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	---
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:1760
	- loss:MultipleNegativesRankingLoss
	base_model: WhereIsAI/UAE-Large-V1
	widget:
	- source_sentence: What is the relationship between the x- and y-coordinates in a
	linear relationship, and how can this relationship be represented visually on
	a graph?
	sentences:
	- '"A linear relationship is a relationship between variables such that when plotted
	on a coordinate plane, the points lie on a line." Additionally, "You can think
	of a line, then, as a collection of an infinite number of individual points that
	share the same mathematical relationship."'
	- '"A ''model'' is a situation-specific description of a phenomenon based on a theory,
	that allows us to make a specific prediction." and "In physics, it is particularly
	important to distinguish between these two terms. A model provides an immediate
	understanding of something based on a theory."'
	- '"Use capital letters to denote sets, $A,B, C, X, Y$ etc. [...] if you stick with
	these conventions people reading your work (including the person marking your
	exams) will know — ''Oh $A$ is that set they are talking about'' and ''$a$ is
	an element of that set.''"'
	- source_sentence: What factors influence whether thin-film interference results in
	constructive or destructive interference?
	sentences:
	- '"For nonrelativistic velocities, an observer moving along at the same velocity
	as an Ohmic conductor measures the usual Ohm''s law in his reference frame, $\textbf{J}_{f}''
	= \sigma \textbf{E}''$... the current density in all inertial frames is the same
	so that (3) in (4) gives us the generalized Ohm''s law as $\textbf{J}_{f}'' =
	\textbf{J}_{f} = \sigma (\textbf{E} + \textbf{v} \times \textbf{B})$ where v is
	the velocity of the conductor."'
	- '"Thin-film interference thus depends on film thickness, the wavelength of light,
	and the refractive indices."'
	- '"A summary of the properties of concave mirrors is shown below: • converging
	• real image • inverted • image in front of mirror. A summary of the properties
	of convex mirrors is shown below: • diverging • virtual image • upright • image
	behind mirror."'
	- source_sentence: How do non-conservative forces affect the total energy change in
	a system undergoing an irreversible process?
	sentences:
	- '"Energy is conserved but some mechanical energy has been transferred into nonrecoverable
	energy $W_{\mathrm{nc}}$. We shall refer to processes in which there is non-zero
	nonrecoverable energy as irreversible processes."'
	- '"Hamilton’s equations give $2s$ first-order differential equations for $p_{k},q_{k}$
	for each of the $s=n-m$ degrees of freedom. Lagrange’s equations give $s$ second-order
	differential equations for the $s$ independent generalized coordinates $q_{k},\dot{q}_{k}."'
	- '"Determine what happens as $\Delta x$ approaches 0."'
	- source_sentence: What are the conditions under which a mutant virus is likely to
	replace a wildtype virus in a population, according to the SIR model of disease
	dynamics?
	sentences:
	- '"In the limit of high Reynolds number, viscosity disappears from the problem
	and the drag force should not depend on viscosity. This reasoning contains several
	subtle untruths, yet its conclusion is mostly correct. ... To make $ F $ independent
	of viscosity, $ F $ must be independent of Reynolds number!"'
	- '"A more mathematically rigorous name would be the renormalization monoid."'
	- '"I^{\prime}$ increases exponentially if $\frac{\beta^{\prime}(d+c+\gamma)}{\beta}-\left(d+c^{\prime}+\gamma^{\prime}\right)>0$
	or after some elementary algebra, $\frac{\beta^{\prime}}{d+c^{\prime}+\gamma^{\prime}}>\frac{\beta}{d+c+\gamma}$."
	Additionally, "our result (4.6.8) suggests that endemic viruses (or other microorganisms)
	will tend to evolve (i) to be more easily transmitted between people $\left(\beta^{\prime}>\beta\right)
	;$ (ii) to make people sick longer $\left(\gamma^{\prime}<\gamma\right)$, and;
	(iii) to be less deadly $c^{\prime}<c$."'
	- source_sentence: What is the relationship between the smallest perturbation of a
	matrix and its rank, as established in theorems regarding matrix perturbations?
	sentences:
	- '"Suppose $A \in C^{m \times n}$ has full column rank (= n). Then $\min _{\Delta
	\in \mathbb{C}^{m \times n}}\left\{\\|\Delta\\|_{2} \mid A+\Delta \text { has rank
	}<n\right\}=\sigma_{n}(A)$."'
	- '"Complementary angles have measures that add up to 90 degrees."'
	- '"If a beam of light enters and then exits the elevator, the observer on Earth
	and the one accelerating in empty space must observe the same thing, since they
	cannot distinguish between being on Earth or accelerating in space. The observer
	in space, who is accelerating, will observe that the beam of light bends as it
	crosses the elevator... that means that if the path of a beam of light is curved
	near Earth, it must be because space itself is curved in the presence of a gravitational
	field!"'
