modernbert-embed-quickb-video / README.md

Add new SentenceTransformer model

1aaa6f3 verified 5 days ago

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	---
	language:
	- en
	license: apache-2.0
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:1440
	- loss:MatryoshkaLoss
	- loss:MultipleNegativesRankingLoss
	base_model: nomic-ai/modernbert-embed-base
	widget:
	- source_sentence: What section of the Code of Federal Regulations is quoted?
	sentences:
	- "and other legal relations of any interested party seeking such declaration.”\
	\ 28 U.S.C. § 2201(a). \nThis statute “is not an independent source of federal\
	\ jurisdiction”; rather, “the availability of \nsuch relief presupposes the existence\
	\ of a judicially remediable right.” Schilling v. Rogers, 363 \nU.S. 666, 677\
	\ (1960). The Court independently has jurisdiction here under the mandamus"
	- "appropriate only when the nature of the work is sporadic and unpredictable so\
	\ that a tour of duty \ncannot be regularly scheduled in advance.” Pl.’s Mem.\
	\ at 18 (quoting 5 C.F.R. § 340.403(a)). \nThis regulation explicitly distinguishes\
	\ “intermittent” status from “part-time” status, as it says \nthat “[w]hen an\
	\ agency is able to schedule work in advance on a regular basis, it has an"
	- "its discretion, a reviewing court looks to the trial court’s “stated justification\
	\ for refusing to \nmodify” the order. Skolnick, 191 Ill. 2d at 226. \n \n \n\
	In the case at bar, the one-sentence April 25 order did not provide any reasons\
	\ at all. The \nlosing party drafted the order without any stated reasons, although\
	\ a lack of stated reasons may"
	- source_sentence: Which office was determined to be an agency in the Soucie case?
	sentences:
	- "inquiry”); Doe v. Skyline Automobiles, Inc., 375 F. Supp. 3d 401, 405-06 (S.D.N.Y.\
	\ 2019) \n(“other factors must be taken into consideration and analyzed in comparison\
	\ to the public’s \ninterest and the interests of the opposing parties”). \n \n\
	\ \nIllinois has taken steps to protect individuals’ private information. Examples\
	\ include the"
	- "Aside from whether the Department’s “approach to artificial intelligence development\
	\ and \nimplementation” should be considered “critical infrastructure,” the Department’s\
	\ affidavit is \n \n \n5\ndeficient in showing that its withholdings qualify as\
	\ “critical infrastructure security information” \nin other ways. For example,\
	\ the affidavit fails to explain how the disclosure of the withheld infor-"
	- "whether an entity wields “substantial independent authority”: investigative\
	\ power and authority \nto make final and binding decisions. \nConsider first\
	\ Soucie. The Circuit held that the Office of Science and Technology \n(“OST”)\
	\ was an agency because, beyond advising the President, it had the “independent\
	\ function"
	- source_sentence: What is the appellant's burden on appeal?
	sentences:
	- "Defs.’ Reply at 7–8, 8 n.1. It cites Judicial Watch, Inc. v. Department of Energy,\
	\ 412 F.3d 125 \n(D.C. Cir. 2005), which dealt with the records of employees that\
	\ the Department of Energy \n(“DOE”) had detailed to the National Energy Policy\
	\ Development Group (“NEPDG”). Id. at \n132. The Government quotes the court’s\
	\ statement that “the records those employees created or"
	- "records available for inspection and copying is a violation of 5 U.S.C. app.\
	\ 2 § 10(b) and \nconstitutes a failure to perform a duty owed to EPIC within\
	\ the meaning of 28 U.S.C. § 1361.” \nId. . Both counts seek “a writ of mandamus”\
	\ compelling the Commission and its officers to \ncomply with FACA. Id. , 139.\
	\ These counts make clear that EPIC seeks mandamus relief"
	- "counsel now cannot fairly contend that the trial court did not consider all the\
	\ facts, especially \nwhen [d]efendant’s counsel offers no court transcript to\
	\ show otherwise.” On appeal, it is \ngenerally the appellant’s burden to provide\
	\ the reviewing court with a sufficient record to \nestablish the error that he\
	\ complains of. Webster v. Hartman, 195 Ill. 2d 426, 436 (2001). “[A]"
	- source_sentence: What does the text refer to as a 'statutory distinction'?
	sentences:
	- "inconsistency in deeming the same entity an advisory committee and an agency.”\
	\ Defs.’ Reply \nat 8. The problem, according to the Government, is that FACA\
	\ generally requires disclosure of \nrecords, yet Exemption 5 would shield a portion\
	\ of these records from public view, which would \nundermine FACA’s “purpose.”\
	\ Id. at 8–9. Gates, Wolfe, and the 1988 OLC opinion echo this"
	- "agencies are operating arms of government characterized by ‘substantial independent\
	\ authority in \nthe exercise of specific functions.’” Disclosure of Advisory\
	\ Comm. Deliberative Materials, 12 \nOp. O.L.C. 73, 81 (1988). This “statutory\
	\ distinction,” it concludes, signifies that “advisory \ncommittees are not agencies.”\
	\ Id."
	- "the Hon. Israel A. Desierto, Judge, presiding. \n \n \nJudgment \nAffirmed. \n\
	\ \nCounsel on \nAppeal \n \nVictor P. Henderson and Colin Quinn Commito, of Henderson\
	\ Parks, \nLLC, of Chicago, for appellant. \n \nTamara N. Holder, Law Firm of\
	\ Tamara N. Holder LLC, of Chicago, \nfor appellee. \n \n \n \nPanel \n \nPRESIDING\
	\ JUSTICE ODEN JOHNSON delivered the judgment of \nthe court, with opinion."
	- source_sentence: What do the newly enacted laws prohibit hospitals from doing regarding
	sexual assault victims?
	sentences:
	- "exclusion for committees “composed wholly of . . . permanent part-time . . .\
	\ employees.” 5 \nU.S.C. app. 2 § 3(2). \n32 \nA second, independent reason why\
	\ the Commission does not fall within this exclusion is \nthat its members are\
	\ not “part-time” federal employees. Instead, they are “intermittent” \nemployees.\
	\ EPIC points to a regulation stating that “[a]n intermittent work schedule is"
	- "committee, board, commission, council, conference, panel, task force, or other\
