Add new SentenceTransformer model.

0992837 verified 4 months ago

150 kB

	---
	base_model: SQAI/bge-embedding-model
	datasets: []
	language:
	- en
	library_name: sentence-transformers
	license: apache-2.0
	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
	pipeline_tag: sentence-similarity
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:1865
	- loss:MatryoshkaLoss
	- loss:MultipleNegativesRankingLoss
	widget:
	- source_sentence: threshold.highLuxThreshold
	sentences:
	- '"Can you provide the timestamp of the last update to the threshold settings,
	and detail any faults in the lux module related to light level sensing and control
	for the streetlight on this specific street name? I also want to know the longitude
	of the streetlight. And also, can you tell me what type of dimming schedule is
	applied to the streetlight, the type of port used for its dimming controls, and
	the total energy it has consumed, recorded in kilowatt-hours. Lastly, could you
	also provide the timestamp of the recorded streetlighting error, and confirm the
	status of the relay responsible for turning this streetlight on and off, as I
	am suspecting it might be sticking?"'
	- '"Can you provide me with the unique streetlight identifier, upper lux level for
	managing light intensity, a brief description, and the delta or height of the
	grid area occupied by a group of streetlights? Also, can you note the AC voltage
	supply for these streetlights, any issues with communication related to their
	lux sensors, and the count of how many times each streetlight has been switched
	on? Please ensure that the data is constrained to just those that can be determined
	with the unique streetlight identifier I provided."'
	- '"What was the last recorded data or action timestamp of the streetlight located
	at the specific longitude, and in which time zone is it situated? Could you also
	provide information on its default dimming level and the maximum power usage threshold
	above which indicates potential faults? Are there any identified faults in the
	lux module impacting light level sensing and control? Additionally, what are the
	minimum longitude and delta or height for the grid area occupied by this group
	of streetlights and could you specify the network time received from the central
	control for synchronization purposes?"'
	- source_sentence: asset.geoZone
	sentences:
	- '"Could you check the status of the streetlight with the unique identifier, located
	on the named street, specifically looking at any records of complete loss of power
	which could indicate supply issues or damage? Also, could you provide details
	on the instances where the voltage under load is lower than expected, as well
	as instances of lower than expected power consumption, which could signal potential
	electrical or hardware issues? I''m also interested in understanding if there
	are any faults in our link control mechanism managing multiple streetlights. Additionally,
	could you tell me the current drawn by this specific streetlight when it was lower
	than expected and the current dimming level of the streetlight in operation? Lastly,
	could you specify the maximum safe voltage under load conditions for this light
	and verify whether its broadcast subscription used for receiving control signals
	is doing fine?"'
	- '"Can you provide me with the details regarding a specific streetlight on Main
	Street, particularly the minimum current level below which it''s considered abnormal,
	its power factor indicating efficient power usage, total operational hours logged,
	any incidences where power consumption was higher than expected possibly due to
	potential faults, its geoZone, X-coordinate in the grid layout, minimum operational
	voltage under load conditions, minimum load current that indicates suboptimal
	performance, and the timestamp of the last update made to the threshold settings?"'
	- '"What is the width and height of the grid area occupied by the group of streetlights,
	type of port used for dimming controls, power consumption levels, and what is
	the safety of the current exceeded on the streetlight? Besides, could you explain
	the high power factor indicating potential overloads or capacitive imbalances?"'
	- source_sentence: errors.deviceId
	sentences:
	- '"Can you show me a report of all the streetlights with a unique identifier, which
	have an internal temperature indicating abnormal operating conditions such as
	voltage supplied being below the safe level, and operating temperature below expected
	limit possibly due to environmental conditions? Can this report also include instances
	of faults in link control mechanism managing multiple streetlights and cases of
	open circuit in the relay preventing normal operation?"'
	- '"Could you provide information about the streetlight on ''specific street name'',
	specifically concerning its current drawn which appears to be lower than expected,
	potential issues in the link control mechanism that manages multiple streetlights,
	whether its operating temperature exceeds safe limits thus risking damage, and
	if its power output is lower than expected? Also, could you let me know at what
	interval this streetlight sends data reports and inform about any other issues
	detected, particularly when the current is below the expected range?"'
	- '"What is the minimum power usage level below which it is considered abnormal
	for our ''Main Street Lamps'' group of streetlights, which are described as a
	series of LED lamps installed along the main town stretch, and what could be the
	reasons if the power consumption is lower than expected, possibly due to hardware
	issues? Also, could you give me the description on what means when intermittent
	flashing of the streetlight occurs, indicating instability and tell me about the
	strength of the wireless signal received by the streetlight''s communication module.
	Could you confirm what control mode switch identifier we should use for changing
	streetlight settings and the highest power factor that is considered optimal for
	streetlight efficiency? Additionally, we discovered issues with group management
	of streetlights via our central control system, and we would like to know the
	time taken for the streetlight to activate or light up from the command."'
	- source_sentence: threshold.lowLoadVoltage
	sentences:
	- '"Could you please show me the latest data recorded or action performed by the
	streetlight, specifically highlighting the control mode switch identifier used
	for changing its settings, the type of DALI dimming protocol it uses, and the
	type of port used for its dimming controls? Furthermore, has there been any intermittent
	flashing indicating instability? Also, could you provide data on its minimum operational
	voltage under load conditions, and let me know if its power consumption is lower
	than expected due to potential hardware issues?"

	'
	- '"Can the operator managing the streetlight provide the timestamp of the latest
	data recorded or action performed by the streetlight, details on the minimum operational
	voltage under load conditions, the current issues with the driver that powers
	and controls the streetlight, why the power output is lower than expected for
	the streetlight, and what is the maximum latitude of the geographic area covered
	by this group of streetlights?"'
	- '"Can you provide a report that shows all the streetlights in a grid layout with
	Y-coordinate information, indicating whether their control mode setting is on
	automated or manual, their minimum current level, and instances of communication
	issues between the streetlight''s driver and the control system, as well as instances
	when the operating temperature fell below expected limits, possibly due to environmental
	conditions?"'
	- source_sentence: errors.controllerFault.lowLoadCurrent
	sentences:
	- '"Can you provide me with the current status of the streetlight on ''street name'',
	specifically in relation to its voltage under load, whether it''s lower than expected
	and how that might be indicating potential electrical issues? Could you also give
	me insight into the current drawn by the streetlight, whether or not the relay
	is currently on or off, and if there are any faults in the lux module that may
	affect light level sensing and control? Moreover, could you tell me the type of
	dimming schedule applied, the ambient light level detected in lux, the total energy
	consumed so far recorded in kilowatt-hours, and the lower voltage threshold for
	this streetlight''s efficient operation?"'
	- '"Can you provide a detailed report for the streetlight on [Name of the street
	for the streetlight in error]? The report should include the timestamp of the
	last recorded error, synchronization time received from the central control, the
	dimming schedule type we''re currently using, and both minimum operational and
	maximum safe voltage under load conditions. Also, indicate the time of the last
	action was recorded and if there are any reported faults in the metering components
	affecting data reporting. Can you also specify the port type used for dimming
	controls and whether the power consumption has been unusually low due to potential
	hardware issues?"'
