test2 / README.md

Add new SentenceTransformer model

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	---
	language:
	- en
	license: apache-2.0
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:3692
	- loss:MatryoshkaLoss
	- loss:MultipleNegativesRankingLoss
	base_model: nomic-ai/modernbert-embed-base
	widget:
	- source_sentence: '\| :-- \| :--: \| :--: \|\| After the coming into force date \| 1.00
	\| 1.00 \|\| 6 April 2014 - coming into force date \| 1.30 \| 1.30 \|\| 1 Oct 2010 -
	5 April 2014 \| 1.40 \| 1.40 \|\| 6 April 2006 - 30 Sept 2010 \| 1.67 \| 1.67 \|\| Pre
	6 April 2006 \| 1.67 \| 1.67 \|'
	sentences:
	- What are the applicable building regulation change factors for different time
	periods starting from pre-6 April 2006 to the current date?
	- When are protected lobbies or corridors required for staircases in buildings with
	multiple storeys above ground level?
	- What are the standards for repairing, reconstructing, and altering existing drains
	and sewers?
	- source_sentence: The door has a minimum clear opening width of 850 mm , when measured
	in accordance with Diagram 3.2.g.Where there are double doors, the main (or leading)
	leaf provides the required minimum clear opening width.A minimum 200 mm nib is
	provided to the following edge of the door and the extra width created by the
	nib is maintained for a minimum of 1500 mm beyond it.[IMAGE]h.The door is located
	reasonably centrally within the thickness of the wall while ensuring that the
	depth of the reveal on the leading face of the door (usually the inside) is a
	maximum of 200 mm .i.The threshold is an accessible threshold.j.Where there is
	a lobby or porch, the doors are a minimum of 1500 mm apart and there is a minimum
	of 1500 mm between door swings.k.Door entry controls, where provided, are mounted
	900-1000mm above finished ground level a minimum of 300 mm away from any external
	return corner.l.A fused spur, suitable for the fitting of a powered door opener,
	is provided on the hinge side of the door.# Other external doors3.23 All other
	external doors - including doors to and from a private garden, balcony, terrace,
	garage, carport, conservatory or storage area that is integral with, or connected,
	the dwelling comply with provisions $f$. to $k$. of paragraph 3.22 and should
	have a minimum 300 m nib to the leading edge of the door with the extra width
	created by this nib extending for a minimum 1800 mm beyond it.## Circulation areas,
	internal doorways and storage## Hall and door widths3.24 To facilitate wheelchair
	movement into and between rooms, internal halls and doors should comply with all
	of the following (see Diagram 3.4).
	sentences:
	- What are the minimum clear opening width and placement requirements for doors
	according to the UK building regulations?
	- What are the material class and limits for rooms and circulation spaces in buildings,
	excluding protected stairways?
	- What is the purpose of the Building and Engineering Services Association's DW/144
	Specification for Sheet Metal Ductwork as referenced in the UK building regulations?
	- source_sentence: a.Every individual dwelling complies with all of the following
	conditions.i.The dwelling primary energy rate must not exceed the individual dwelling's
	target primary energy rate.ii.The dwelling emission rate must not exceed the individual
	dwelling's target emission rate.iii.The dwelling fabric energy efficiency rate
	must not exceed the individual dwelling's target fabric energy efficiency rate.ORb.All
	of the following are met.i.
	sentences:
	- What are the requirements for a parking space in a dwelling to accommodate a wheelchair
	user?
	- What are the fire separation requirements for a garage attached to a dwellinghouse?
	- What criteria must a dwelling meet to comply with target energy and emission rates
	in UK building regulations?
	- source_sentence: ISBN 071760 4136.The Workplace (Health, Safety and Welfare) Regulations
	1992 apply to the common parts of flats and similar buildings if people such as
	cleaners and caretakers are employed to work in these common parts.Where the requirements
	of the Building Regulations that are covered by this Part do not apply to dwellings,
	the provisions may still be required in the situations described above in order
	to satisfy the Workplace Regulations.# USE OF GUIDANCE# The RequirementsThis Approved
	Document, which took effect on 1 July 2003, deals with the Requirements of Part
	E of Schedule 1 to the Building Regulations 2010.## Requirement## Protection against
	sound from other parts of the building and adjoining buildings
	sentences:
	- What are the fire resistance requirements for constructions separating firefighting
	shafts from the rest of the building or from their own components within the shaft?
	- What is required to provide sprinkler protection to open-plan areas in a building?
	- What are the sound protection requirements for the common parts of flats and similar
	buildings employed with workers such as cleaners and caretakers?
	- source_sentence: Appendix A:Key terms ..... 11Appendix B:Standards referred to .....
	12# Approved Document P:Electrical safety Dwellings## Summary0.1 This approved
	document gives guidance on how to comply with Part P of the Building Regulations.It
	contains the following sections:Section 1:Technical requirements for electrical
	work in dwellingsSection 2:The types of building and electrical installation within
	the scope of Part P, and the types of electrical work that are notifiableSection
	3:The different procedures that may be followed to show that electrical work complies
	with Part PAppendix A:Key terms
	sentences:
	- What conditions classify a district heat network as 'under construction' based
	on the building regulations defined on 15 June 2022?
	- What are the requirements for providing reasonable access for maintaining ventilation
	systems in buildings?