	\ similar group, or \nany subcommittee or other subgroup thereof.” Id. § 3(2).\
	\ Second, it must be “established by \nstatute or reorganization plan,” “established\
	\ or utilized by the President,” or “established or \nutilized by one or more\
	\ agencies.” Id. Third, it must be “established” or “utilized” “in the"
	- "confidential advisors (735 ILCS 5/8-804(c) (West 2022)) and prohibit hospitals\
	\ treating sexual \nassault victims from directly billing the victims for the\
	\ services, communicating with victims \nabout a bill, or referring overdue bills\
	\ to collection agencies or credit reporting agencies. 410 \nILCS 70/7.5(a)(1)-(4)\
	\ (West 2022). These recently enacted laws encourage victims to report"
	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: Fine-tuned with [QuicKB](https://github.com/ALucek/QuicKB)
	results:
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: dim 768
	type: dim_768
	metrics:
	- type: cosine_accuracy@1
	value: 0.51875
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.69375
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.75
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.83125
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.51875
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.23125
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.14999999999999997
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.08312499999999999
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.51875
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.69375
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.75
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.83125
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.671534966140965
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.6211160714285715
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.6261949467277568
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: dim 512
	type: dim_512
	metrics:
	- type: cosine_accuracy@1
	value: 0.49375
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.7
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.73125
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.825
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.49375
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.2333333333333333
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.14625
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.08249999999999999
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.49375
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.7
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.73125
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.825
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.6607544642083831
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.6085367063492064
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.6146313607229802
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: dim 256
	type: dim_256
	metrics:
	- type: cosine_accuracy@1
	value: 0.4375
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.6875
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.725
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.79375
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.4375
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.22916666666666666
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.145
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.079375
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.4375
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.6875
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.725
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.79375
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.6224957341997419
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.566939484126984
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.5740997074969412
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: dim 128
	type: dim_128
	metrics:
	- type: cosine_accuracy@1
	value: 0.40625
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.625
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.69375
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.775
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.40625
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.20833333333333331
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.13874999999999998
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.07749999999999999
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.40625
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.625
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.69375
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.775
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.5931742895464828
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.5348859126984128
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.5417826806767716
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: dim 64
	type: dim_64
	metrics:
	- type: cosine_accuracy@1
	value: 0.30625
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.4875
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.6
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.6875
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.30625
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.16249999999999998
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.12
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.06875
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.30625
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.4875
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.6
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.6875
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.4854299754851493
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.42175347222222237
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.4326739799760461
	name: Cosine Map@100
	---