	- '"Can you show me the current status of the relay in the streetlights located
	at the X-coordinate grid, highlighting any faults in the lux module that might
	be affecting light level sensing and control? Also, could you provide information
	on the current dimming level of these streetlights in operation, the type of dimming
	schedule applied, and whether the voltage is within the upper limit considered
	safe and efficient for their operation?"'
	model-index:
	- name: BGE base Financial Matryoshka
	results:
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: dim 768
	type: dim_768
	metrics:
	- type: cosine_accuracy@1
	value: 0.0
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.0
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.0
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.014423076923076924
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.0
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.0
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.0
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.0014423076923076926
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.0
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.0
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.0
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.014423076923076924
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.004284253930989665
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.001549145299145299
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.005857063109582476
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: dim 512
	type: dim_512
	metrics:
	- type: cosine_accuracy@1
	value: 0.0
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.0
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.0
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.014423076923076924
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.0
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.0
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.0
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.0014423076923076926
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.0
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.0
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.0
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.014423076923076924
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.004284253930989665
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.001549145299145299
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.005857063109582476
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: dim 256
	type: dim_256
	metrics:
	- type: cosine_accuracy@1
	value: 0.0
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.0
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.0
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.014423076923076924
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.0
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.0
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.0
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.0014423076923076926
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.0
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.0
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.0
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.014423076923076924
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.0043536523979211435
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.0016159188034188035
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.005708010488423065
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: dim 128
	type: dim_128
	metrics:
	- type: cosine_accuracy@1
	value: 0.0
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.0
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.0
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.009615384615384616
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.0
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.0
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.0
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.0009615384615384616
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.0
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.0
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.0
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.009615384615384616
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.0030498236971024735
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.001221001221001221
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.005185692544152747
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: dim 64
	type: dim_64
	metrics:
	- type: cosine_accuracy@1
	value: 0.0
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.0
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.0
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.019230769230769232
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.0
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.0
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.0
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.0019230769230769232
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.0
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.0
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.0
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.019230769230769232
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.005956216500485246
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.0023027319902319903
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.0051874402718147935
	name: Cosine Map@100
	---

	# BGE base Financial Matryoshka

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [SQAI/bge-embedding-model](https://huggingface.co/SQAI/bge-embedding-model). It maps sentences & paragraphs to a 384-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: [SQAI/bge-embedding-model](https://huggingface.co/SQAI/bge-embedding-model) <!-- at revision 9a9bc3f795ddfc56610a621b37aa077ae0653fa4 -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 384 tokens
	- 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': 512, 'do_lower_case': True}) with Transformer model: BertModel
	(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
	(2): Normalize()
	)
	```

	## Usage

	### Direct Usage (Sentence Transformers)

	First install the Sentence Transformers library:

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

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

	# Download from the 🤗 Hub
	model = SentenceTransformer("SQAI/bge-embedding-model2")
	# Run inference
	sentences = [
	'errors.controllerFault.lowLoadCurrent',
	'"Can you provide me with the current status of the streetlight on \'street name\', specifically in relation to its voltage under load, whether it\'s lower than expected and how that might be indicating potential electrical issues? Could you also give me insight into the current drawn by the streetlight, whether or not the relay is currently on or off, and if there are any faults in the lux module that may affect light level sensing and control? Moreover, could you tell me the type of dimming schedule applied, the ambient light level detected in lux, the total energy consumed so far recorded in kilowatt-hours, and the lower voltage threshold for this streetlight\'s efficient operation?"',
	'"Can you show me the current status of the relay in the streetlights located at the X-coordinate grid, highlighting any faults in the lux module that might be affecting light level sensing and control? Also, could you provide information on the current dimming level of these streetlights in operation, the type of dimming schedule applied, and whether the voltage is within the upper limit considered safe and efficient for their operation?"',
	]
	embeddings = model.encode(sentences)
	print(embeddings.shape)
	# [3, 384]

	# 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>
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	### Out-of-Scope Use

	List how the model may foreseeably be misused and address what users ought not to do with the model.
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	## Evaluation

	### Metrics

	#### Information Retrieval
	* Dataset: `dim_768`
	* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| cosine_accuracy@1 \| 0.0 \|
	\| cosine_accuracy@3 \| 0.0 \|
	\| cosine_accuracy@5 \| 0.0 \|
	\| cosine_accuracy@10 \| 0.0144 \|
	\| cosine_precision@1 \| 0.0 \|
	\| cosine_precision@3 \| 0.0 \|
	\| cosine_precision@5 \| 0.0 \|
	\| cosine_precision@10 \| 0.0014 \|
	\| cosine_recall@1 \| 0.0 \|
	\| cosine_recall@3 \| 0.0 \|
	\| cosine_recall@5 \| 0.0 \|
	\| cosine_recall@10 \| 0.0144 \|
	\| cosine_ndcg@10 \| 0.0043 \|
	\| cosine_mrr@10 \| 0.0015 \|
	\| cosine_map@100 \| 0.0059 \|

	#### Information Retrieval
	* Dataset: `dim_512`
	* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| cosine_accuracy@1 \| 0.0 \|
	\| cosine_accuracy@3 \| 0.0 \|
	\| cosine_accuracy@5 \| 0.0 \|
	\| cosine_accuracy@10 \| 0.0144 \|
	\| cosine_precision@1 \| 0.0 \|
	\| cosine_precision@3 \| 0.0 \|
	\| cosine_precision@5 \| 0.0 \|
	\| cosine_precision@10 \| 0.0014 \|
	\| cosine_recall@1 \| 0.0 \|
	\| cosine_recall@3 \| 0.0 \|
	\| cosine_recall@5 \| 0.0 \|
	\| cosine_recall@10 \| 0.0144 \|
	\| cosine_ndcg@10 \| 0.0043 \|
	\| cosine_mrr@10 \| 0.0015 \|
	\| cosine_map@100 \| 0.0059 \|

	#### Information Retrieval
	* Dataset: `dim_256`
	* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| cosine_accuracy@1 \| 0.0 \|
	\| cosine_accuracy@3 \| 0.0 \|
	\| cosine_accuracy@5 \| 0.0 \|
	\| cosine_accuracy@10 \| 0.0144 \|
	\| cosine_precision@1 \| 0.0 \|
	\| cosine_precision@3 \| 0.0 \|
	\| cosine_precision@5 \| 0.0 \|
	\| cosine_precision@10 \| 0.0014 \|
	\| cosine_recall@1 \| 0.0 \|
	\| cosine_recall@3 \| 0.0 \|
	\| cosine_recall@5 \| 0.0 \|
	\| cosine_recall@10 \| 0.0144 \|
	\| cosine_ndcg@10 \| 0.0044 \|
	\| cosine_mrr@10 \| 0.0016 \|
	\| cosine_map@100 \| 0.0057 \|

	#### Information Retrieval
	* Dataset: `dim_128`
	* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| cosine_accuracy@1 \| 0.0 \|
	\| cosine_accuracy@3 \| 0.0 \|
	\| cosine_accuracy@5 \| 0.0 \|
	\| cosine_accuracy@10 \| 0.0096 \|
	\| cosine_precision@1 \| 0.0 \|
	\| cosine_precision@3 \| 0.0 \|
	\| cosine_precision@5 \| 0.0 \|
	\| cosine_precision@10 \| 0.001 \|
	\| cosine_recall@1 \| 0.0 \|
	\| cosine_recall@3 \| 0.0 \|
	\| cosine_recall@5 \| 0.0 \|
	\| cosine_recall@10 \| 0.0096 \|
	\| cosine_ndcg@10 \| 0.003 \|
	\| cosine_mrr@10 \| 0.0012 \|
	\| cosine_map@100 \| 0.0052 \|

	#### Information Retrieval
	* Dataset: `dim_64`
	* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| cosine_accuracy@1 \| 0.0 \|
	\| cosine_accuracy@3 \| 0.0 \|
	\| cosine_accuracy@5 \| 0.0 \|
	\| cosine_accuracy@10 \| 0.0192 \|
	\| cosine_precision@1 \| 0.0 \|
	\| cosine_precision@3 \| 0.0 \|
	\| cosine_precision@5 \| 0.0 \|
	\| cosine_precision@10 \| 0.0019 \|
	\| cosine_recall@1 \| 0.0 \|
	\| cosine_recall@3 \| 0.0 \|
	\| cosine_recall@5 \| 0.0 \|
	\| cosine_recall@10 \| 0.0192 \|
	\| cosine_ndcg@10 \| 0.006 \|
	\| cosine_mrr@10 \| 0.0023 \|
	\| cosine_map@100 \| 0.0052 \|

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	## 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.