	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 WhereIsAI/UAE-Large-V1
	results:
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: eval
	type: eval
	metrics:
	- type: cosine_accuracy@1
	value: 0.6142857142857143
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.7357142857142858
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.7833333333333333
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.8380952380952381
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.6142857142857143
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.24523809523809523
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.15666666666666665
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.08380952380952378
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.6142857142857143
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.7357142857142858
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.7833333333333333
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.8380952380952381
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.7234956246301203
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.6871305744520029
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.6925322242948972
	name: Cosine Map@100
	---

	# SentenceTransformer based on WhereIsAI/UAE-Large-V1

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [WhereIsAI/UAE-Large-V1](https://huggingface.co/WhereIsAI/UAE-Large-V1). 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: [WhereIsAI/UAE-Large-V1](https://huggingface.co/WhereIsAI/UAE-Large-V1) <!-- at revision f4264cd240f4e46a527f9f57a70cda6c2a12d248 -->
	- 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})
	)
	```

	## 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("cyberbabooshka/uae_large_ft1")
	# Run inference
	sentences = [
	'What is the relationship between the smallest perturbation of a matrix and its rank, as established in theorems regarding matrix perturbations?',
	'"Suppose $A \\in C^{m \\times n}$ has full column rank (= n). Then $\\min _{\\Delta \\in \\mathbb{C}^{m \\times n}}\\left\\{\\\|\\Delta\\\|_{2} \\mid A+\\Delta \\text { has rank }<n\\right\\}=\\sigma_{n}(A)$."',
	'"If a beam of light enters and then exits the elevator, the observer on Earth and the one accelerating in empty space must observe the same thing, since they cannot distinguish between being on Earth or accelerating in space. The observer in space, who is accelerating, will observe that the beam of light bends as it crosses the elevator... that means that if the path of a beam of light is curved near Earth, it must be because space itself is curved in the presence of a gravitational field!"',
	]
	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

	* Dataset: `eval`
	* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| cosine_accuracy@1 \| 0.6143 \|
	\| cosine_accuracy@3 \| 0.7357 \|
	\| cosine_accuracy@5 \| 0.7833 \|
	\| cosine_accuracy@10 \| 0.8381 \|
	\| cosine_precision@1 \| 0.6143 \|
	\| cosine_precision@3 \| 0.2452 \|
	\| cosine_precision@5 \| 0.1567 \|
	\| cosine_precision@10 \| 0.0838 \|
	\| cosine_recall@1 \| 0.6143 \|
	\| cosine_recall@3 \| 0.7357 \|
	\| cosine_recall@5 \| 0.7833 \|
	\| cosine_recall@10 \| 0.8381 \|
	\| cosine_ndcg@10 \| 0.7235 \|
	\| cosine_mrr@10 \| 0.6871 \|
	\| cosine_map@100 \| 0.6925 \|

	<!--
	## 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: 1,760 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: 9 tokens</li><li>mean: 24.87 tokens</li><li>max: 70 tokens</li></ul> \| <ul><li>min: 11 tokens</li><li>mean: 68.37 tokens</li><li>max: 500 tokens</li></ul> \|
	* Samples:
	\| anchor \| positive \|
	\|:---------------------------------------------------------------------------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>How is a proper coloring of a graph defined in the context of vertices and edges?</code> \| <code>"A coloring is called proper if for each edge joining two distinct vertices, the two vertices it joins have different colors."</code> \|