	- What guidance does Approved Document P provide for complying with Part P of the
	Building Regulations regarding electrical work in dwellings?
	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: ModernBERT Embed base Legal Matryoshka
	results:
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: dim 768
	type: dim_768
	metrics:
	- type: cosine_accuracy@1
	value: 0.656934306569343
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.8442822384428224
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.8905109489051095
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.9367396593673966
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.656934306569343
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.28142741281427414
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.1781021897810219
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.09367396593673966
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.656934306569343
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.8442822384428224
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.8905109489051095
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.9367396593673966
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.8031293036354475
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.7597178774186073
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.7623797955453413
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: dim 512
	type: dim_512
	metrics:
	- type: cosine_accuracy@1
	value: 0.656934306569343
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.8442822384428224
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.8880778588807786
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.9440389294403893
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.656934306569343
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.28142741281427414
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.17761557177615572
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.09440389294403892
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.656934306569343
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.8442822384428224
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.8880778588807786
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.9440389294403893
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.8037305107001143
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.7585679527285365
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.7605016315461136
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: dim 256
	type: dim_256
	metrics:
	- type: cosine_accuracy@1
	value: 0.6472019464720195
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.8369829683698297
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.8880778588807786
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.9440389294403893
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.6472019464720195
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.27899432278994324
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.1776155717761557
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.09440389294403892
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.6472019464720195
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.8369829683698297
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.8880778588807786
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.9440389294403893
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.7994650257091209
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.7527816398254354
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.7549760733760151
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: dim 128
	type: dim_128
	metrics:
	- type: cosine_accuracy@1
	value: 0.610705596107056
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.8126520681265207
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.851581508515815
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.9294403892944039
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.610705596107056
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.27088402270884027
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.170316301703163
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.09294403892944038
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.610705596107056
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.8126520681265207
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.851581508515815
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.9294403892944039
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.7725417704200617
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.7222550496273127
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.7253503796965786
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: dim 64
	type: dim_64
	metrics:
	- type: cosine_accuracy@1
	value: 0.5450121654501217
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.7493917274939172
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.7931873479318735
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.8637469586374696
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.5450121654501217
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.2497972424979724
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.15863746958637467
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.08637469586374696
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.5450121654501217
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.7493917274939172
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.7931873479318735
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.8637469586374696
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.7081090686112841
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.657939404472251
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.663604050590536
	name: Cosine Map@100
	---

