Add new SentenceTransformer model.

2db6871 verified 5 months ago

34.9 kB

	---
	base_model: dbourget/pb-ds1-48K
	datasets: []
	language: []
	library_name: sentence-transformers
	metrics:
	- pearson_cosine
	- spearman_cosine
	- pearson_manhattan
	- spearman_manhattan
	- pearson_euclidean
	- spearman_euclidean
	- pearson_dot
	- spearman_dot
	- pearson_max
	- spearman_max
	pipeline_tag: sentence-similarity
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:106810
	- loss:CosineSimilarityLoss
	widget:
	- source_sentence: In The Law of Civilization and Decay, Brooks provides a detailed
	look at the rise and fall of civilizations, offering a critical perspective on
	the impact of capitalism. As societies become prosperous, their pursuit of wealth
	ultimately leads to their own downfall as greed takes over.
	sentences:
	- Patrick Todd's The Open Future argues that all future contingent statements, such
	as 'It will rain tomorrow', are inherently false.
	- If propositions are made true in virtue of corresponding to facts, then what are
	the truth-makers of true negative propositions such as ‘The apple is not red’?
	Russell argued that there must be negative facts to account for what makes true
	negative propositions true and false positive propositions false. Others, more
	parsimonious in their ontological commitments, have attempted to avoid them. Wittgenstein
	rejected them since he was loath to think that the sign for negation referred
	to a negative element in a fact. A contemporary of Russell’s, Raphael Demos, attempted
	to eliminate them by appealing to ‘incompatibility’ facts. More recently, Armstrong
	has appealed to the totality of positive facts as the ground of the truth of true
	negative propositions. Oaklander and Miracchi have suggested that the absence
	or non-existence of the positive fact (which is not itself a further fact) is
	the basis of a positive proposition being false and therefore of the truth of
	its negation.
	- The Law of Civilization and Decay is an overview of history, articulating Brooks'
	critical view of capitalism. A civilization grows wealthy, and then its wealth
	causes it to crumble upon itself due to greed.
	- source_sentence: It is generally accepted that the development of the modern sciences
	is rooted in experiment. Yet for a long time, experimentation did not occupy a
	prominent role, neither in philosophy nor in history of science. With the ‘practical
	turn’ in studying the sciences and their history, this has begun to change. This
	paper is concerned with systems and cultures of experimentation and the consistencies
	that are generated within such systems and cultures. The first part of the paper
	exposes the forms of historical and structural coherence that characterize the
	experimental exploration of epistemic objects. In the second part, a particular
	experimental culture in the life sciences is briefly described as an example.
	A survey will be given of what it means and what it takes to analyze biological
	functions in the test tube
	sentences:
	- Experimentation has long been overlooked in the study of science, but with a new
	focus on practical aspects, this is starting to change. This paper explores the
	systems and cultures of experimentation and the patterns that emerge within them.
	The first part discusses the historical and structural coherence of experimental
	exploration. The second part provides a brief overview of an experimental culture
	in the life sciences. The paper concludes with a discussion on analyzing biological
	functions in the test tube.
	- Hintikka and Mutanen have introduced Trail-And-Error machines as a new way to
	think about computation, expanding on the traditional Turing machine model. This
	innovation opens up new possibilities in the field of computation theory.
	- As Allaire and Firsirotu (1984) pointed out over a decade ago, the concept of
	culture seemed to be sliding inexorably into a superficial explanatory pool that
	promised everything and nothing. However, since then, some sophisticated and interesting
	theoretical developments have prevented drowning in the pool of superficiality
	and hence theoretical redundancy. The purpose of this article is to build upon
	such theoretical developments and to introduce an approach that maintains that
	culture can be theorized in the same way as structure, possessing irreducible
	powers and properties that predispose organizational actors towards specific courses
	of action. The morphogenetic approach is the methodological complement of transcendental
	realism, providing explanatory leverage on the conditions that maintain for cultural
	change or stability.
	- source_sentence: 'This chapter examines three approaches to applied political and
	legal philosophy: Standard activism is primarily addressed to other philosophers,
	adopts an indirect and coincidental role in creating change, and counts articulating
	sound arguments as success. Extreme activism, in contrast, is a form of applied
	philosophy directly addressed to policy-makers, with the goal of bringing about
	a particular outcome, and measures success in terms of whether it makes a direct