	\| <code>What is the relationship between the first excited state of the box model and the p orbitals in a hydrogen atom?</code> \| <code>"The p orbitals are similar to the first excited state of the box, i.e. $(n_{x},n_{y},n_{z})=(2,1,1)$ is similar to a $p_{x}$ orbital, $(n_{x},n_{y},n_{z})=(1,2,1)$ is similar to a $p_{y}$ orbital and $(n_{x},n_{y},n_{z})=(1,1,2)$ is similar to a $p_{z}$ orbital."</code> \|
	\| <code>How can the behavior of the derivative $ f'(x) $ indicate the presence of a local maximum or minimum at a critical point $ x=a $?</code> \| <code>"If there is a local maximum when $ x=a $, the function must be lower near $ x=a $ than it is right at $ x=a $. If the derivative exists near $ x=a $, this means $ f'(x)>0 $ when $ x $ is near $ a $ and $ x < a $, because the function must 'slope up' just to the left of $ a $. Similarly, $ f'(x) < 0 $ when $ x $ is near $ a $ and $ x>a $, because $ f $ slopes down from the local maximum as we move to the right. Using the same reasoning, if there is a local minimum at $ x=a $, the derivative of $ f $ must be negative just to the left of $ a $ and positive just to the right."</code> \|
	* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
	```json
	{
	"scale": 20.0,
	"similarity_fct": "cos_sim"
	}
	```

	### Evaluation Dataset

	#### Unnamed Dataset

	* Size: 420 evaluation samples
	* Columns: <code>anchor</code> and <code>positive</code>
	* Approximate statistics based on the first 420 samples:
	\| \| anchor \| positive \|
	\|:--------\|:-----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|
	\| type \| string \| string \|
	\| details \| <ul><li>min: 12 tokens</li><li>mean: 24.97 tokens</li><li>max: 66 tokens</li></ul> \| <ul><li>min: 7 tokens</li><li>mean: 68.52 tokens</li><li>max: 452 tokens</li></ul> \|
	* Samples:
	\| anchor \| positive \|
	\|:------------------------------------------------------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>What are the two central classes mentioned in the FileSystem framework and what do they represent?</code> \| <code>"The class `FileReference` is the most important entry point to the framework." and "FileSystem is a powerful and elegant library to manipulate files."</code> \|
	\| <code>What is the significance of Turing's work in the context of PDE-based models for self-organization of complex systems?</code> \| <code>"Turing’s monumental work on the chemical basis of morphogenesis played an important role in igniting researchers’ attention to the PDE-based continuous field models as a mathematical framework to study self-organization of complex systems."</code> \|
	\| <code>What are the two options for reducing accelerations as discussed in the passage?</code> \| <code>"From the above definitions we see that there are really two options for reducing accelerations. We can reduce the amount that velocity changes, or we can increase the time over which the velocity changes (or both)."</code> \|
	* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
	```json
	{
	"scale": 20.0,
	"similarity_fct": "cos_sim"
	}
	```

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

	- `eval_strategy`: epoch
	- `per_device_train_batch_size`: 16
	- `per_device_eval_batch_size`: 16
	- `learning_rate`: 2e-05
	- `weight_decay`: 0.05
	- `num_train_epochs`: 10
	- `warmup_ratio`: 0.1
	- `fp16`: True
	- `eval_on_start`: True

	#### 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`: 16
	- `per_device_eval_batch_size`: 16
	- `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`: 2e-05
	- `weight_decay`: 0.05
	- `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`: 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`: False
	- `fp16`: True
	- `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
	- `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`: True
	- `use_liger_kernel`: False
	- `eval_use_gather_object`: False
	- `average_tokens_across_devices`: False
	- `prompts`: None
	- `batch_sampler`: batch_sampler
	- `multi_dataset_batch_sampler`: proportional

	</details>

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

	\| Epoch \| Step \| Training Loss \| Validation Loss \| eval_cosine_ndcg@10 \|
	\|:------:\|:----:\|:-------------:\|:---------------:\|:-------------------:\|