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	<!--
	### Recommendations

	What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.
	-->

	## Training Details

	### Training Dataset

	#### Unnamed Dataset


	* Size: 1,865 training samples
	* Columns: <code>positive</code> and <code>anchor</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| positive \| anchor \|
	\|:--------\|:---------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------\|
	\| type \| string \| string \|
	\| details \| <ul><li>min: 5 tokens</li><li>mean: 7.68 tokens</li><li>max: 14 tokens</li></ul> \| <ul><li>min: 17 tokens</li><li>mean: 89.79 tokens</li><li>max: 187 tokens</li></ul> \|
	* Samples:
	\| positive \| anchor \|
	\|:----------------------------------------------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>threshold.lowLoadVoltage</code> \| <code>"What is the maximum current level above which it is considered unsafe for a specific streetlight in my area, what is the minimum longitude of the geographic area this streetlight covers, is this streetlight's control mode automated or manually controlled, also, can you provide the delta or width of the grid area occupied by this group of streetlights, what is the level of AC voltage supply to this streetlight, what's the lower voltage threshold below which this streetlight may not operate efficiently, how many times has this streetlight been switched on, what is the minimum operational voltage under load conditions, and finally, what is the latitude of this streetlight?"</code> \|
	\| <code>asset.id</code> \| <code>"Could you please tell me the scheduled dimming settings for the string stored streetlights, troubleshoot why these streetlights remain on during daylight hours, and confirm if this could be due to sensor faults? Also, I'd like to know the identifier for the parent group to which this group of streetlights belongs, and the IMEI number of the streetlight device."</code> \|
	\| <code>errors.controllerFault.highPower</code> \| <code>"Can you provide an analysis of the efficiency of power usage by examining the power factor of the streetlights, especially in areas of the grid with high Y-coordinates, highlight instances where power consumption is significantly higher than expected which may indicate faults, identify situations where voltage under load is above safe levels, and assess if there are any problems with our central control system's ability to manage streetlight groups?"</code> \|
	* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
	```json
	{
	"loss": "MultipleNegativesRankingLoss",
	"matryoshka_dims": [
	384,
	256,
	128,
	64
	],
	"matryoshka_weights": [
	1,
	1,
	1,
	1
	],
	"n_dims_per_step": -1
	}
	```

	### Evaluation Dataset

	#### Unnamed Dataset


	* Size: 208 evaluation samples
	* Columns: <code>positive</code> and <code>anchor</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| positive \| anchor \|
	\|:--------\|:---------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------\|
	\| type \| string \| string \|
	\| details \| <ul><li>min: 5 tokens</li><li>mean: 7.55 tokens</li><li>max: 14 tokens</li></ul> \| <ul><li>min: 19 tokens</li><li>mean: 90.69 tokens</li><li>max: 187 tokens</li></ul> \|
	* Samples:
	\| positive \| anchor \|
	\|:---------------------------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>log.controlModeSwitch</code> \| <code>"Can you provide the control mode switch identifier used for changing the default dimming level set for a specific group of streetlights, identified by their unique identifier, considering the time taken for the streetlight to activate or light up from the command, and possibly troubleshoot why the power consumption is lower than expected which could be due to hardware issues, quite possibly due to the relay responsible for turning the streetlight on and off sticking?"</code> \|
	\| <code>errors.controllerFault.luxModuleFault</code> \| <code>"Can you provide the timestamp of the last update to the threshold settings, and detail any faults in the lux module related to light level sensing and control for the streetlight on this specific street name? I also want to know the longitude of the streetlight. And also, can you tell me what type of dimming schedule is applied to the streetlight, the type of port used for its dimming controls, and the total energy it has consumed, recorded in kilowatt-hours. Lastly, could you also provide the timestamp of the recorded streetlighting error, and confirm the status of the relay responsible for turning this streetlight on and off, as I am suspecting it might be sticking?"</code> \|