	causal contribution to that goal. Finally, conceptual activism (like standard
	activism), primarily targets an audience of fellow philosophers, bears a distant,
	non-direct, relation to a desired outcome, and counts success in terms of whether
	it encourages a particular understanding and adoption of the concepts under examination.'
	sentences:
	- John Rawls’ resistance to any kind of global egalitarian principle has seemed
	strange and unconvincing to many commentators, including those generally supportive
	of Rawls’ project. His rejection of a global egalitarian principle seems to rely
	on an assumption that states are economically bounded and separate from one another,
	which is not an accurate portrayal of economic relations among states in our globalised
	world. In this article, I examine the implications of the domestic theory of justice
	as fairness to argue that Rawls has good reason to insist on economically bounded
	states. I argue that certain central features of the contemporary global economy,
	particularly the free movement of capital across borders, undermine the distributional
	autonomy required for states to realise Rawls’ principles of justice, and the
	domestic theory thus requires a certain degree of economic separation among states
	prior to the convening of the international original position. Given this, I defend
	Rawls’ reluctance to endorse a global egalitarian principle and defend a policy
	regime of international capital controls, to restore distributional autonomy and
	make the realisation of the principles of justice as fairness possible.
	- 'Bibliography of the writings by Hilary Putnam: 16 books, 198 articles, 10 translations
	into German (up to 1994).'
	- The jurisprudence under international human rights treaties has had a considerable
	impact across countries. Known for addressing complex agendas, the work of expert
	bodies under the treaties has been credited and relied upon for filling the gaps
	in the realization of several objectives, including the peace and security agenda. In
	1982, the Human Rights Committee (ICCPR), in a General Comment observed that “states
	have the supreme duty to prevent wars, acts of genocide and other acts of mass
	violence ... Every effort … to avert the danger of war, especially thermonuclear
	war, and to strengthen international peace and security would constitute the most
	important condition and guarantee for the safeguarding of the right to life.”
	Over the years, all treaty bodies have contributed in this direction, endorsing
	peace and security so as “to protect people against direct and structural violence
	… as systemic problems and not merely as isolated incidents …”. A closer look
	at the jurisprudence on peace and security, emanating from treaty monitoring mechanisms
	including state periodic reports, interpretive statements, the individual communications
	procedure, and others, reveals its distinctive nature
	- source_sentence: Autonomist accounts of cognitive science suggest that cognitive
	model building and theory construction (can or should) proceed independently of
	findings in neuroscience. Common functionalist justifications of autonomy rely
	on there being relatively few constraints between neural structure and cognitive
	function (e.g., Weiskopf, 2011). In contrast, an integrative mechanistic perspective
	stresses the mutual constraining of structure and function (e.g., Piccinini &
	Craver, 2011; Povich, 2015). In this paper, I show how model-based cognitive neuroscience
	(MBCN) epitomizes the integrative mechanistic perspective and concentrates the
	most revolutionary elements of the cognitive neuroscience revolution (Boone &
	Piccinini, 2016). I also show how the prominent subset account of functional realization
	supports the integrative mechanistic perspective I take on MBCN and use it to
	clarify the intralevel and interlevel components of integration.
	sentences:
	- Fictional truth, or truth in fiction/pretense, has been the object of extended
	scrutiny among philosophers and logicians in recent decades. Comparatively little
	attention, however, has been paid to its inferential relationships with time and
	with certain deliberate and contingent human activities, namely, the creation
	of fictional works. The aim of the paper is to contribute to filling the gap.
	Toward this goal, a formal framework is outlined that is consistent with a variety
	of conceptions of fictional truth and based upon a specific formal treatment of
	time and agency, that of so-called stit logics. Moreover, a complete axiomatic
	theory of fiction-making TFM is defined, where fiction-making is understood as
	the exercise of agency and choice in time over what is fictionally true. The language
	\ of TFM is an extension of the language of propositional logic, with the addition
	of temporal and modal operators. A distinctive feature of \ with respect to other
	modal languages is a variety of operators having to do with fictional truth, including
	a ‘fictionality’ operator \ . Some applications of TFM are outlined, and some