	\| 0 \| 0 \| - \| 0.0971 \| 0.6824 \|
	\| 0.0091 \| 1 \| 0.1198 \| - \| - \|
	\| 0.0182 \| 2 \| 0.0787 \| - \| - \|
	\| 0.0273 \| 3 \| 0.0614 \| - \| - \|
	\| 0.0364 \| 4 \| 0.138 \| - \| - \|
	\| 0.0455 \| 5 \| 0.1204 \| - \| - \|
	\| 0.0545 \| 6 \| 0.1885 \| - \| - \|
	\| 0.0636 \| 7 \| 0.0475 \| - \| - \|
	\| 0.0727 \| 8 \| 0.1358 \| - \| - \|
	\| 0.0818 \| 9 \| 0.1666 \| - \| - \|
	\| 0.0909 \| 10 \| 0.0737 \| - \| - \|
	\| 0.1 \| 11 \| 0.0997 \| - \| - \|
	\| 0.1091 \| 12 \| 0.0795 \| - \| - \|
	\| 0.1182 \| 13 \| 0.1071 \| - \| - \|
	\| 0.1273 \| 14 \| 0.1224 \| - \| - \|
	\| 0.1364 \| 15 \| 0.0499 \| - \| - \|
	\| 0.1455 \| 16 \| 0.0806 \| - \| - \|
	\| 0.1545 \| 17 \| 0.0353 \| - \| - \|
	\| 0.1636 \| 18 \| 0.0542 \| - \| - \|
	\| 0.1727 \| 19 \| 0.0412 \| - \| - \|
	\| 0.1818 \| 20 \| 0.1375 \| - \| - \|
	\| 0.1909 \| 21 \| 0.1124 \| - \| - \|
	\| 0.2 \| 22 \| 0.0992 \| - \| - \|
	\| 0.2091 \| 23 \| 0.0285 \| - \| - \|
	\| 0.2182 \| 24 \| 0.0337 \| - \| - \|
	\| 0.2273 \| 25 \| 0.0737 \| - \| - \|
	\| 0.2364 \| 26 \| 0.2011 \| - \| - \|
	\| 0.2455 \| 27 \| 0.0241 \| - \| - \|
	\| 0.2545 \| 28 \| 0.1319 \| - \| - \|
	\| 0.2636 \| 29 \| 0.0104 \| - \| - \|
	\| 0.2727 \| 30 \| 0.0162 \| - \| - \|
	\| 0.2818 \| 31 \| 0.3061 \| - \| - \|
	\| 0.2909 \| 32 \| 0.0422 \| - \| - \|
	\| 0.3 \| 33 \| 0.1893 \| - \| - \|
	\| 0.3091 \| 34 \| 0.0207 \| - \| - \|
	\| 0.3182 \| 35 \| 0.0744 \| - \| - \|
	\| 0.3273 \| 36 \| 0.0246 \| - \| - \|
	\| 0.3364 \| 37 \| 0.0079 \| - \| - \|
	\| 0.3455 \| 38 \| 0.0256 \| - \| - \|
	\| 0.3545 \| 39 \| 0.0224 \| - \| - \|
	\| 0.3636 \| 40 \| 0.0151 \| - \| - \|
	\| 0.3727 \| 41 \| 0.0738 \| - \| - \|
	\| 0.3818 \| 42 \| 0.0239 \| - \| - \|
	\| 0.3909 \| 43 \| 0.0169 \| - \| - \|
	\| 0.4 \| 44 \| 0.0152 \| - \| - \|
	\| 0.4091 \| 45 \| 0.0244 \| - \| - \|
	\| 0.4182 \| 46 \| 0.1708 \| - \| - \|
	\| 0.4273 \| 47 \| 0.0146 \| - \| - \|
	\| 0.4364 \| 48 \| 0.1367 \| - \| - \|
	\| 0.4455 \| 49 \| 0.049 \| - \| - \|
	\| 0.4545 \| 50 \| 0.0211 \| - \| - \|
	\| 0.4636 \| 51 \| 0.0135 \| - \| - \|
	\| 0.4727 \| 52 \| 0.0668 \| - \| - \|
	\| 0.4818 \| 53 \| 0.087 \| - \| - \|
	\| 0.4909 \| 54 \| 0.0046 \| - \| - \|
	\| 0.5 \| 55 \| 0.0032 \| - \| - \|
	\| 0.5091 \| 56 \| 0.0133 \| - \| - \|
	\| 0.5182 \| 57 \| 0.0109 \| - \| - \|
	\| 0.5273 \| 58 \| 0.0396 \| - \| - \|
	\| 0.5364 \| 59 \| 0.0291 \| - \| - \|
	\| 0.5455 \| 60 \| 0.0299 \| - \| - \|
	\| 0.5545 \| 61 \| 0.0134 \| - \| - \|
	\| 0.5636 \| 62 \| 0.0135 \| - \| - \|
	\| 0.5727 \| 63 \| 0.0049 \| - \| - \|
	\| 0.5818 \| 64 \| 0.0199 \| - \| - \|
	\| 0.5909 \| 65 \| 0.1533 \| - \| - \|
	\| 0.6 \| 66 \| 0.3639 \| - \| - \|
	\| 0.6091 \| 67 \| 0.0652 \| - \| - \|
	\| 0.6182 \| 68 \| 0.0315 \| - \| - \|
	\| 0.6273 \| 69 \| 0.0403 \| - \| - \|
	\| 0.6364 \| 70 \| 0.011 \| - \| - \|
	\| 0.6455 \| 71 \| 0.0265 \| - \| - \|
	\| 0.6545 \| 72 \| 0.1146 \| - \| - \|
	\| 0.6636 \| 73 \| 0.0932 \| - \| - \|
	\| 0.6727 \| 74 \| 0.0234 \| - \| - \|
	\| 0.6818 \| 75 \| 0.0581 \| - \| - \|
	\| 0.6909 \| 76 \| 0.0132 \| - \| - \|
	\| 0.7 \| 77 \| 0.1183 \| - \| - \|
	\| 0.7091 \| 78 \| 0.0913 \| - \| - \|
	\| 0.7182 \| 79 \| 0.0262 \| - \| - \|
	\| 0.7273 \| 80 \| 0.0262 \| - \| - \|
	\| 0.7364 \| 81 \| 0.0159 \| - \| - \|
	\| 0.7455 \| 82 \| 0.0407 \| - \| - \|
	\| 0.7545 \| 83 \| 0.0294 \| - \| - \|
	\| 0.7636 \| 84 \| 0.0567 \| - \| - \|
	\| 0.7727 \| 85 \| 0.0959 \| - \| - \|
	\| 0.7818 \| 86 \| 0.033 \| - \| - \|
	\| 0.7909 \| 87 \| 0.0234 \| - \| - \|
	\| 0.8 \| 88 \| 0.0088 \| - \| - \|
	\| 0.8091 \| 89 \| 0.0249 \| - \| - \|
	\| 0.8182 \| 90 \| 0.0276 \| - \| - \|
	\| 0.8273 \| 91 \| 0.0936 \| - \| - \|
	\| 0.8364 \| 92 \| 0.0067 \| - \| - \|
	\| 0.8455 \| 93 \| 0.0064 \| - \| - \|
	\| 0.8545 \| 94 \| 0.0654 \| - \| - \|
	\| 0.8636 \| 95 \| 0.0048 \| - \| - \|
	\| 0.8727 \| 96 \| 0.0087 \| - \| - \|
	\| 0.8818 \| 97 \| 0.0115 \| - \| - \|
	\| 0.8909 \| 98 \| 0.0092 \| - \| - \|
	\| 0.9 \| 99 \| 0.0514 \| - \| - \|
	\| 0.9091 \| 100 \| 0.1856 \| - \| - \|
	\| 0.9182 \| 101 \| 0.0364 \| - \| - \|
	\| 0.9273 \| 102 \| 0.0455 \| - \| - \|
	\| 0.9364 \| 103 \| 0.0057 \| - \| - \|