	\| <code>threshold.lowLoadCurrent</code> \| <code>"What is the maximum safe voltage under load conditions for the city's streetlights, and do we possess the necessary rights to link these streetlights for synchronized control? Could you provide me with the timestamp of the latest data or action performed by our streetlights, and tell me the lower lux level threshold at which we would need to consider additional lighting? How often does each streetlight send a data report in normal operation, and what is the minimum load current level where we might start seeing suboptimal functioning? Have we been experiencing any problems with managing groups of streetlights via the central control system? Also, has there been any instances where the current under load was excessively high, indicating possible overloads, or situations where the operation temperature was belo normal limits due to environmental conditions? Lastly, have there been any noted communication issues between the streetlight's driver and the control system?"</code> \|
	* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
	```json
	{
	"loss": "MultipleNegativesRankingLoss",
	"matryoshka_dims": [
	384,
	256,
	128,
	64
	],
	"matryoshka_weights": [
	1,
	1,
	1,
	1
	],
	"n_dims_per_step": -1
	}
	```

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

	- `eval_strategy`: epoch
	- `per_device_train_batch_size`: 32
	- `per_device_eval_batch_size`: 16
	- `gradient_accumulation_steps`: 16
	- `learning_rate`: 2e-06
	- `weight_decay`: 0.03
	- `num_train_epochs`: 200
	- `lr_scheduler_type`: cosine
	- `warmup_ratio`: 0.2
	- `bf16`: True
	- `tf32`: True
	- `load_best_model_at_end`: True
	- `optim`: adamw_torch_fused
	- `batch_sampler`: no_duplicates

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

	- `overwrite_output_dir`: False
	- `do_predict`: False
	- `eval_strategy`: epoch
	- `prediction_loss_only`: True
	- `per_device_train_batch_size`: 32
	- `per_device_eval_batch_size`: 16
	- `per_gpu_train_batch_size`: None
	- `per_gpu_eval_batch_size`: None
	- `gradient_accumulation_steps`: 16
	- `eval_accumulation_steps`: None
	- `learning_rate`: 2e-06
	- `weight_decay`: 0.03
	- `adam_beta1`: 0.9
	- `adam_beta2`: 0.999
	- `adam_epsilon`: 1e-08
	- `max_grad_norm`: 1.0
	- `num_train_epochs`: 200
	- `max_steps`: -1
	- `lr_scheduler_type`: cosine
	- `lr_scheduler_kwargs`: {}
	- `warmup_ratio`: 0.2
	- `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`: 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
	- `batch_sampler`: no_duplicates
	- `multi_dataset_batch_sampler`: proportional

	</details>

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

	\| Epoch \| Step \| Training Loss \| loss \| dim_128_cosine_map@100 \| dim_256_cosine_map@100 \| dim_512_cosine_map@100 \| dim_64_cosine_map@100 \| dim_768_cosine_map@100 \|
	\|:----------:\|:------:\|:-------------:\|:----------:\|:----------------------:\|:----------------------:\|:----------------------:\|:---------------------:\|:----------------------:\|
	\| 0.2712 \| 1 \| 13.2713 \| - \| - \| - \| - \| - \| - \|
	\| 0.5424 \| 2 \| 13.2895 \| - \| - \| - \| - \| - \| - \|
	\| 0.8136 \| 3 \| 9.9139 \| - \| - \| - \| - \| - \| - \|
	\| 1.0847 \| 4 \| 5.6117 \| - \| - \| - \| - \| - \| - \|
	\| 1.3559 \| 5 \| 4.7571 \| - \| - \| - \| - \| - \| - \|
	\| 1.6271 \| 6 \| 5.5215 \| - \| - \| - \| - \| - \| - \|
	\| 1.8983 \| 7 \| 5.7945 \| - \| - \| - \| - \| - \| - \|
	\| 2.1695 \| 8 \| 5.7064 \| - \| - \| - \| - \| - \| - \|
	\| 2.4407 \| 9 \| 5.6794 \| - \| - \| - \| - \| - \| - \|
	\| 2.7119 \| 10 \| 5.7384 \| - \| - \| - \| - \| - \| - \|
	\| 2.9831 \| 11 \| 5.6081 \| - \| - \| - \| - \| - \| - \|
	\| 3.2542 \| 12 \| 5.5278 \| - \| - \| - \| - \| - \| - \|
	\| 3.5254 \| 13 \| 5.149 \| - \| - \| - \| - \| - \| - \|
	\| 3.7966 \| 14 \| 5.5904 \| 5.6043 \| 0.0081 \| 0.0072 \| 0.0079 \| 0.0055 \| 0.0079 \|
	\| 1.0169 \| 15 \| 3.9458 \| - \| - \| - \| - \| - \| - \|
	\| 1.2881 \| 16 \| 13.3653 \| - \| - \| - \| - \| - \| - \|
	\| 1.5593 \| 17 \| 13.4413 \| - \| - \| - \| - \| - \| - \|
	\| 1.8305 \| 18 \| 9.4188 \| - \| - \| - \| - \| - \| - \|
	\| 2.1017 \| 19 \| 5.717 \| - \| - \| - \| - \| - \| - \|
	\| 2.3729 \| 20 \| 5.2455 \| - \| - \| - \| - \| - \| - \|
	\| 2.6441 \| 21 \| 5.2117 \| - \| - \| - \| - \| - \| - \|
	\| 2.9153 \| 22 \| 5.5217 \| - \| - \| - \| - \| - \| - \|
	\| 3.1864 \| 23 \| 5.6725 \| - \| - \| - \| - \| - \| - \|
	\| 3.4576 \| 24 \| 5.786 \| - \| - \| - \| - \| - \| - \|
	\| 3.7288 \| 25 \| 5.6507 \| - \| - \| - \| - \| - \| - \|
	\| 4.0 \| 26 \| 5.7215 \| - \| - \| - \| - \| - \| - \|
	\| 4.2712 \| 27 \| 5.3999 \| - \| - \| - \| - \| - \| - \|
	\| 4.5424 \| 28 \| 5.4275 \| - \| - \| - \| - \| - \| - \|
	\| 4.8136 \| 29 \| 5.7143 \| 5.5718 \| 0.0082 \| 0.0071 \| 0.0077 \| 0.0052 \| 0.0077 \|
	\| 2.0339 \| 30 \| 4.478 \| - \| - \| - \| - \| - \| - \|
	\| 2.3051 \| 31 \| 13.1821 \| - \| - \| - \| - \| - \| - \|
	\| 2.5763 \| 32 \| 13.2473 \| - \| - \| - \| - \| - \| - \|
	\| 2.8475 \| 33 \| 8.8654 \| - \| - \| - \| - \| - \| - \|
	\| 3.1186 \| 34 \| 5.3181 \| - \| - \| - \| - \| - \| - \|
	\| 3.3898 \| 35 \| 5.2091 \| - \| - \| - \| - \| - \| - \|
	\| 3.6610 \| 36 \| 5.6027 \| - \| - \| - \| - \| - \| - \|
	\| 3.9322 \| 37 \| 5.6839 \| - \| - \| - \| - \| - \| - \|
	\| 4.2034 \| 38 \| 5.5955 \| - \| - \| - \| - \| - \| - \|
	\| 4.4746 \| 39 \| 5.5786 \| - \| - \| - \| - \| - \| - \|
	\| 4.7458 \| 40 \| 5.4509 \| - \| - \| - \| - \| - \| - \|
	\| 5.0169 \| 41 \| 5.3361 \| - \| - \| - \| - \| - \| - \|