	interesting linguistic and inferential phenomena, which are not so easily dealt
	with in other frameworks, are accounted for
	- 'We have structured our response according to five questions arising from the
	commentaries: (i) What is sentience? (ii) Is sentience a necessary or sufficient
	condition for moral standing? (iii) What methods should guide comparative cognitive
	research in general, and specifically in studying invertebrates? (iv) How should
	we balance scientific uncertainty and moral risk? (v) What practical strategies
	can help reduce biases and morally dismissive attitudes toward invertebrates?'
	- 'In 2007, ten world-renowned neuroscientists proposed “A Decade of the Mind Initiative.”
	The contention was that, despite the successes of the Decade of the Brain, “a
	fundamental understanding of how the brain gives rise to the mind [was] still
	lacking” (2007, 1321). The primary aims of the decade of the mind were “to build
	on the progress of the recent Decade of the Brain (1990-99)” by focusing on “four
	broad but intertwined areas” of research, including: healing and protecting, understanding,
	enriching, and modeling the mind. These four aims were to be the result of “transdisciplinary
	and multiagency” research spanning “across disparate fields, such as cognitive
	science, medicine, neuroscience, psychology, mathematics, engineering, and computer
	science.” The proposal for a decade of the mind prompted many questions (See Spitzer
	2008). In this chapter, I address three of them: (1) How do proponents of this
	new decade conceive of the mind? (2) Why should a decade be devoted to understanding
	it? (3) What should this decade look like?'
	- source_sentence: This essay explores the historical and modern perspectives on the
	Gettier problem, highlighting the connections between this issue, skepticism,
	and relevance. Through methods such as historical analysis, induction, and deduction,
	it is found that while contextual theories and varying definitions of knowledge
	do not fully address skeptical challenges, they can help clarify our understanding
	of knowledge. Ultimately, embracing subjectivity and intuition can provide insight
	into what it truly means to claim knowledge.
	sentences:
	- In this article I present and analyze three popular moral justifications for hunting.
	My purpose is to expose the moral terrain of this issue and facilitate more fruitful,
	philosophically relevant discussions about the ethics of hunting.
	- Teaching competency in bioethics has been a concern since the field's inception.
	The first report on the teaching of contemporary bioethics was published in 1976
	by The Hastings Center, which concluded that graduate programs were not necessary
	at the time. However, the report speculated that future developments may require
	new academic structures for graduate education in bioethics. The creation of a
	terminal degree in bioethics has its critics, with scholars debating whether bioethics
	is a discipline with its own methods and theoretical grounding, a multidisciplinary
	field, or something else entirely. Despite these debates, new bioethics training
	programs have emerged at all postsecondary levels in the U.S. This essay examines
	the number and types of programs and degrees in this growing field.
	- 'Objective: In this essay, I will try to track some historical and modern stages
	of the discussion on the Gettier problem, and point out the interrelations of
	the questions that this problem raises for epistemologists, with sceptical arguments,
	and a so-called problem of relevance. Methods: historical analysis, induction,
	generalization, deduction, discourse, intuition results: Albeit the contextual
	theories of knowledge, the use of different definitions of knowledge, and the
	different ways of the uses of knowledge do not resolve all the issues that the
	sceptic can put forward, but they can be productive in giving clarity to a concept
	of knowledge for us. On the other hand, our knowledge will always have an element
	of intuition and subjectivity, however not equating to epistemic luck and probability. Significance
	novelty: the approach to the context in general, not giving up being a Subject
	may give us a clarity about the sense of what it means to say – “I know”.'
	model-index:
	- name: SentenceTransformer based on dbourget/pb-ds1-48K
	results:
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts dev
	type: sts-dev
	metrics:
	- type: pearson_cosine
	value: 0.9378177365442741
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8943299298202461
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.9709949018414847
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8969442622028955
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.9711044669329696
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8966133108746955
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.9419649751470724
	name: Pearson Dot
	- type: spearman_dot
	value: 0.8551487313582053
	name: Spearman Dot
	- type: pearson_max
	value: 0.9711044669329696
	name: Pearson Max
	- type: spearman_max
	value: 0.8969442622028955
	name: Spearman Max
	---

