0.0226 \| - \|
	\| 3.0 \| 17379 \| - \| 0.8278 \|


	### Framework Versions
	- Python: 3.10.15
	- Sentence Transformers: 3.4.1
	- Transformers: 4.49.0
	- PyTorch: 2.6.0+cu126
	- Accelerate: 1.5.2
	- Datasets: 3.4.1
	- Tokenizers: 0.21.1

	## 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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