	# ModernBERT Embed base Legal Matryoshka

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base). 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: [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base) <!-- at revision d556a88e332558790b210f7bdbe87da2fa94a8d8 -->
	- Maximum Sequence Length: 8192 tokens
	- Output Dimensionality: 768 dimensions
	- Similarity Function: Cosine Similarity
	<!-- - Training Dataset: Unknown -->
	- Language: en
	- License: apache-2.0

	### Model Sources

	- Documentation: [Sentence Transformers Documentation](https://sbert.net)
	- Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
	- Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

	### Full Model Architecture

	```
	SentenceTransformer(
	(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel
	(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
	(2): Normalize()
	)
	```

	## Usage

	### Direct Usage (Sentence Transformers)

	First install the Sentence Transformers library:

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

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

	# Download from the 🤗 Hub
	model = SentenceTransformer("calvin2258000/test2")
	# Run inference
	sentences = [
	'Appendix A:Key terms ..... 11Appendix B:Standards referred to ..... 12# Approved Document P:Electrical safety Dwellings## Summary0.1 This approved document gives guidance on how to comply with Part P of the Building Regulations.It contains the following sections:Section 1:Technical requirements for electrical work in dwellingsSection 2:The types of building and electrical installation within the scope of Part P, and the types of electrical work that are notifiableSection 3:The different procedures that may be followed to show that electrical work complies with Part PAppendix A:Key terms',
	'What guidance does Approved Document P provide for complying with Part P of the Building Regulations regarding electrical work in dwellings?',
	"What conditions classify a district heat network as 'under construction' based on the building regulations defined on 15 June 2022?",
	]
	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

	* Datasets: `dim_768`, `dim_512`, `dim_256`, `dim_128` and `dim_64`
	* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

	\| Metric \| dim_768 \| dim_512 \| dim_256 \| dim_128 \| dim_64 \|
	\|:--------------------\|:-----------\|:-----------\|:-----------\|:-----------\|:-----------\|
	\| cosine_accuracy@1 \| 0.6569 \| 0.6569 \| 0.6472 \| 0.6107 \| 0.545 \|
	\| cosine_accuracy@3 \| 0.8443 \| 0.8443 \| 0.837 \| 0.8127 \| 0.7494 \|
	\| cosine_accuracy@5 \| 0.8905 \| 0.8881 \| 0.8881 \| 0.8516 \| 0.7932 \|
	\| cosine_accuracy@10 \| 0.9367 \| 0.944 \| 0.944 \| 0.9294 \| 0.8637 \|
	\| cosine_precision@1 \| 0.6569 \| 0.6569 \| 0.6472 \| 0.6107 \| 0.545 \|
	\| cosine_precision@3 \| 0.2814 \| 0.2814 \| 0.279 \| 0.2709 \| 0.2498 \|
	\| cosine_precision@5 \| 0.1781 \| 0.1776 \| 0.1776 \| 0.1703 \| 0.1586 \|
	\| cosine_precision@10 \| 0.0937 \| 0.0944 \| 0.0944 \| 0.0929 \| 0.0864 \|
	\| cosine_recall@1 \| 0.6569 \| 0.6569 \| 0.6472 \| 0.6107 \| 0.545 \|
	\| cosine_recall@3 \| 0.8443 \| 0.8443 \| 0.837 \| 0.8127 \| 0.7494 \|
	\| cosine_recall@5 \| 0.8905 \| 0.8881 \| 0.8881 \| 0.8516 \| 0.7932 \|
	\| cosine_recall@10 \| 0.9367 \| 0.944 \| 0.944 \| 0.9294 \| 0.8637 \|
	\| cosine_ndcg@10 \| 0.8031 \| 0.8037 \| 0.7995 \| 0.7725 \| 0.7081 \|
	\| cosine_mrr@10 \| 0.7597 \| 0.7586 \| 0.7528 \| 0.7223 \| 0.6579 \|
	\| cosine_map@100 \| 0.7624 \| 0.7605 \| 0.755 \| 0.7254 \| 0.6636 \|