	\| 5.2881 \| 42 \| 5.1608 \| - \| - \| - \| - \| - \| - \|
	\| 5.5593 \| 43 \| 5.4896 \| - \| - \| - \| - \| - \| - \|
	\| 5.8305 \| 44 \| 5.6466 \| 5.5241 \| 0.0062 \| 0.0070 \| 0.0076 \| 0.0095 \| 0.0076 \|
	\| 3.0508 \| 45 \| 4.5617 \| - \| - \| - \| - \| - \| - \|
	\| 3.3220 \| 46 \| 13.0665 \| - \| - \| - \| - \| - \| - \|
	\| 3.5932 \| 47 \| 13.1848 \| - \| - \| - \| - \| - \| - \|
	\| 3.8644 \| 48 \| 8.4053 \| - \| - \| - \| - \| - \| - \|
	\| 4.1356 \| 49 \| 5.2706 \| - \| - \| - \| - \| - \| - \|
	\| 4.4068 \| 50 \| 5.4269 \| - \| - \| - \| - \| - \| - \|
	\| 4.6780 \| 51 \| 5.3645 \| - \| - \| - \| - \| - \| - \|
	\| 4.9492 \| 52 \| 5.3587 \| - \| - \| - \| - \| - \| - \|
	\| 5.2203 \| 53 \| 5.1047 \| - \| - \| - \| - \| - \| - \|
	\| 5.4915 \| 54 \| 5.743 \| - \| - \| - \| - \| - \| - \|
	\| 5.7627 \| 55 \| 5.3754 \| - \| - \| - \| - \| - \| - \|
	\| 6.0339 \| 56 \| 5.3021 \| - \| - \| - \| - \| - \| - \|
	\| 6.3051 \| 57 \| 5.6983 \| - \| - \| - \| - \| - \| - \|
	\| 6.5763 \| 58 \| 5.302 \| - \| - \| - \| - \| - \| - \|
	\| 6.8475 \| 59 \| 5.4545 \| 5.4638 \| 0.0060 \| 0.0070 \| 0.0077 \| 0.0094 \| 0.0077 \|
	\| 4.0678 \| 60 \| 5.2213 \| - \| - \| - \| - \| - \| - \|
	\| 4.3390 \| 61 \| 12.9854 \| - \| - \| - \| - \| - \| - \|
	\| 4.6102 \| 62 \| 13.207 \| - \| - \| - \| - \| - \| - \|
	\| 4.8814 \| 63 \| 7.7493 \| - \| - \| - \| - \| - \| - \|
	\| 5.1525 \| 64 \| 5.3787 \| - \| - \| - \| - \| - \| - \|
	\| 5.4237 \| 65 \| 4.9406 \| - \| - \| - \| - \| - \| - \|
	\| 5.6949 \| 66 \| 5.3963 \| - \| - \| - \| - \| - \| - \|
	\| 5.9661 \| 67 \| 5.3429 \| - \| - \| - \| - \| - \| - \|
	\| 6.2373 \| 68 \| 5.292 \| - \| - \| - \| - \| - \| - \|
	\| 6.5085 \| 69 \| 5.6738 \| - \| - \| - \| - \| - \| - \|
	\| 6.7797 \| 70 \| 5.5927 \| - \| - \| - \| - \| - \| - \|
	\| 7.0508 \| 71 \| 5.5245 \| - \| - \| - \| - \| - \| - \|
	\| 7.3220 \| 72 \| 4.8334 \| - \| - \| - \| - \| - \| - \|
	\| 7.5932 \| 73 \| 5.2015 \| - \| - \| - \| - \| - \| - \|
	\| 7.8644 \| 74 \| 5.5393 \| 5.3954 \| 0.0060 \| 0.0071 \| 0.0078 \| 0.0094 \| 0.0078 \|
	\| 5.0847 \| 75 \| 5.6168 \| - \| - \| - \| - \| - \| - \|
	\| 5.3559 \| 76 \| 12.8678 \| - \| - \| - \| - \| - \| - \|
	\| 5.6271 \| 77 \| 13.2377 \| - \| - \| - \| - \| - \| - \|
	\| 5.8983 \| 78 \| 7.1882 \| - \| - \| - \| - \| - \| - \|
	\| 6.1695 \| 79 \| 5.1293 \| - \| - \| - \| - \| - \| - \|
	\| 6.4407 \| 80 \| 4.9413 \| - \| - \| - \| - \| - \| - \|
	\| 6.7119 \| 81 \| 5.1763 \| - \| - \| - \| - \| - \| - \|
	\| 6.9831 \| 82 \| 4.9512 \| - \| - \| - \| - \| - \| - \|
	\| 7.2542 \| 83 \| 5.2744 \| - \| - \| - \| - \| - \| - \|
	\| 7.5254 \| 84 \| 5.0573 \| - \| - \| - \| - \| - \| - \|
	\| 7.7966 \| 85 \| 5.1938 \| - \| - \| - \| - \| - \| - \|
	\| 8.0678 \| 86 \| 5.1514 \| - \| - \| - \| - \| - \| - \|
	\| 8.3390 \| 87 \| 4.9808 \| - \| - \| - \| - \| - \| - \|
	\| 8.6102 \| 88 \| 4.9983 \| - \| - \| - \| - \| - \| - \|
	\| 8.8814 \| 89 \| 5.3211 \| 5.3268 \| 0.0062 \| 0.0067 \| 0.0075 \| 0.0095 \| 0.0075 \|
	\| 6.1017 \| 90 \| 6.1513 \| - \| - \| - \| - \| - \| - \|
	\| 6.3729 \| 91 \| 12.7972 \| - \| - \| - \| - \| - \| - \|
	\| 6.6441 \| 92 \| 13.0051 \| - \| - \| - \| - \| - \| - \|
	\| 6.9153 \| 93 \| 6.551 \| - \| - \| - \| - \| - \| - \|
	\| 7.1864 \| 94 \| 4.6644 \| - \| - \| - \| - \| - \| - \|
	\| 7.4576 \| 95 \| 4.8619 \| - \| - \| - \| - \| - \| - \|
	\| 7.7288 \| 96 \| 5.0812 \| - \| - \| - \| - \| - \| - \|
	\| 8.0 \| 97 \| 4.758 \| - \| - \| - \| - \| - \| - \|
	\| 8.2712 \| 98 \| 5.1362 \| - \| - \| - \| - \| - \| - \|
	\| 8.5424 \| 99 \| 5.5405 \| - \| - \| - \| - \| - \| - \|
	\| 8.8136 \| 100 \| 5.228 \| - \| - \| - \| - \| - \| - \|
	\| 9.0847 \| 101 \| 5.1084 \| - \| - \| - \| - \| - \| - \|
	\| 9.3559 \| 102 \| 5.1574 \| - \| - \| - \| - \| - \| - \|
	\| 9.6271 \| 103 \| 5.3326 \| - \| - \| - \| - \| - \| - \|
	\| 9.8983 \| 104 \| 5.34 \| 5.2658 \| 0.0060 \| 0.0066 \| 0.0076 \| 0.0052 \| 0.0076 \|
	\| 7.1186 \| 105 \| 6.5789 \| - \| - \| - \| - \| - \| - \|
	\| 7.3898 \| 106 \| 12.7557 \| - \| - \| - \| - \| - \| - \|
	\| 7.6610 \| 107 \| 13.0203 \| - \| - \| - \| - \| - \| - \|
	\| 7.9322 \| 108 \| 5.7148 \| - \| - \| - \| - \| - \| - \|
	\| 8.2034 \| 109 \| 4.7945 \| - \| - \| - \| - \| - \| - \|
	\| 8.4746 \| 110 \| 4.5926 \| - \| - \| - \| - \| - \| - \|
	\| 8.7458 \| 111 \| 4.6727 \| - \| - \| - \| - \| - \| - \|
	\| 9.0169 \| 112 \| 5.0886 \| - \| - \| - \| - \| - \| - \|
	\| 9.2881 \| 113 \| 5.0562 \| - \| - \| - \| - \| - \| - \|
	\| 9.5593 \| 114 \| 5.2167 \| - \| - \| - \| - \| - \| - \|
	\| 9.8305 \| 115 \| 5.048 \| - \| - \| - \| - \| - \| - \|
	\| 10.1017 \| 116 \| 4.7765 \| - \| - \| - \| - \| - \| - \|
	\| 10.3729 \| 117 \| 4.9875 \| - \| - \| - \| - \| - \| - \|
	\| 10.6441 \| 118 \| 4.9501 \| - \| - \| - \| - \| - \| - \|
	\| 10.9153 \| 119 \| 4.756 \| 5.2124 \| 0.0057 \| 0.0065 \| 0.0075 \| 0.0054 \| 0.0075 \|
	\| 8.1356 \| 120 \| 6.9381 \| - \| - \| - \| - \| - \| - \|
	\| 8.4068 \| 121 \| 12.7916 \| - \| - \| - \| - \| - \| - \|
	\| 8.6780 \| 122 \| 12.8517 \| - \| - \| - \| - \| - \| - \|