	\| 0.9455 \| 104 \| 0.0038 \| - \| - \|
	\| 0.9545 \| 105 \| 0.0209 \| - \| - \|
	\| 0.9636 \| 106 \| 0.0247 \| - \| - \|
	\| 0.9727 \| 107 \| 0.0735 \| - \| - \|
	\| 0.9818 \| 108 \| 0.004 \| - \| - \|
	\| 0.9909 \| 109 \| 0.0174 \| - \| - \|
	\| 1.0 \| 110 \| 0.018 \| 0.0282 \| 0.7093 \|
	\| 1.0091 \| 111 \| 0.0187 \| - \| - \|
	\| 1.0182 \| 112 \| 0.0116 \| - \| - \|
	\| 1.0273 \| 113 \| 0.0043 \| - \| - \|
	\| 1.0364 \| 114 \| 0.0059 \| - \| - \|
	\| 1.0455 \| 115 \| 0.0067 \| - \| - \|
	\| 1.0545 \| 116 \| 0.0093 \| - \| - \|
	\| 1.0636 \| 117 \| 0.0821 \| - \| - \|
	\| 1.0727 \| 118 \| 0.0097 \| - \| - \|
	\| 1.0818 \| 119 \| 0.0141 \| - \| - \|
	\| 1.0909 \| 120 \| 0.0202 \| - \| - \|
	\| 1.1 \| 121 \| 0.0034 \| - \| - \|
	\| 1.1091 \| 122 \| 0.0025 \| - \| - \|
	\| 1.1182 \| 123 \| 0.006 \| - \| - \|
	\| 1.1273 \| 124 \| 0.004 \| - \| - \|
	\| 1.1364 \| 125 \| 0.003 \| - \| - \|
	\| 1.1455 \| 126 \| 0.0399 \| - \| - \|
	\| 1.1545 \| 127 \| 0.0026 \| - \| - \|
	\| 1.1636 \| 128 \| 0.0043 \| - \| - \|
	\| 1.1727 \| 129 \| 0.1317 \| - \| - \|
	\| 1.1818 \| 130 \| 0.0024 \| - \| - \|
	\| 1.1909 \| 131 \| 0.0027 \| - \| - \|
	\| 1.2 \| 132 \| 0.076 \| - \| - \|
	\| 1.2091 \| 133 \| 0.0302 \| - \| - \|
	\| 1.2182 \| 134 \| 0.0026 \| - \| - \|
	\| 1.2273 \| 135 \| 0.1611 \| - \| - \|
	\| 1.2364 \| 136 \| 0.0413 \| - \| - \|
	\| 1.2455 \| 137 \| 0.0118 \| - \| - \|
	\| 1.2545 \| 138 \| 0.0042 \| - \| - \|
	\| 1.2636 \| 139 \| 0.0401 \| - \| - \|
	\| 1.2727 \| 140 \| 0.0036 \| - \| - \|
	\| 1.2818 \| 141 \| 0.0034 \| - \| - \|
	\| 1.2909 \| 142 \| 0.0026 \| - \| - \|
	\| 1.3 \| 143 \| 0.0044 \| - \| - \|
	\| 1.3091 \| 144 \| 0.0024 \| - \| - \|
	\| 1.3182 \| 145 \| 0.0036 \| - \| - \|
	\| 1.3273 \| 146 \| 0.0242 \| - \| - \|
	\| 1.3364 \| 147 \| 0.0015 \| - \| - \|
	\| 1.3455 \| 148 \| 0.1008 \| - \| - \|
	\| 1.3545 \| 149 \| 0.0057 \| - \| - \|
	\| 1.3636 \| 150 \| 0.0062 \| - \| - \|
	\| 1.3727 \| 151 \| 0.0048 \| - \| - \|
	\| 1.3818 \| 152 \| 0.0026 \| - \| - \|
	\| 1.3909 \| 153 \| 0.0045 \| - \| - \|
	\| 1.4 \| 154 \| 0.0139 \| - \| - \|
	\| 1.4091 \| 155 \| 0.0017 \| - \| - \|
	\| 1.4182 \| 156 \| 0.0012 \| - \| - \|
	\| 1.4273 \| 157 \| 0.0009 \| - \| - \|
	\| 1.4364 \| 158 \| 0.006 \| - \| - \|
	\| 1.4455 \| 159 \| 0.0618 \| - \| - \|
	\| 1.4545 \| 160 \| 0.0889 \| - \| - \|
	\| 1.4636 \| 161 \| 0.0034 \| - \| - \|
	\| 1.4727 \| 162 \| 0.0184 \| - \| - \|
	\| 1.4818 \| 163 \| 0.0035 \| - \| - \|
	\| 1.4909 \| 164 \| 0.002 \| - \| - \|
	\| 1.5 \| 165 \| 0.0115 \| - \| - \|
	\| 1.5091 \| 166 \| 0.0008 \| - \| - \|
	\| 1.5182 \| 167 \| 0.0113 \| - \| - \|
	\| 1.5273 \| 168 \| 0.01 \| - \| - \|
	\| 1.5364 \| 169 \| 0.0177 \| - \| - \|
	\| 1.5455 \| 170 \| 0.0059 \| - \| - \|
	\| 1.5545 \| 171 \| 0.0123 \| - \| - \|
	\| 1.5636 \| 172 \| 0.0103 \| - \| - \|
	\| 1.5727 \| 173 \| 0.008 \| - \| - \|
	\| 1.5818 \| 174 \| 0.002 \| - \| - \|
	\| 1.5909 \| 175 \| 0.0039 \| - \| - \|
	\| 1.6 \| 176 \| 0.0174 \| - \| - \|
	\| 1.6091 \| 177 \| 0.0191 \| - \| - \|
	\| 1.6182 \| 178 \| 0.002 \| - \| - \|
	\| 1.6273 \| 179 \| 0.0009 \| - \| - \|
	\| 1.6364 \| 180 \| 0.0021 \| - \| - \|
	\| 1.6455 \| 181 \| 0.0011 \| - \| - \|
	\| 1.6545 \| 182 \| 0.0027 \| - \| - \|
	\| 1.6636 \| 183 \| 0.0005 \| - \| - \|
	\| 1.6727 \| 184 \| 0.0026 \| - \| - \|
	\| 1.6818 \| 185 \| 0.0047 \| - \| - \|
	\| 1.6909 \| 186 \| 0.0033 \| - \| - \|
	\| 1.7 \| 187 \| 0.0402 \| - \| - \|
	\| 1.7091 \| 188 \| 0.0128 \| - \| - \|
	\| 1.7182 \| 189 \| 0.01 \| - \| - \|
	\| 1.7273 \| 190 \| 0.0057 \| - \| - \|
	\| 1.7364 \| 191 \| 0.0133 \| - \| - \|
	\| 1.7455 \| 192 \| 0.0099 \| - \| - \|
	\| 1.7545 \| 193 \| 0.1022 \| - \| - \|
	\| 1.7636 \| 194 \| 0.0223 \| - \| - \|
	\| 1.7727 \| 195 \| 0.0037 \| - \| - \|
	\| 1.7818 \| 196 \| 0.0073 \| - \| - \|
	\| 1.7909 \| 197 \| 0.0212 \| - \| - \|
	\| 1.8 \| 198 \| 0.0231 \| - \| - \|
	\| 1.8091 \| 199 \| 0.0016 \| - \| - \|
	\| 1.8182 \| 200 \| 0.0017 \| - \| - \|
	\| 1.8273 \| 201 \| 0.0035 \| - \| - \|
	\| 1.8364 \| 202 \| 0.0165 \| - \| - \|
	\| 1.8455 \| 203 \| 0.0131 \| - \| - \|
	\| 1.8545 \| 204 \| 0.0032 \| - \| - \|