	<!--
	## 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: 3,692 training samples
	* Columns: <code>positive</code> and <code>anchor</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| positive \| anchor \|
	\|:--------\|:------------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|
	\| type \| string \| string \|
	\| details \| <ul><li>min: 3 tokens</li><li>mean: 176.61 tokens</li><li>max: 742 tokens</li></ul> \| <ul><li>min: 12 tokens</li><li>mean: 23.56 tokens</li><li>max: 51 tokens</li></ul> \|
	* Samples:
	\| positive \| anchor \|
	\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code># Section 3:Subsoil drainage3.1 The provisions which follow assume that the site of the building is not subject to general flooding (see paragraph 0.8 ) or, if it is, that appropriate steps are being taken.3.2 Where the water table can rise to within 0.25 m of the lowest floor of the building, or where surface water could enter or adversely affect the building, either the ground to be covered by the building should be drained by gravity, or other effective means of safeguarding the building should be taken.3.3 If an active subsoil drain is cut during excavation and if it passes under the building it should be:a. re-laid in pipes with sealed joints and have access points outside the building; orb. re-routed around the building; orc. re-run to another outfall (see Diagram 3).3.4 Where there is a risk that groundwater beneath or around the building could adversely affect the stability and properties of the ground, consideration should be given to site drainage or other protection (see Sec...</code> \| <code>What measures should be taken if a building site has a water table that can rise to within 0.25 meters of the lowest floor?</code> \|
	\| <code>Easily accessibleEither:- a window or doorway, any part of which is within 2 m vertically of an accessible level surface such as the ground or basement level, or an access balcony, or- a window within 2 m vertically of a flat or sloping roof (with a pitch of less than $30^{\circ}$ ) that is within 3.5 m of ground level.Coupled assemblyA doorset and window that are supplied as separate self-contained frames and fixed together on site.</code> \| <code>What criteria determine if a window or doorway is considered easily accessible in a building?</code> \|
	\| <code>Fuels such as bituminous coal, untreated wood or compressed paper are not smokeless or low-volatiles fuels.3.These appliances are known as 'exempted fireplaces'.2.7 For fireplaces with openings larger than $500 \mathrm{~mm} \times 550 \mathrm{~mm}$ or fireplaces exposed on two or more sides (such as a fireplace under a canopy or open on both sides of a central chimney breast) a way of showing compliance would be to provide a flue with a cross-sectional area equal to 15 per cent of the total face area of the fireplace opening(s) (see Appendix B).However, specialist advice should be sought when proposing to construct flues having an area of:a. more than 15 per cent of the total face area of the fireplace openings; or</code> \| <code>What is the required flue cross-sectional area for fireplaces with openings larger than 500 mm x 550 mm or exposed on multiple sides?</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`: epoch
	- `per_device_train_batch_size`: 32
	- `per_device_eval_batch_size`: 16
	- `gradient_accumulation_steps`: 16
	- `learning_rate`: 2e-05
	- `num_train_epochs`: 10
	- `lr_scheduler_type`: cosine
	- `warmup_ratio`: 0.1
	- `bf16`: True
	- `tf32`: True
	- `load_best_model_at_end`: True
	- `optim`: adamw_torch_fused
	- `batch_sampler`: no_duplicates