	\| 8.9492 \| 123 \| 5.51 \| - \| - \| - \| - \| - \| - \|
	\| 9.2203 \| 124 \| 4.686 \| - \| - \| - \| - \| - \| - \|
	\| 9.4915 \| 125 \| 4.6611 \| - \| - \| - \| - \| - \| - \|
	\| 9.7627 \| 126 \| 5.2767 \| - \| - \| - \| - \| - \| - \|
	\| 10.0339 \| 127 \| 4.6103 \| - \| - \| - \| - \| - \| - \|
	\| 10.3051 \| 128 \| 4.957 \| - \| - \| - \| - \| - \| - \|
	\| 10.5763 \| 129 \| 5.0236 \| - \| - \| - \| - \| - \| - \|
	\| 10.8475 \| 130 \| 5.0894 \| - \| - \| - \| - \| - \| - \|
	\| 11.1186 \| 131 \| 4.7025 \| - \| - \| - \| - \| - \| - \|
	\| 11.3898 \| 132 \| 5.0765 \| - \| - \| - \| - \| - \| - \|
	\| 11.6610 \| 133 \| 4.6601 \| - \| - \| - \| - \| - \| - \|
	\| 11.9322 \| 134 \| 4.9064 \| 5.1731 \| 0.0056 \| 0.0060 \| 0.0070 \| 0.0054 \| 0.0070 \|
	\| 9.1525 \| 135 \| 7.5884 \| - \| - \| - \| - \| - \| - \|
	\| 9.4237 \| 136 \| 12.679 \| - \| - \| - \| - \| - \| - \|
	\| 9.6949 \| 137 \| 12.417 \| - \| - \| - \| - \| - \| - \|
	\| 9.9661 \| 138 \| 5.1632 \| - \| - \| - \| - \| - \| - \|
	\| 10.2373 \| 139 \| 4.9486 \| - \| - \| - \| - \| - \| - \|
	\| 10.5085 \| 140 \| 4.6341 \| - \| - \| - \| - \| - \| - \|
	\| 10.7797 \| 141 \| 4.9664 \| - \| - \| - \| - \| - \| - \|
	\| 11.0508 \| 142 \| 4.9567 \| - \| - \| - \| - \| - \| - \|
	\| 11.3220 \| 143 \| 4.7532 \| - \| - \| - \| - \| - \| - \|
	\| 11.5932 \| 144 \| 5.2556 \| - \| - \| - \| - \| - \| - \|
	\| 11.8644 \| 145 \| 4.9652 \| - \| - \| - \| - \| - \| - \|
	\| 12.1356 \| 146 \| 4.8118 \| - \| - \| - \| - \| - \| - \|
	\| 12.4068 \| 147 \| 4.704 \| - \| - \| - \| - \| - \| - \|
	\| 12.6780 \| 148 \| 4.8922 \| - \| - \| - \| - \| - \| - \|
	\| 12.9492 \| 149 \| 4.6571 \| 5.1441 \| 0.0061 \| 0.0055 \| 0.0064 \| 0.0053 \| 0.0064 \|
	\| 10.1695 \| 150 \| 8.1284 \| - \| - \| - \| - \| - \| - \|
	\| 10.4407 \| 151 \| 12.5703 \| - \| - \| - \| - \| - \| - \|
	\| 10.7119 \| 152 \| 11.8696 \| - \| - \| - \| - \| - \| - \|
	\| 10.9831 \| 153 \| 4.8543 \| - \| - \| - \| - \| - \| - \|
	\| 11.2542 \| 154 \| 4.8099 \| - \| - \| - \| - \| - \| - \|
	\| 11.5254 \| 155 \| 4.7009 \| - \| - \| - \| - \| - \| - \|
	\| 11.7966 \| 156 \| 4.7986 \| - \| - \| - \| - \| - \| - \|
	\| 12.0678 \| 157 \| 4.7973 \| - \| - \| - \| - \| - \| - \|
	\| 12.3390 \| 158 \| 4.5529 \| - \| - \| - \| - \| - \| - \|
	\| 12.6102 \| 159 \| 5.0275 \| - \| - \| - \| - \| - \| - \|
	\| 12.8814 \| 160 \| 4.6675 \| - \| - \| - \| - \| - \| - \|
	\| 13.1525 \| 161 \| 4.6538 \| - \| - \| - \| - \| - \| - \|
	\| 13.4237 \| 162 \| 4.8355 \| - \| - \| - \| - \| - \| - \|
	\| 13.6949 \| 163 \| 4.6304 \| - \| - \| - \| - \| - \| - \|
	\| 13.9661 \| 164 \| 4.7047 \| 5.1242 \| 0.0064 \| 0.0054 \| 0.0064 \| 0.0095 \| 0.0064 \|
	\| 11.1864 \| 165 \| 8.6549 \| - \| - \| - \| - \| - \| - \|
	\| 11.4576 \| 166 \| 12.4788 \| - \| - \| - \| - \| - \| - \|
	\| 11.7288 \| 167 \| 11.6425 \| - \| - \| - \| - \| - \| - \|
	\| 12.0 \| 168 \| 4.5654 \| - \| - \| - \| - \| - \| - \|
	\| 12.2712 \| 169 \| 4.7016 \| - \| - \| - \| - \| - \| - \|
	\| 12.5424 \| 170 \| 4.3306 \| - \| - \| - \| - \| - \| - \|
	\| 12.8136 \| 171 \| 4.9692 \| - \| - \| - \| - \| - \| - \|
	\| 13.0847 \| 172 \| 4.7557 \| - \| - \| - \| - \| - \| - \|
	\| 13.3559 \| 173 \| 4.8665 \| - \| - \| - \| - \| - \| - \|
	\| 13.6271 \| 174 \| 4.8338 \| - \| - \| - \| - \| - \| - \|
	\| 13.8983 \| 175 \| 4.9221 \| - \| - \| - \| - \| - \| - \|
	\| 14.1695 \| 176 \| 4.4968 \| - \| - \| - \| - \| - \| - \|
	\| 14.4407 \| 177 \| 4.6104 \| - \| - \| - \| - \| - \| - \|
	\| 14.7119 \| 178 \| 4.8449 \| - \| - \| - \| - \| - \| - \|
	\| 14.9831 \| 179 \| 4.2392 \| 5.1123 \| 0.0059 \| 0.0055 \| 0.0065 \| 0.0094 \| 0.0065 \|
	\| 12.2034 \| 180 \| 9.4893 \| - \| - \| - \| - \| - \| - \|
	\| 12.4746 \| 181 \| 12.4241 \| - \| - \| - \| - \| - \| - \|
	\| 12.7458 \| 182 \| 11.0389 \| - \| - \| - \| - \| - \| - \|
	\| 13.0169 \| 183 \| 4.7595 \| - \| - \| - \| - \| - \| - \|
	\| 13.2881 \| 184 \| 4.5408 \| - \| - \| - \| - \| - \| - \|
	\| 13.5593 \| 185 \| 4.6108 \| - \| - \| - \| - \| - \| - \|
	\| 13.8305 \| 186 \| 4.5832 \| - \| - \| - \| - \| - \| - \|
	\| 14.1017 \| 187 \| 4.6741 \| - \| - \| - \| - \| - \| - \|
	\| 14.3729 \| 188 \| 4.9353 \| - \| - \| - \| - \| - \| - \|
	\| 14.6441 \| 189 \| 5.0511 \| - \| - \| - \| - \| - \| - \|
	\| 14.9153 \| 190 \| 4.6575 \| - \| - \| - \| - \| - \| - \|
	\| 15.1864 \| 191 \| 4.648 \| - \| - \| - \| - \| - \| - \|
	\| 15.4576 \| 192 \| 4.6224 \| - \| - \| - \| - \| - \| - \|
	\| 15.7288 \| 193 \| 4.9292 \| - \| - \| - \| - \| - \| - \|
	\| 16.0 \| 194 \| 3.7805 \| 5.1058 \| 0.0063 \| 0.0057 \| 0.0062 \| 0.0094 \| 0.0062 \|
	\| 13.2203 \| 195 \| 10.2695 \| - \| - \| - \| - \| - \| - \|
	\| 13.4915 \| 196 \| 12.5043 \| - \| - \| - \| - \| - \| - \|
	\| 13.7627 \| 197 \| 10.4795 \| - \| - \| - \| - \| - \| - \|
	\| 14.0339 \| 198 \| 4.6901 \| - \| - \| - \| - \| - \| - \|
	\| 14.3051 \| 199 \| 4.6538 \| - \| - \| - \| - \| - \| - \|
	\| 14.5763 \| 200 \| 4.4736 \| - \| - \| - \| - \| - \| - \|
	\| 14.8475 \| 201 \| 4.4383 \| - \| - \| - \| - \| - \| - \|