	# SentenceTransformer based on dbourget/pb-ds1-48K

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [dbourget/pb-ds1-48K](https://huggingface.co/dbourget/pb-ds1-48K). 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: [dbourget/pb-ds1-48K](https://huggingface.co/dbourget/pb-ds1-48K) <!-- at revision fcd4aeedcdc3ad836820d47fd28ffd2529914647 -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 768 tokens
	- Similarity Function: Cosine Similarity
	<!-- - Training Dataset: Unknown -->
	<!-- - Language: Unknown -->
	<!-- - License: Unknown -->

	### Model Sources

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

	### Full Model Architecture

	```
	SentenceTransformer(
	(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
	(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': 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})
	)
	```

	## 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("dbourget/pb-ds1-48K-philsim")
	# Run inference
	sentences = [
	'This essay explores the historical and modern perspectives on the Gettier problem, highlighting the connections between this issue, skepticism, and relevance. Through methods such as historical analysis, induction, and deduction, it is found that while contextual theories and varying definitions of knowledge do not fully address skeptical challenges, they can help clarify our understanding of knowledge. Ultimately, embracing subjectivity and intuition can provide insight into what it truly means to claim knowledge.',
	'Objective: In this essay, I will try to track some historical and modern stages of the discussion on the Gettier problem, and point out the interrelations of the questions that this problem raises for epistemologists, with sceptical arguments, and a so-called problem of relevance. Methods: historical analysis, induction, generalization, deduction, discourse, intuition results: Albeit the contextual theories of knowledge, the use of different definitions of knowledge, and the different ways of the uses of knowledge do not resolve all the issues that the sceptic can put forward, but they can be productive in giving clarity to a concept of knowledge for us. On the other hand, our knowledge will always have an element of intuition and subjectivity, however not equating to epistemic luck and probability. Significance novelty: the approach to the context in general, not giving up being a Subject may give us a clarity about the sense of what it means to say – “I know”.',
	"Teaching competency in bioethics has been a concern since the field's inception. The first report on the teaching of contemporary bioethics was published in 1976 by The Hastings Center, which concluded that graduate programs were not necessary at the time. However, the report speculated that future developments may require new academic structures for graduate education in bioethics. The creation of a terminal degree in bioethics has its critics, with scholars debating whether bioethics is a discipline with its own methods and theoretical grounding, a multidisciplinary field, or something else entirely. Despite these debates, new bioethics training programs have emerged at all postsecondary levels in the U.S. This essay examines the number and types of programs and degrees in this growing field.",
	]
	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

	#### Semantic Similarity
	* Dataset: `sts-dev`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.9378 \|
	\| spearman_cosine \| 0.8943 \|
	\| pearson_manhattan \| 0.971 \|
	\| spearman_manhattan \| 0.8969 \|
	\| pearson_euclidean \| 0.9711 \|
	\| spearman_euclidean \| 0.8966 \|
	\| pearson_dot \| 0.942 \|
	\| spearman_dot \| 0.8551 \|
	\| pearson_max \| 0.9711 \|
	\| spearman_max \| 0.8969 \|

	<!--
	## 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 Hyperparameters
	#### Non-Default Hyperparameters

	- `eval_strategy`: steps
	- `per_device_train_batch_size`: 190
	- `per_device_eval_batch_size`: 190
	- `learning_rate`: 5e-06
	- `num_train_epochs`: 2
	- `warmup_ratio`: 0.1
	- `bf16`: True
	- `batch_sampler`: no_duplicates