	\| 1.8636 \| 205 \| 0.0075 \| - \| - \|
	\| 1.8727 \| 206 \| 0.0438 \| - \| - \|
	\| 1.8818 \| 207 \| 0.0022 \| - \| - \|
	\| 1.8909 \| 208 \| 0.0501 \| - \| - \|
	\| 1.9 \| 209 \| 0.0121 \| - \| - \|
	\| 1.9091 \| 210 \| 0.0036 \| - \| - \|
	\| 1.9182 \| 211 \| 0.0041 \| - \| - \|
	\| 1.9273 \| 212 \| 0.0048 \| - \| - \|
	\| 1.9364 \| 213 \| 0.0159 \| - \| - \|
	\| 1.9455 \| 214 \| 0.0036 \| - \| - \|
	\| 1.9545 \| 215 \| 0.0035 \| - \| - \|
	\| 1.9636 \| 216 \| 0.004 \| - \| - \|
	\| 1.9727 \| 217 \| 0.0039 \| - \| - \|
	\| 1.9818 \| 218 \| 0.0177 \| - \| - \|
	\| 1.9909 \| 219 \| 0.0042 \| - \| - \|
	\| 2.0 \| 220 \| 0.0044 \| 0.0230 \| 0.7225 \|
	\| 2.0091 \| 221 \| 0.0339 \| - \| - \|
	\| 2.0182 \| 222 \| 0.0032 \| - \| - \|
	\| 2.0273 \| 223 \| 0.0133 \| - \| - \|
	\| 2.0364 \| 224 \| 0.0031 \| - \| - \|
	\| 2.0455 \| 225 \| 0.0025 \| - \| - \|
	\| 2.0545 \| 226 \| 0.0039 \| - \| - \|
	\| 2.0636 \| 227 \| 0.0011 \| - \| - \|
	\| 2.0727 \| 228 \| 0.0021 \| - \| - \|
	\| 2.0818 \| 229 \| 0.0591 \| - \| - \|
	\| 2.0909 \| 230 \| 0.0011 \| - \| - \|
	\| 2.1 \| 231 \| 0.0008 \| - \| - \|
	\| 2.1091 \| 232 \| 0.0014 \| - \| - \|
	\| 2.1182 \| 233 \| 0.0057 \| - \| - \|
	\| 2.1273 \| 234 \| 0.0044 \| - \| - \|
	\| 2.1364 \| 235 \| 0.001 \| - \| - \|
	\| 2.1455 \| 236 \| 0.0009 \| - \| - \|
	\| 2.1545 \| 237 \| 0.0028 \| - \| - \|
	\| 2.1636 \| 238 \| 0.0076 \| - \| - \|
	\| 2.1727 \| 239 \| 0.0018 \| - \| - \|
	\| 2.1818 \| 240 \| 0.0022 \| - \| - \|
	\| 2.1909 \| 241 \| 0.0029 \| - \| - \|
	\| 2.2 \| 242 \| 0.0004 \| - \| - \|
	\| 2.2091 \| 243 \| 0.0025 \| - \| - \|
	\| 2.2182 \| 244 \| 0.0013 \| - \| - \|
	\| 2.2273 \| 245 \| 0.0487 \| - \| - \|
	\| 2.2364 \| 246 \| 0.0016 \| - \| - \|
	\| 2.2455 \| 247 \| 0.0023 \| - \| - \|
	\| 2.2545 \| 248 \| 0.0038 \| - \| - \|
	\| 2.2636 \| 249 \| 0.003 \| - \| - \|
	\| 2.2727 \| 250 \| 0.0017 \| - \| - \|
	\| 2.2818 \| 251 \| 0.0056 \| - \| - \|
	\| 2.2909 \| 252 \| 0.0036 \| - \| - \|
	\| 2.3 \| 253 \| 0.0016 \| - \| - \|
	\| 2.3091 \| 254 \| 0.0021 \| - \| - \|
	\| 2.3182 \| 255 \| 0.0019 \| - \| - \|
	\| 2.3273 \| 256 \| 0.001 \| - \| - \|
	\| 2.3364 \| 257 \| 0.0017 \| - \| - \|
	\| 2.3455 \| 258 \| 0.0027 \| - \| - \|
	\| 2.3545 \| 259 \| 0.0039 \| - \| - \|
	\| 2.3636 \| 260 \| 0.0011 \| - \| - \|
	\| 2.3727 \| 261 \| 0.0248 \| - \| - \|
	\| 2.3818 \| 262 \| 0.0219 \| - \| - \|
	\| 2.3909 \| 263 \| 0.0015 \| - \| - \|
	\| 2.4 \| 264 \| 0.0009 \| - \| - \|
	\| 2.4091 \| 265 \| 0.0013 \| - \| - \|
	\| 2.4182 \| 266 \| 0.0049 \| - \| - \|
	\| 2.4273 \| 267 \| 0.0073 \| - \| - \|
	\| 2.4364 \| 268 \| 0.007 \| - \| - \|
	\| 2.4455 \| 269 \| 0.0024 \| - \| - \|
	\| 2.4545 \| 270 \| 0.0008 \| - \| - \|
	\| 2.4636 \| 271 \| 0.001 \| - \| - \|
	\| 2.4727 \| 272 \| 0.0016 \| - \| - \|
	\| 2.4818 \| 273 \| 0.0007 \| - \| - \|
	\| 2.4909 \| 274 \| 0.0091 \| - \| - \|
	\| 2.5 \| 275 \| 0.0127 \| - \| - \|
	\| 2.5091 \| 276 \| 0.0013 \| - \| - \|
	\| 2.5182 \| 277 \| 0.001 \| - \| - \|
	\| 2.5273 \| 278 \| 0.0006 \| - \| - \|
	\| 2.5364 \| 279 \| 0.005 \| - \| - \|
	\| 2.5455 \| 280 \| 0.0154 \| - \| - \|
	\| 2.5545 \| 281 \| 0.0015 \| - \| - \|
	\| 2.5636 \| 282 \| 0.0229 \| - \| - \|
	\| 2.5727 \| 283 \| 0.0026 \| - \| - \|
	\| 2.5818 \| 284 \| 0.0008 \| - \| - \|
	\| 2.5909 \| 285 \| 0.0024 \| - \| - \|
	\| 2.6 \| 286 \| 0.0012 \| - \| - \|
	\| 2.6091 \| 287 \| 0.0748 \| - \| - \|
	\| 2.6182 \| 288 \| 0.0086 \| - \| - \|
	\| 2.6273 \| 289 \| 0.0013 \| - \| - \|
	\| 2.6364 \| 290 \| 0.0089 \| - \| - \|
	\| 2.6455 \| 291 \| 0.0011 \| - \| - \|
	\| 2.6545 \| 292 \| 0.0096 \| - \| - \|
	\| 2.6636 \| 293 \| 0.1416 \| - \| - \|
	\| 2.6727 \| 294 \| 0.0005 \| - \| - \|
	\| 2.6818 \| 295 \| 0.0021 \| - \| - \|
	\| 2.6909 \| 296 \| 0.0014 \| - \| - \|
	\| 2.7 \| 297 \| 0.0097 \| - \| - \|
	\| 2.7091 \| 298 \| 0.0014 \| - \| - \|
	\| 2.7182 \| 299 \| 0.0009 \| - \| - \|
	\| 2.7273 \| 300 \| 0.0016 \| - \| - \|
	\| 2.7364 \| 301 \| 0.0166 \| - \| - \|
	\| 2.7455 \| 302 \| 0.0028 \| - \| - \|
	\| 2.7545 \| 303 \| 0.0014 \| - \| - \|
	\| 2.7636 \| 304 \| 0.0018 \| - \| - \|
	\| 2.7727 \| 305 \| 0.0059 \| - \| - \|
	\| 2.7818 \| 306 \| 0.0012 \| - \| - \|