	\| 15.1186 \| 202 \| 5.0382 \| - \| - \| - \| - \| - \| - \|
	\| 15.3898 \| 203 \| 4.5636 \| - \| - \| - \| - \| - \| - \|
	\| 15.6610 \| 204 \| 4.8089 \| - \| - \| - \| - \| - \| - \|
	\| 15.9322 \| 205 \| 4.4746 \| - \| - \| - \| - \| - \| - \|
	\| 16.2034 \| 206 \| 4.5876 \| - \| - \| - \| - \| - \| - \|
	\| 16.4746 \| 207 \| 4.4972 \| - \| - \| - \| - \| - \| - \|
	\| 16.7458 \| 208 \| 4.8569 \| - \| - \| - \| - \| - \| - \|
	\| 17.0169 \| 209 \| 3.5883 \| 5.1031 \| 0.0059 \| 0.0057 \| 0.0061 \| 0.0095 \| 0.0061 \|
	\| 14.2373 \| 210 \| 10.8988 \| - \| - \| - \| - \| - \| - \|
	\| 14.5085 \| 211 \| 12.4944 \| - \| - \| - \| - \| - \| - \|
	\| 14.7797 \| 212 \| 10.1041 \| - \| - \| - \| - \| - \| - \|
	\| 15.0508 \| 213 \| 4.8811 \| - \| - \| - \| - \| - \| - \|
	\| 15.3220 \| 214 \| 4.6292 \| - \| - \| - \| - \| - \| - \|
	\| 15.5932 \| 215 \| 4.4828 \| - \| - \| - \| - \| - \| - \|
	\| 15.8644 \| 216 \| 4.7588 \| - \| - \| - \| - \| - \| - \|
	\| 16.1356 \| 217 \| 4.26 \| - \| - \| - \| - \| - \| - \|
	\| 16.4068 \| 218 \| 4.9124 \| - \| - \| - \| - \| - \| - \|
	\| 16.6780 \| 219 \| 4.8098 \| - \| - \| - \| - \| - \| - \|
	\| 16.9492 \| 220 \| 4.4439 \| - \| - \| - \| - \| - \| - \|
	\| 17.2203 \| 221 \| 4.4824 \| - \| - \| - \| - \| - \| - \|
	\| 17.4915 \| 222 \| 4.7771 \| - \| - \| - \| - \| - \| - \|
	\| 17.7627 \| 223 \| 4.5966 \| - \| - \| - \| - \| - \| - \|
	\| 18.0339 \| 224 \| 3.1409 \| 5.1009 \| 0.0055 \| 0.0057 \| 0.0062 \| 0.0052 \| 0.0062 \|
	\| 15.2542 \| 225 \| 11.657 \| - \| - \| - \| - \| - \| - \|
	\| 15.5254 \| 226 \| 12.5032 \| - \| - \| - \| - \| - \| - \|
	\| 15.7966 \| 227 \| 9.4495 \| - \| - \| - \| - \| - \| - \|
	\| 16.0678 \| 228 \| 4.7099 \| - \| - \| - \| - \| - \| - \|
	\| 16.3390 \| 229 \| 4.6049 \| - \| - \| - \| - \| - \| - \|
	\| 16.6102 \| 230 \| 4.6311 \| - \| - \| - \| - \| - \| - \|
	\| 16.8814 \| 231 \| 4.7562 \| - \| - \| - \| - \| - \| - \|
	\| 17.1525 \| 232 \| 4.7195 \| - \| - \| - \| - \| - \| - \|
	\| 17.4237 \| 233 \| 4.8557 \| - \| - \| - \| - \| - \| - \|
	\| 17.6949 \| 234 \| 4.8423 \| - \| - \| - \| - \| - \| - \|
	\| 17.9661 \| 235 \| 4.5764 \| - \| - \| - \| - \| - \| - \|
	\| 18.2373 \| 236 \| 4.5081 \| - \| - \| - \| - \| - \| - \|
	\| 18.5085 \| 237 \| 4.7974 \| - \| - \| - \| - \| - \| - \|
	\| 18.7797 \| 238 \| 4.871 \| - \| - \| - \| - \| - \| - \|
	\| 19.0508 \| 239 \| 2.8558 \| 5.1020 \| 0.0054 \| 0.0057 \| 0.0061 \| 0.0054 \| 0.0061 \|
	\| 16.2712 \| 240 \| 12.4297 \| - \| - \| - \| - \| - \| - \|
	\| 16.5424 \| 241 \| 12.5186 \| - \| - \| - \| - \| - \| - \|
	\| 16.8136 \| 242 \| 8.8827 \| - \| - \| - \| - \| - \| - \|
	\| 17.0847 \| 243 \| 4.8406 \| - \| - \| - \| - \| - \| - \|
	\| 17.3559 \| 244 \| 4.4367 \| - \| - \| - \| - \| - \| - \|
	\| 17.6271 \| 245 \| 4.5996 \| - \| - \| - \| - \| - \| - \|
	\| 17.8983 \| 246 \| 4.6692 \| - \| - \| - \| - \| - \| - \|
	\| 18.1695 \| 247 \| 4.6498 \| - \| - \| - \| - \| - \| - \|
	\| 18.4407 \| 248 \| 4.7211 \| - \| - \| - \| - \| - \| - \|
	\| 18.7119 \| 249 \| 4.7675 \| - \| - \| - \| - \| - \| - \|
	\| 18.9831 \| 250 \| 4.4405 \| - \| - \| - \| - \| - \| - \|
	\| 19.2542 \| 251 \| 4.6778 \| - \| - \| - \| - \| - \| - \|
	\| 19.5254 \| 252 \| 4.6674 \| - \| - \| - \| - \| - \| - \|
	\| 19.7966 \| 253 \| 4.735 \| 5.1036 \| 0.0054 \| 0.0056 \| 0.0060 \| 0.0054 \| 0.0060 \|
	\| 17.0169 \| 254 \| 3.6188 \| - \| - \| - \| - \| - \| - \|
	\| 17.2881 \| 255 \| 12.4112 \| - \| - \| - \| - \| - \| - \|
	\| 17.5593 \| 256 \| 12.5261 \| - \| - \| - \| - \| - \| - \|
	\| 17.8305 \| 257 \| 8.3408 \| - \| - \| - \| - \| - \| - \|
	\| 18.1017 \| 258 \| 4.6496 \| - \| - \| - \| - \| - \| - \|
	\| 18.3729 \| 259 \| 4.7177 \| - \| - \| - \| - \| - \| - \|
	\| 18.6441 \| 260 \| 4.7956 \| - \| - \| - \| - \| - \| - \|
	\| 18.9153 \| 261 \| 4.7228 \| - \| - \| - \| - \| - \| - \|
	\| 19.1864 \| 262 \| 4.6083 \| - \| - \| - \| - \| - \| - \|
	\| 19.4576 \| 263 \| 4.7985 \| - \| - \| - \| - \| - \| - \|
	\| 19.7288 \| 264 \| 4.6675 \| - \| - \| - \| - \| - \| - \|
	\| 20.0 \| 265 \| 4.6353 \| - \| - \| - \| - \| - \| - \|
	\| 20.2712 \| 266 \| 4.5717 \| - \| - \| - \| - \| - \| - \|
	\| 20.5424 \| 267 \| 4.4358 \| - \| - \| - \| - \| - \| - \|
	\| 20.8136 \| 268 \| 4.8288 \| 5.1030 \| 0.0056 \| 0.0057 \| 0.0062 \| 0.0053 \| 0.0062 \|
	\| 18.0339 \| 269 \| 3.7877 \| - \| - \| - \| - \| - \| - \|
	\| 18.3051 \| 270 \| 12.4042 \| - \| - \| - \| - \| - \| - \|
	\| 18.5763 \| 271 \| 12.4793 \| - \| - \| - \| - \| - \| - \|
	\| 18.8475 \| 272 \| 7.9475 \| - \| - \| - \| - \| - \| - \|
	\| 19.1186 \| 273 \| 4.5502 \| - \| - \| - \| - \| - \| - \|
	\| 19.3898 \| 274 \| 4.5565 \| - \| - \| - \| - \| - \| - \|
	\| 19.6610 \| 275 \| 4.4172 \| - \| - \| - \| - \| - \| - \|
	\| 19.9322 \| 276 \| 4.5319 \| - \| - \| - \| - \| - \| - \|
	\| 20.2034 \| 277 \| 4.5635 \| - \| - \| - \| - \| - \| - \|
	\| 20.4746 \| 278 \| 4.5233 \| - \| - \| - \| - \| - \| - \|
	\| 20.7458 \| 279 \| 4.6766 \| - \| - \| - \| - \| - \| - \|
	\| 21.0169 \| 280 \| 4.5863 \| - \| - \| - \| - \| - \| - \|