	#### 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`: 190
	- `per_device_eval_batch_size`: 190
	- `per_gpu_train_batch_size`: None
	- `per_gpu_eval_batch_size`: None
	- `gradient_accumulation_steps`: 1
	- `eval_accumulation_steps`: None
	- `learning_rate`: 5e-06
	- `weight_decay`: 0.0
	- `adam_beta1`: 0.9
	- `adam_beta2`: 0.999
	- `adam_epsilon`: 1e-08
	- `max_grad_norm`: 1.0
	- `num_train_epochs`: 2
	- `max_steps`: -1
	- `lr_scheduler_type`: linear
	- `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`: None
	- `local_rank`: 0
	- `ddp_backend`: None
	- `tpu_num_cores`: None
	- `tpu_metrics_debug`: False
	- `debug`: []
	- `dataloader_drop_last`: False
	- `dataloader_num_workers`: 0
	- `dataloader_prefetch_factor`: None
	- `past_index`: -1
	- `disable_tqdm`: False
	- `remove_unused_columns`: True
	- `label_names`: None
	- `load_best_model_at_end`: False
	- `ignore_data_skip`: False
	- `fsdp`: []
	- `fsdp_min_num_params`: 0
	- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
	- `fsdp_transformer_layer_cls_to_wrap`: None
	- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
	- `deepspeed`: None
	- `label_smoothing_factor`: 0.0
	- `optim`: adamw_torch
	- `optim_args`: None
	- `adafactor`: False
	- `group_by_length`: False
	- `length_column_name`: length
	- `ddp_find_unused_parameters`: None
	- `ddp_bucket_cap_mb`: None
	- `ddp_broadcast_buffers`: False
	- `dataloader_pin_memory`: True
	- `dataloader_persistent_workers`: False
	- `skip_memory_metrics`: True
	- `use_legacy_prediction_loop`: False
	- `push_to_hub`: False
	- `resume_from_checkpoint`: None
	- `hub_model_id`: None
	- `hub_strategy`: every_save
	- `hub_private_repo`: False
	- `hub_always_push`: False
	- `gradient_checkpointing`: False
	- `gradient_checkpointing_kwargs`: None
	- `include_inputs_for_metrics`: False
	- `eval_do_concat_batches`: True
	- `fp16_backend`: auto
	- `push_to_hub_model_id`: None
	- `push_to_hub_organization`: None
	- `mp_parameters`:
	- `auto_find_batch_size`: False
	- `full_determinism`: False
	- `torchdynamo`: None
	- `ray_scope`: last
	- `ddp_timeout`: 1800
	- `torch_compile`: False
	- `torch_compile_backend`: None
	- `torch_compile_mode`: None
	- `dispatch_batches`: None
	- `split_batches`: None
	- `include_tokens_per_second`: False
	- `include_num_input_tokens_seen`: False
	- `neftune_noise_alpha`: None
	- `optim_target_modules`: None
	- `batch_eval_metrics`: False
	- `eval_on_start`: False
	- `batch_sampler`: no_duplicates
	- `multi_dataset_batch_sampler`: proportional