	\| 2.7909 \| 307 \| 0.0008 \| - \| - \|
	\| 2.8 \| 308 \| 0.0007 \| - \| - \|
	\| 2.8091 \| 309 \| 0.0038 \| - \| - \|
	\| 2.8182 \| 310 \| 0.0012 \| - \| - \|
	\| 2.8273 \| 311 \| 0.0091 \| - \| - \|
	\| 2.8364 \| 312 \| 0.0111 \| - \| - \|
	\| 2.8455 \| 313 \| 0.0016 \| - \| - \|
	\| 2.8545 \| 314 \| 0.0089 \| - \| - \|
	\| 2.8636 \| 315 \| 0.0071 \| - \| - \|
	\| 2.8727 \| 316 \| 0.0012 \| - \| - \|
	\| 2.8818 \| 317 \| 0.0251 \| - \| - \|
	\| 2.8909 \| 318 \| 0.0017 \| - \| - \|
	\| 2.9 \| 319 \| 0.0006 \| - \| - \|
	\| 2.9091 \| 320 \| 0.0014 \| - \| - \|
	\| 2.9182 \| 321 \| 0.0011 \| - \| - \|
	\| 2.9273 \| 322 \| 0.0084 \| - \| - \|
	\| 2.9364 \| 323 \| 0.0055 \| - \| - \|
	\| 2.9455 \| 324 \| 0.0011 \| - \| - \|
	\| 2.9545 \| 325 \| 0.0017 \| - \| - \|
	\| 2.9636 \| 326 \| 0.0008 \| - \| - \|
	\| 2.9727 \| 327 \| 0.0082 \| - \| - \|
	\| 2.9818 \| 328 \| 0.0006 \| - \| - \|
	\| 2.9909 \| 329 \| 0.0008 \| - \| - \|
	\| 3.0 \| 330 \| 0.0022 \| 0.0275 \| 0.6950 \|
	\| 3.0091 \| 331 \| 0.0007 \| - \| - \|
	\| 3.0182 \| 332 \| 0.0012 \| - \| - \|
	\| 3.0273 \| 333 \| 0.0007 \| - \| - \|
	\| 3.0364 \| 334 \| 0.0038 \| - \| - \|
	\| 3.0455 \| 335 \| 0.0006 \| - \| - \|
	\| 3.0545 \| 336 \| 0.0012 \| - \| - \|
	\| 3.0636 \| 337 \| 0.0873 \| - \| - \|
	\| 3.0727 \| 338 \| 0.0022 \| - \| - \|
	\| 3.0818 \| 339 \| 0.0004 \| - \| - \|
	\| 3.0909 \| 340 \| 0.001 \| - \| - \|
	\| 3.1 \| 341 \| 0.0002 \| - \| - \|
	\| 3.1091 \| 342 \| 0.0069 \| - \| - \|
	\| 3.1182 \| 343 \| 0.0009 \| - \| - \|
	\| 3.1273 \| 344 \| 0.0101 \| - \| - \|
	\| 3.1364 \| 345 \| 0.0022 \| - \| - \|
	\| 3.1455 \| 346 \| 0.009 \| - \| - \|
	\| 3.1545 \| 347 \| 0.0018 \| - \| - \|
	\| 3.1636 \| 348 \| 0.0018 \| - \| - \|
	\| 3.1727 \| 349 \| 0.0045 \| - \| - \|
	\| 3.1818 \| 350 \| 0.029 \| - \| - \|
	\| 3.1909 \| 351 \| 0.0036 \| - \| - \|
	\| 3.2 \| 352 \| 0.0015 \| - \| - \|
	\| 3.2091 \| 353 \| 0.0021 \| - \| - \|
	\| 3.2182 \| 354 \| 0.0103 \| - \| - \|
	\| 3.2273 \| 355 \| 0.0005 \| - \| - \|
	\| 3.2364 \| 356 \| 0.0133 \| - \| - \|
	\| 3.2455 \| 357 \| 0.0015 \| - \| - \|
	\| 3.2545 \| 358 \| 0.001 \| - \| - \|
	\| 3.2636 \| 359 \| 0.0024 \| - \| - \|
	\| 3.2727 \| 360 \| 0.0052 \| - \| - \|
	\| 3.2818 \| 361 \| 0.0032 \| - \| - \|
	\| 3.2909 \| 362 \| 0.0024 \| - \| - \|
	\| 3.3 \| 363 \| 0.0008 \| - \| - \|
	\| 3.3091 \| 364 \| 0.0035 \| - \| - \|
	\| 3.3182 \| 365 \| 0.0012 \| - \| - \|
	\| 3.3273 \| 366 \| 0.0049 \| - \| - \|
	\| 3.3364 \| 367 \| 0.0452 \| - \| - \|
	\| 3.3455 \| 368 \| 0.0017 \| - \| - \|
	\| 3.3545 \| 369 \| 0.0112 \| - \| - \|
	\| 3.3636 \| 370 \| 0.0011 \| - \| - \|
	\| 3.3727 \| 371 \| 0.0016 \| - \| - \|
	\| 3.3818 \| 372 \| 0.0015 \| - \| - \|
	\| 3.3909 \| 373 \| 0.004 \| - \| - \|
	\| 3.4 \| 374 \| 0.0074 \| - \| - \|
	\| 3.4091 \| 375 \| 0.0005 \| - \| - \|
	\| 3.4182 \| 376 \| 0.0007 \| - \| - \|
	\| 3.4273 \| 377 \| 0.0014 \| - \| - \|
	\| 3.4364 \| 378 \| 0.0097 \| - \| - \|
	\| 3.4455 \| 379 \| 0.0026 \| - \| - \|
	\| 3.4545 \| 380 \| 0.0022 \| - \| - \|
	\| 3.4636 \| 381 \| 0.001 \| - \| - \|
	\| 3.4727 \| 382 \| 0.0004 \| - \| - \|
	\| 3.4818 \| 383 \| 0.004 \| - \| - \|
	\| 3.4909 \| 384 \| 0.0017 \| - \| - \|
	\| 3.5 \| 385 \| 0.0014 \| - \| - \|
	\| 3.5091 \| 386 \| 0.001 \| - \| - \|
	\| 3.5182 \| 387 \| 0.0047 \| - \| - \|
	\| 3.5273 \| 388 \| 0.0061 \| - \| - \|
	\| 3.5364 \| 389 \| 0.0017 \| - \| - \|
	\| 3.5455 \| 390 \| 0.0024 \| - \| - \|
	\| 3.5545 \| 391 \| 0.0021 \| - \| - \|
	\| 3.5636 \| 392 \| 0.0007 \| - \| - \|
	\| 3.5727 \| 393 \| 0.0009 \| - \| - \|
	\| 3.5818 \| 394 \| 0.0006 \| - \| - \|
	\| 3.5909 \| 395 \| 0.0038 \| - \| - \|
	\| 3.6 \| 396 \| 0.0006 \| - \| - \|
	\| 3.6091 \| 397 \| 0.0011 \| - \| - \|
	\| 3.6182 \| 398 \| 0.001 \| - \| - \|
	\| 3.6273 \| 399 \| 0.0014 \| - \| - \|
	\| 3.6364 \| 400 \| 0.0007 \| - \| - \|
	\| 3.6455 \| 401 \| 0.0052 \| - \| - \|
	\| 3.6545 \| 402 \| 0.0008 \| - \| - \|
	\| 3.6636 \| 403 \| 0.0009 \| - \| - \|
	\| 3.6727 \| 404 \| 0.0017 \| - \| - \|
	\| 3.6818 \| 405 \| 0.0028 \| - \| - \|
	\| 3.6909 \| 406 \| 0.0044 \| - \| - \|
	\| 3.7 \| 407 \| 0.0009 \| - \| - \|