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

	- `overwrite_output_dir`: False
	- `do_predict`: False
	- `eval_strategy`: epoch
	- `prediction_loss_only`: True
	- `per_device_train_batch_size`: 32
	- `per_device_eval_batch_size`: 16
	- `per_gpu_train_batch_size`: None
	- `per_gpu_eval_batch_size`: None
	- `gradient_accumulation_steps`: 16
	- `eval_accumulation_steps`: None
	- `torch_empty_cache_steps`: None
	- `learning_rate`: 2e-05
	- `weight_decay`: 0.0
	- `adam_beta1`: 0.9
	- `adam_beta2`: 0.999
	- `adam_epsilon`: 1e-08
	- `max_grad_norm`: 1.0
	- `num_train_epochs`: 10
	- `max_steps`: -1
	- `lr_scheduler_type`: cosine
	- `lr_scheduler_kwargs`: {}
	- `warmup_ratio`: 0.1
	- `warmup_steps`: 0
	- `log_level`: passive
	- `log_level_replica`: warning
	- `log_on_each_node`: True
	- `logging_nan_inf_filter`: True
	- `save_safetensors`: True
	- `save_on_each_node`: False
	- `save_only_model`: False
	- `restore_callback_states_from_checkpoint`: False
	- `no_cuda`: False
	- `use_cpu`: False
	- `use_mps_device`: False
	- `seed`: 42
	- `data_seed`: None
	- `jit_mode_eval`: False
	- `use_ipex`: False
	- `bf16`: True
	- `fp16`: False
	- `fp16_opt_level`: O1
	- `half_precision_backend`: auto
	- `bf16_full_eval`: False
	- `fp16_full_eval`: False
	- `tf32`: True
	- `local_rank`: 0
	- `ddp_backend`: None
	- `tpu_num_cores`: None
	- `tpu_metrics_debug`: False
	- `debug`: []
	- `dataloader_drop_last`: False
	- `dataloader_num_workers`: 0
	- `dataloader_prefetch_factor`: None
	- `past_index`: -1
	- `disable_tqdm`: False
	- `remove_unused_columns`: True
	- `label_names`: None
	- `load_best_model_at_end`: True
	- `ignore_data_skip`: False
	- `fsdp`: []
	- `fsdp_min_num_params`: 0
	- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
	- `tp_size`: 0
	- `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_fused
	- `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`: no_duplicates
	- `multi_dataset_batch_sampler`: proportional

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \| dim_768_cosine_ndcg@10 \| dim_512_cosine_ndcg@10 \| dim_256_cosine_ndcg@10 \| dim_128_cosine_ndcg@10 \| dim_64_cosine_ndcg@10 \|
	\|:----------:\|:------:\|:-------------:\|:----------------------:\|:----------------------:\|:----------------------:\|:----------------------:\|:---------------------:\|
	\| 1.0 \| 8 \| - \| 0.7456 \| 0.7485 \| 0.7467 \| 0.7117 \| 0.6394 \|
	\| 1.2759 \| 10 \| 33.8924 \| - \| - \| - \| - \| - \|
	\| 2.0 \| 16 \| - \| 0.7781 \| 0.7773 \| 0.7785 \| 0.7466 \| 0.6837 \|
	\| 2.5517 \| 20 \| 10.7256 \| - \| - \| - \| - \| - \|
	\| 3.0 \| 24 \| - \| 0.7935 \| 0.7870 \| 0.7888 \| 0.7535 \| 0.7016 \|
	\| 3.8276 \| 30 \| 5.5408 \| - \| - \| - \| - \| - \|
	\| 4.0 \| 32 \| - \| 0.8000 \| 0.7962 \| 0.7969 \| 0.7585 \| 0.7082 \|
	\| 5.0 \| 40 \| 3.4556 \| 0.8017 \| 0.8011 \| 0.7992 \| 0.7644 \| 0.7082 \|
	\| 6.0 \| 48 \| - \| 0.8037 \| 0.8021 \| 0.7974 \| 0.7692 \| 0.7082 \|
	\| 6.2759 \| 50 \| 2.9963 \| - \| - \| - \| - \| - \|
	\| 7.0 \| 56 \| - \| 0.8025 \| 0.8013 \| 0.7987 \| 0.7719 \| 0.7072 \|
	\| 7.5517 \| 60 \| 3.1681 \| - \| - \| - \| - \| - \|
	\| 8.0 \| 64 \| - \| 0.8035 \| 0.8024 \| 0.7996 \| 0.7723 \| 0.7077 \|
	\| 8.8276 \| 70 \| 2.5551 \| 0.8031 \| 0.8037 \| 0.7995 \| 0.7725 \| 0.7081 \|

	* The bold row denotes the saved checkpoint.

	### Framework Versions
	- Python: 3.12.2
	- Sentence Transformers: 3.4.1
	- Transformers: 4.50.0
	- PyTorch: 2.6.0+cu124
	- 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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