	</details>

	### Training Logs
	<details><summary>Click to expand</summary>

	\| Epoch \| Step \| Training Loss \| loss \| sts-dev_spearman_cosine \|
	\|:------:\|:----:\|:-------------:\|:------:\|:-----------------------:\|
	\| 0 \| 0 \| - \| - \| 0.8229 \|
	\| 0.0178 \| 10 \| 0.0545 \| - \| - \|
	\| 0.0355 \| 20 \| 0.0556 \| - \| - \|
	\| 0.0533 \| 30 \| 0.0502 \| - \| - \|
	\| 0.0710 \| 40 \| 0.0497 \| - \| - \|
	\| 0.0888 \| 50 \| 0.0413 \| - \| - \|
	\| 0.1066 \| 60 \| 0.0334 \| - \| - \|
	\| 0.1243 \| 70 \| 0.0238 \| - \| - \|
	\| 0.1421 \| 80 \| 0.0206 \| - \| - \|
	\| 0.1599 \| 90 \| 0.0167 \| - \| - \|
	\| 0.1776 \| 100 \| 0.0146 \| 0.0725 \| 0.8788 \|
	\| 0.1954 \| 110 \| 0.0127 \| - \| - \|
	\| 0.2131 \| 120 \| 0.0125 \| - \| - \|
	\| 0.2309 \| 130 \| 0.0115 \| - \| - \|
	\| 0.2487 \| 140 \| 0.0116 \| - \| - \|
	\| 0.2664 \| 150 \| 0.0111 \| - \| - \|
	\| 0.2842 \| 160 \| 0.0107 \| - \| - \|
	\| 0.3020 \| 170 \| 0.0113 \| - \| - \|
	\| 0.3197 \| 180 \| 0.0106 \| - \| - \|
	\| 0.3375 \| 190 \| 0.0099 \| - \| - \|
	\| 0.3552 \| 200 \| 0.0092 \| 0.0207 \| 0.8856 \|
	\| 0.3730 \| 210 \| 0.0097 \| - \| - \|
	\| 0.3908 \| 220 \| 0.0099 \| - \| - \|
	\| 0.4085 \| 230 \| 0.0087 \| - \| - \|
	\| 0.4263 \| 240 \| 0.0087 \| - \| - \|
	\| 0.4440 \| 250 \| 0.0082 \| - \| - \|
	\| 0.4618 \| 260 \| 0.0083 \| - \| - \|
	\| 0.4796 \| 270 \| 0.0089 \| - \| - \|
	\| 0.4973 \| 280 \| 0.0082 \| - \| - \|
	\| 0.5151 \| 290 \| 0.0078 \| - \| - \|
	\| 0.5329 \| 300 \| 0.0081 \| 0.0078 \| 0.8891 \|
	\| 0.5506 \| 310 \| 0.0081 \| - \| - \|
	\| 0.5684 \| 320 \| 0.0072 \| - \| - \|
	\| 0.5861 \| 330 \| 0.0084 \| - \| - \|
	\| 0.6039 \| 340 \| 0.0083 \| - \| - \|
	\| 0.6217 \| 350 \| 0.0078 \| - \| - \|
	\| 0.6394 \| 360 \| 0.0077 \| - \| - \|
	\| 0.6572 \| 370 \| 0.008 \| - \| - \|
	\| 0.6750 \| 380 \| 0.0073 \| - \| - \|
	\| 0.6927 \| 390 \| 0.008 \| - \| - \|
	\| 0.7105 \| 400 \| 0.0073 \| 0.0058 \| 0.8890 \|
	\| 0.7282 \| 410 \| 0.0075 \| - \| - \|
	\| 0.7460 \| 420 \| 0.0077 \| - \| - \|
	\| 0.7638 \| 430 \| 0.0074 \| - \| - \|
	\| 0.7815 \| 440 \| 0.0073 \| - \| - \|
	\| 0.7993 \| 450 \| 0.007 \| - \| - \|
	\| 0.8171 \| 460 \| 0.0043 \| - \| - \|
	\| 0.8348 \| 470 \| 0.0052 \| - \| - \|
	\| 0.8526 \| 480 \| 0.0046 \| - \| - \|
	\| 0.8703 \| 490 \| 0.0073 \| - \| - \|
	\| 0.8881 \| 500 \| 0.0056 \| 0.0069 \| 0.8922 \|
	\| 0.9059 \| 510 \| 0.0059 \| - \| - \|
	\| 0.9236 \| 520 \| 0.0045 \| - \| - \|
	\| 0.9414 \| 530 \| 0.0033 \| - \| - \|
	\| 0.9591 \| 540 \| 0.0058 \| - \| - \|
	\| 0.9769 \| 550 \| 0.0056 \| - \| - \|
	\| 0.9947 \| 560 \| 0.0046 \| - \| - \|
	\| 1.0124 \| 570 \| 0.003 \| - \| - \|
	\| 1.0302 \| 580 \| 0.0039 \| - \| - \|
	\| 1.0480 \| 590 \| 0.0032 \| - \| - \|
	\| 1.0657 \| 600 \| 0.0031 \| 0.0029 \| 0.8931 \|
	\| 1.0835 \| 610 \| 0.0046 \| - \| - \|