	\| 3.7091 \| 408 \| 0.0134 \| - \| - \|
	\| 3.7182 \| 409 \| 0.001 \| - \| - \|
	\| 3.7273 \| 410 \| 0.0044 \| - \| - \|
	\| 3.7364 \| 411 \| 0.0138 \| - \| - \|
	\| 3.7455 \| 412 \| 0.0032 \| - \| - \|
	\| 3.7545 \| 413 \| 0.0004 \| - \| - \|
	\| 3.7636 \| 414 \| 0.0065 \| - \| - \|
	\| 3.7727 \| 415 \| 0.0007 \| - \| - \|
	\| 3.7818 \| 416 \| 0.0008 \| - \| - \|
	\| 3.7909 \| 417 \| 0.0007 \| - \| - \|
	\| 3.8 \| 418 \| 0.0018 \| - \| - \|
	\| 3.8091 \| 419 \| 0.001 \| - \| - \|
	\| 3.8182 \| 420 \| 0.0305 \| - \| - \|
	\| 3.8273 \| 421 \| 0.001 \| - \| - \|
	\| 3.8364 \| 422 \| 0.0011 \| - \| - \|
	\| 3.8455 \| 423 \| 0.0004 \| - \| - \|
	\| 3.8545 \| 424 \| 0.003 \| - \| - \|
	\| 3.8636 \| 425 \| 0.002 \| - \| - \|
	\| 3.8727 \| 426 \| 0.0018 \| - \| - \|
	\| 3.8818 \| 427 \| 0.0968 \| - \| - \|
	\| 3.8909 \| 428 \| 0.002 \| - \| - \|
	\| 3.9 \| 429 \| 0.002 \| - \| - \|
	\| 3.9091 \| 430 \| 0.0156 \| - \| - \|
	\| 3.9182 \| 431 \| 0.0059 \| - \| - \|
	\| 3.9273 \| 432 \| 0.001 \| - \| - \|
	\| 3.9364 \| 433 \| 0.0153 \| - \| - \|
	\| 3.9455 \| 434 \| 0.0013 \| - \| - \|
	\| 3.9545 \| 435 \| 0.0003 \| - \| - \|
	\| 3.9636 \| 436 \| 0.001 \| - \| - \|
	\| 3.9727 \| 437 \| 0.0005 \| - \| - \|
	\| 3.9818 \| 438 \| 0.0012 \| - \| - \|
	\| 3.9909 \| 439 \| 0.0109 \| - \| - \|
	\| 4.0 \| 440 \| 0.1597 \| 0.0211 \| 0.7235 \|
	\| 4.0091 \| 441 \| 0.0027 \| - \| - \|
	\| 4.0182 \| 442 \| 0.0007 \| - \| - \|
	\| 4.0273 \| 443 \| 0.0089 \| - \| - \|
	\| 4.0364 \| 444 \| 0.0007 \| - \| - \|
	\| 4.0455 \| 445 \| 0.005 \| - \| - \|
	\| 4.0545 \| 446 \| 0.0019 \| - \| - \|
	\| 4.0636 \| 447 \| 0.0007 \| - \| - \|
	\| 4.0727 \| 448 \| 0.0008 \| - \| - \|
	\| 4.0818 \| 449 \| 0.002 \| - \| - \|
	\| 4.0909 \| 450 \| 0.043 \| - \| - \|
	\| 4.1 \| 451 \| 0.0273 \| - \| - \|
	\| 4.1091 \| 452 \| 0.0009 \| - \| - \|
	\| 4.1182 \| 453 \| 0.0011 \| - \| - \|
	\| 4.1273 \| 454 \| 0.0007 \| - \| - \|
	\| 4.1364 \| 455 \| 0.0062 \| - \| - \|
	\| 4.1455 \| 456 \| 0.0004 \| - \| - \|
	\| 4.1545 \| 457 \| 0.0008 \| - \| - \|
	\| 4.1636 \| 458 \| 0.0128 \| - \| - \|
	\| 4.1727 \| 459 \| 0.0012 \| - \| - \|
	\| 4.1818 \| 460 \| 0.0013 \| - \| - \|
	\| 4.1909 \| 461 \| 0.0009 \| - \| - \|
	\| 4.2 \| 462 \| 0.0011 \| - \| - \|
	\| 4.2091 \| 463 \| 0.0336 \| - \| - \|
	\| 4.2182 \| 464 \| 0.0018 \| - \| - \|
	\| 4.2273 \| 465 \| 0.0009 \| - \| - \|
	\| 4.2364 \| 466 \| 0.0049 \| - \| - \|
	\| 4.2455 \| 467 \| 0.0012 \| - \| - \|
	\| 4.2545 \| 468 \| 0.001 \| - \| - \|
	\| 4.2636 \| 469 \| 0.0024 \| - \| - \|
	\| 4.2727 \| 470 \| 0.0063 \| - \| - \|
	\| 4.2818 \| 471 \| 0.0008 \| - \| - \|
	\| 4.2909 \| 472 \| 0.0793 \| - \| - \|
	\| 4.3 \| 473 \| 0.0016 \| - \| - \|
	\| 4.3091 \| 474 \| 0.0016 \| - \| - \|
	\| 4.3182 \| 475 \| 0.0043 \| - \| - \|
	\| 4.3273 \| 476 \| 0.036 \| - \| - \|
	\| 4.3364 \| 477 \| 0.002 \| - \| - \|
	\| 4.3455 \| 478 \| 0.0019 \| - \| - \|
	\| 4.3545 \| 479 \| 0.0012 \| - \| - \|
	\| 4.3636 \| 480 \| 0.0059 \| - \| - \|
	\| 4.3727 \| 481 \| 0.0017 \| - \| - \|
	\| 4.3818 \| 482 \| 0.0004 \| - \| - \|
	\| 4.3909 \| 483 \| 0.0014 \| - \| - \|
	\| 4.4 \| 484 \| 0.0143 \| - \| - \|
	\| 4.4091 \| 485 \| 0.0014 \| - \| - \|
	\| 4.4182 \| 486 \| 0.0009 \| - \| - \|
	\| 4.4273 \| 487 \| 0.0027 \| - \| - \|
	\| 4.4364 \| 488 \| 0.0017 \| - \| - \|
	\| 4.4455 \| 489 \| 0.0007 \| - \| - \|
	\| 4.4545 \| 490 \| 0.0008 \| - \| - \|
	\| 4.4636 \| 491 \| 0.0008 \| - \| - \|
	\| 4.4727 \| 492 \| 0.0014 \| - \| - \|
	\| 4.4818 \| 493 \| 0.0011 \| - \| - \|
	\| 4.4909 \| 494 \| 0.0013 \| - \| - \|
	\| 4.5 \| 495 \| 0.0016 \| - \| - \|
	\| 4.5091 \| 496 \| 0.001 \| - \| - \|
	\| 4.5182 \| 497 \| 0.0008 \| - \| - \|
	\| 4.5273 \| 498 \| 0.001 \| - \| - \|
	\| 4.5364 \| 499 \| 0.0019 \| - \| - \|
	\| 4.5455 \| 500 \| 0.0008 \| - \| - \|

	</details>

	### Framework Versions
	- Python: 3.12.9
	- Sentence Transformers: 4.1.0
	- Transformers: 4.52.3
	- PyTorch: 2.6.0+cu124
	- Accelerate: 1.7.0
	- Datasets: 3.6.0
	- 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",
	}
	```

	#### 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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