	# Fine-tuned with [QuicKB](https://github.com/ALucek/QuicKB)

	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: 1024 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': 1024, '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("AdamLucek/modernbert-embed-quickb-video")
	# Run inference
	sentences = [
	'What do the newly enacted laws prohibit hospitals from doing regarding sexual assault victims?',
	'confidential advisors (735 ILCS 5/8-804(c) (West 2022)) and prohibit hospitals treating sexual \nassault victims from directly billing the victims for the services, communicating with victims \nabout a bill, or referring overdue bills to collection agencies or credit reporting agencies. 410 \nILCS 70/7.5(a)(1)-(4) (West 2022). These recently enacted laws encourage victims to report',
	'exclusion for committees “composed wholly of . . . permanent part-time . . . employees.” 5 \nU.S.C. app. 2 § 3(2). \n32 \nA second, independent reason why the Commission does not fall within this exclusion is \nthat its members are not “part-time” federal employees. Instead, they are “intermittent” \nemployees. EPIC points to a regulation stating that “[a]n intermittent work schedule is',
	]
	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 \| 0.5188 \| 0.4938 \| 0.4375 \| 0.4062 \| 0.3063 \|
	\| cosine_accuracy@3 \| 0.6937 \| 0.7 \| 0.6875 \| 0.625 \| 0.4875 \|
	\| cosine_accuracy@5 \| 0.75 \| 0.7312 \| 0.725 \| 0.6937 \| 0.6 \|
	\| cosine_accuracy@10 \| 0.8313 \| 0.825 \| 0.7937 \| 0.775 \| 0.6875 \|
	\| cosine_precision@1 \| 0.5188 \| 0.4938 \| 0.4375 \| 0.4062 \| 0.3063 \|
	\| cosine_precision@3 \| 0.2313 \| 0.2333 \| 0.2292 \| 0.2083 \| 0.1625 \|
	\| cosine_precision@5 \| 0.15 \| 0.1462 \| 0.145 \| 0.1387 \| 0.12 \|
	\| cosine_precision@10 \| 0.0831 \| 0.0825 \| 0.0794 \| 0.0775 \| 0.0688 \|
	\| cosine_recall@1 \| 0.5188 \| 0.4938 \| 0.4375 \| 0.4062 \| 0.3063 \|
	\| cosine_recall@3 \| 0.6937 \| 0.7 \| 0.6875 \| 0.625 \| 0.4875 \|
	\| cosine_recall@5 \| 0.75 \| 0.7312 \| 0.725 \| 0.6937 \| 0.6 \|
	\| cosine_recall@10 \| 0.8313 \| 0.825 \| 0.7937 \| 0.775 \| 0.6875 \|
	\| cosine_ndcg@10 \| 0.6715 \| 0.6608 \| 0.6225 \| 0.5932 \| 0.4854 \|
	\| cosine_mrr@10 \| 0.6211 \| 0.6085 \| 0.5669 \| 0.5349 \| 0.4218 \|
	\| cosine_map@100 \| 0.6262 \| 0.6146 \| 0.5741 \| 0.5418 \| 0.4327 \|

	<!--
	## 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: 1,440 training samples
	* Columns: <code>anchor</code> and <code>positive</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| anchor \| positive \|
	\|:--------\|:----------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------\|
	\| type \| string \| string \|
	\| details \| <ul><li>min: 7 tokens</li><li>mean: 15.14 tokens</li><li>max: 29 tokens</li></ul> \| <ul><li>min: 57 tokens</li><li>mean: 97.82 tokens</li><li>max: 161 tokens</li></ul> \|
	* Samples:
	\| anchor \| positive \|
	\|:------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>What must the advisory committee make available for public inspection?</code> \| <code>advisory committee shall be available for public inspection and copying . . . until the advisory <br>committee ceases to exist.” Id. § 10(b). Unlike FOIA, this provision looks forward. It requires <br>committees to take affirmative steps to make their records are public, even absent a request. <br>FACA’s definition of “advisory committee” has four parts. First, it includes “any</code> \|
	\| <code>What did the landlords fail to alert the court about?</code> \| <code>court documents containing fake citations, we conclude that <br>imposing monetary sanctions or dismissing this appeal would be <br>disproportionate to Al-Hamim’s violation of the Appellate Rules. <br> <br>23 <br>Further, in their answer brief, the landlords failed to alert this court <br>to the hallucinations in Al-Hamim’s opening brief and did not <br>request an award of attorney fees against Al-Hamim. Under the</code> \|
	\| <code>On what date was the motion served on the plaintiff’s counsel?</code> \| <code>also alleged (1) that plaintiff violated section 2-401(e) and (2) that she lacked good cause to <br>file anonymously because she signed an affidavit in her own name in another case with similar <br>allegations. The April 13 motion contains a “Certificate of Service” stating that it was served <br>on plaintiff’s counsel by e-mail on April 13.</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
	- `gradient_accumulation_steps`: 16
	- `learning_rate`: 2e-05
	- `num_train_epochs`: 4
	- `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`: 8
	- `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`: 4
	- `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}
	- `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 \| 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 \| 3 \| 0.6493 \| 0.6372 \| 0.5987 \| 0.5536 \| 0.4520 \|
	\| 2.0 \| 6 \| 0.6685 \| 0.6514 \| 0.6208 \| 0.5916 \| 0.4859 \|
	\| 2.7111 \| 8 \| 0.6715 \| 0.6608 \| 0.6225 \| 0.5932 \| 0.4854 \|

	* The bold row denotes the saved checkpoint.

	### Framework Versions
	- Python: 3.10.12
	- Sentence Transformers: 3.4.0
	- Transformers: 4.48.1
	- PyTorch: 2.5.1+cu124
	- Accelerate: 1.3.0
	- Datasets: 3.2.0
	- 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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