	\| 1.1012 \| 620 \| 0.003 \| - \| - \|
	\| 1.1190 \| 630 \| 0.0021 \| - \| - \|
	\| 1.1368 \| 640 \| 0.0031 \| - \| - \|
	\| 1.1545 \| 650 \| 0.0035 \| - \| - \|
	\| 1.1723 \| 660 \| 0.0033 \| - \| - \|
	\| 1.1901 \| 670 \| 0.0024 \| - \| - \|
	\| 1.2078 \| 680 \| 0.0012 \| - \| - \|
	\| 1.2256 \| 690 \| 0.0075 \| - \| - \|
	\| 1.2433 \| 700 \| 0.0028 \| 0.0036 \| 0.8945 \|
	\| 1.2611 \| 710 \| 0.0033 \| - \| - \|
	\| 1.2789 \| 720 \| 0.0023 \| - \| - \|
	\| 1.2966 \| 730 \| 0.0034 \| - \| - \|
	\| 1.3144 \| 740 \| 0.0018 \| - \| - \|
	\| 1.3321 \| 750 \| 0.0016 \| - \| - \|
	\| 1.3499 \| 760 \| 0.0025 \| - \| - \|
	\| 1.3677 \| 770 \| 0.002 \| - \| - \|
	\| 1.3854 \| 780 \| 0.0016 \| - \| - \|
	\| 1.4032 \| 790 \| 0.0018 \| - \| - \|
	\| 1.4210 \| 800 \| 0.003 \| 0.0027 \| 0.8944 \|
	\| 1.4387 \| 810 \| 0.0018 \| - \| - \|
	\| 1.4565 \| 820 \| 0.0008 \| - \| - \|
	\| 1.4742 \| 830 \| 0.0014 \| - \| - \|
	\| 1.4920 \| 840 \| 0.0025 \| - \| - \|
	\| 1.5098 \| 850 \| 0.0026 \| - \| - \|
	\| 1.5275 \| 860 \| 0.0012 \| - \| - \|
	\| 1.5453 \| 870 \| 0.001 \| - \| - \|
	\| 1.5631 \| 880 \| 0.001 \| - \| - \|
	\| 1.5808 \| 890 \| 0.0012 \| - \| - \|
	\| 1.5986 \| 900 \| 0.0021 \| 0.0021 \| 0.8952 \|
	\| 1.6163 \| 910 \| 0.0016 \| - \| - \|
	\| 1.6341 \| 920 \| 0.0008 \| - \| - \|
	\| 1.6519 \| 930 \| 0.0008 \| - \| - \|
	\| 1.6696 \| 940 \| 0.0009 \| - \| - \|
	\| 1.6874 \| 950 \| 0.0004 \| - \| - \|
	\| 1.7052 \| 960 \| 0.0003 \| - \| - \|
	\| 1.7229 \| 970 \| 0.0007 \| - \| - \|
	\| 1.7407 \| 980 \| 0.0007 \| - \| - \|
	\| 1.7584 \| 990 \| 0.0011 \| - \| - \|
	\| 1.7762 \| 1000 \| 0.0007 \| 0.0029 \| 0.8952 \|
	\| 1.7940 \| 1010 \| 0.0008 \| - \| - \|
	\| 1.8117 \| 1020 \| 0.001 \| - \| - \|
	\| 1.8295 \| 1030 \| 0.0006 \| - \| - \|
	\| 1.8472 \| 1040 \| 0.0006 \| - \| - \|
	\| 1.8650 \| 1050 \| 0.0015 \| - \| - \|
	\| 1.8828 \| 1060 \| 0.0009 \| - \| - \|
	\| 1.9005 \| 1070 \| 0.0005 \| - \| - \|
	\| 1.9183 \| 1080 \| 0.0006 \| - \| - \|
	\| 1.9361 \| 1090 \| 0.0021 \| - \| - \|
	\| 1.9538 \| 1100 \| 0.0009 \| 0.0023 \| 0.8943 \|
	\| 1.9716 \| 1110 \| 0.0007 \| - \| - \|
	\| 1.9893 \| 1120 \| 0.0003 \| - \| - \|

	</details>

	### Framework Versions
	- Python: 3.10.12
	- Sentence Transformers: 3.0.1
	- Transformers: 4.42.3
	- PyTorch: 2.2.0+cu121
	- Accelerate: 0.31.0
	- Datasets: 2.20.0
	- Tokenizers: 0.19.1

	## Citation

	### BibTeX

	#### Sentence Transformers
	```bibtex
	@inproceedings{reimers-2019-sentence-bert,
	title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
	author = "Reimers, Nils and Gurevych, Iryna",
	booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
	month = "11",
	year = "2019",
	publisher = "Association for Computational Linguistics",
	url = "https://arxiv.org/abs/1908.10084",
	}
	```

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