	\| 21.2881 \| 281 \| 4.5784 \| - \| - \| - \| - \| - \| - \|
	\| 21.5593 \| 282 \| 4.7198 \| - \| - \| - \| - \| - \| - \|
	\| 21.8305 \| 283 \| 4.7383 \| 5.1065 \| 0.0054 \| 0.0056 \| 0.0061 \| 0.0050 \| 0.0061 \|
	\| 19.0508 \| 284 \| 4.4257 \| - \| - \| - \| - \| - \| - \|
	\| 19.3220 \| 285 \| 12.3475 \| - \| - \| - \| - \| - \| - \|
	\| 19.5932 \| 286 \| 12.5168 \| - \| - \| - \| - \| - \| - \|
	\| 19.8644 \| 287 \| 7.3671 \| - \| - \| - \| - \| - \| - \|
	\| 20.1356 \| 288 \| 4.3771 \| - \| - \| - \| - \| - \| - \|
	\| 20.4068 \| 289 \| 4.542 \| - \| - \| - \| - \| - \| - \|
	\| 20.6780 \| 290 \| 4.3629 \| - \| - \| - \| - \| - \| - \|
	\| 20.9492 \| 291 \| 4.5474 \| - \| - \| - \| - \| - \| - \|
	\| 21.2203 \| 292 \| 4.7436 \| - \| - \| - \| - \| - \| - \|
	\| 21.4915 \| 293 \| 4.5915 \| - \| - \| - \| - \| - \| - \|
	\| 21.7627 \| 294 \| 4.5522 \| - \| - \| - \| - \| - \| - \|
	\| 22.0339 \| 295 \| 4.6591 \| - \| - \| - \| - \| - \| - \|
	\| 22.3051 \| 296 \| 4.6179 \| - \| - \| - \| - \| - \| - \|
	\| 22.5763 \| 297 \| 4.6086 \| - \| - \| - \| - \| - \| - \|
	\| 22.8475 \| 298 \| 4.8808 \| 5.1083 \| 0.0054 \| 0.0057 \| 0.0062 \| 0.0055 \| 0.0062 \|
	\| 20.0678 \| 299 \| 4.7358 \| - \| - \| - \| - \| - \| - \|
	\| 20.3390 \| 300 \| 12.3209 \| - \| - \| - \| - \| - \| - \|
	\| 20.6102 \| 301 \| 12.6406 \| - \| - \| - \| - \| - \| - \|
	\| 20.8814 \| 302 \| 6.7971 \| - \| - \| - \| - \| - \| - \|
	\| 21.1525 \| 303 \| 4.3723 \| - \| - \| - \| - \| - \| - \|
	\| 21.4237 \| 304 \| 4.61 \| - \| - \| - \| - \| - \| - \|
	\| 21.6949 \| 305 \| 4.4624 \| - \| - \| - \| - \| - \| - \|
	\| 21.9661 \| 306 \| 4.6145 \| - \| - \| - \| - \| - \| - \|
	\| 22.2373 \| 307 \| 4.5794 \| - \| - \| - \| - \| - \| - \|
	\| 22.5085 \| 308 \| 4.6625 \| - \| - \| - \| - \| - \| - \|
	\| 22.7797 \| 309 \| 4.5499 \| - \| - \| - \| - \| - \| - \|
	\| 23.0508 \| 310 \| 4.5657 \| - \| - \| - \| - \| - \| - \|
	\| 23.3220 \| 311 \| 4.5896 \| - \| - \| - \| - \| - \| - \|
	\| 23.5932 \| 312 \| 4.5692 \| - \| - \| - \| - \| - \| - \|
	\| 23.8644 \| 313 \| 4.93 \| 5.1119 \| 0.0055 \| 0.0057 \| 0.0061 \| 0.0056 \| 0.0061 \|
	\| 21.0847 \| 314 \| 5.3829 \| - \| - \| - \| - \| - \| - \|
	\| 21.3559 \| 315 \| 12.3422 \| - \| - \| - \| - \| - \| - \|
	\| 21.6271 \| 316 \| 12.601 \| - \| - \| - \| - \| - \| - \|
	\| 21.8983 \| 317 \| 6.5062 \| - \| - \| - \| - \| - \| - \|
	\| 22.1695 \| 318 \| 4.4681 \| - \| - \| - \| - \| - \| - \|
	\| 22.4407 \| 319 \| 4.4244 \| - \| - \| - \| - \| - \| - \|
	\| 22.7119 \| 320 \| 4.4514 \| - \| - \| - \| - \| - \| - \|
	\| 22.9831 \| 321 \| 4.5469 \| - \| - \| - \| - \| - \| - \|
	\| 23.2542 \| 322 \| 4.6924 \| - \| - \| - \| - \| - \| - \|
	\| 23.5254 \| 323 \| 4.682 \| - \| - \| - \| - \| - \| - \|
	\| 23.7966 \| 324 \| 4.6403 \| - \| - \| - \| - \| - \| - \|
	\| 24.0678 \| 325 \| 4.6272 \| - \| - \| - \| - \| - \| - \|
	\| 24.3390 \| 326 \| 4.3605 \| - \| - \| - \| - \| - \| - \|
	\| 24.6102 \| 327 \| 4.5992 \| - \| - \| - \| - \| - \| - \|
	\| 24.8814 \| 328 \| 4.6776 \| 5.1126 \| 0.0053 \| 0.0057 \| 0.0061 \| 0.0056 \| 0.0061 \|
	\| 22.1017 \| 329 \| 5.8504 \| - \| - \| - \| - \| - \| - \|
	\| 22.3729 \| 330 \| 12.335 \| - \| - \| - \| - \| - \| - \|
	\| 22.6441 \| 331 \| 12.5779 \| - \| - \| - \| - \| - \| - \|
	\| 22.9153 \| 332 \| 5.7261 \| - \| - \| - \| - \| - \| - \|
	\| 23.1864 \| 333 \| 4.5411 \| - \| - \| - \| - \| - \| - \|
	\| 23.4576 \| 334 \| 4.4783 \| - \| - \| - \| - \| - \| - \|
	\| 23.7288 \| 335 \| 4.5589 \| - \| - \| - \| - \| - \| - \|
	\| 24.0 \| 336 \| 4.6305 \| - \| - \| - \| - \| - \| - \|
	\| 24.2712 \| 337 \| 4.674 \| - \| - \| - \| - \| - \| - \|
	\| 24.5424 \| 338 \| 4.7455 \| - \| - \| - \| - \| - \| - \|
	\| 24.8136 \| 339 \| 4.6011 \| - \| - \| - \| - \| - \| - \|
	\| 25.0847 \| 340 \| 4.5899 \| - \| - \| - \| - \| - \| - \|
	\| 25.3559 \| 341 \| 4.3981 \| - \| - \| - \| - \| - \| - \|
	\| 25.6271 \| 342 \| 4.7031 \| - \| - \| - \| - \| - \| - \|
	\| 25.8983 \| 343 \| 4.68 \| 5.1182 \| 0.0054 \| 0.0057 \| 0.0059 \| 0.0056 \| 0.0059 \|
	\| 23.1186 \| 344 \| 6.3521 \| - \| - \| - \| - \| - \| - \|
	\| 23.3898 \| 345 \| 12.2283 \| - \| - \| - \| - \| - \| - \|
	\| 23.6610 \| 346 \| 12.533 \| - \| - \| - \| - \| - \| - \|
	\| 23.9322 \| 347 \| 5.2654 \| - \| - \| - \| - \| - \| - \|
	\| 24.2034 \| 348 \| 4.3667 \| - \| - \| - \| - \| - \| - \|
	\| 24.4746 \| 349 \| 4.4718 \| - \| - \| - \| - \| - \| - \|
	\| 24.7458 \| 350 \| 4.6212 \| - \| - \| - \| - \| - \| - \|
	\| 25.0169 \| 351 \| 4.447 \| - \| - \| - \| - \| - \| - \|
	\| 25.2881 \| 352 \| 4.6247 \| - \| - \| - \| - \| - \| - \|
	\| 25.5593 \| 353 \| 5.0093 \| - \| - \| - \| - \| - \| - \|
	\| 25.8305 \| 354 \| 4.6316 \| - \| - \| - \| - \| - \| - \|
	\| 26.1017 \| 355 \| 4.6655 \| - \| - \| - \| - \| - \| - \|
	\| 26.3729 \| 356 \| 4.5964 \| - \| - \| - \| - \| - \| - \|
	\| 26.6441 \| 357 \| 4.682 \| - \| - \| - \| - \| - \| - \|
	\| 26.9153 \| 358 \| 4.6375 \| 5.1205 \| 0.0051 \| 0.0056 \| 0.0059 \| 0.0055 \| 0.0059 \|
	\| 24.1356 \| 359 \| 6.727 \| - \| - \| - \| - \| - \| - \|
	\| 24.4068 \| 360 \| 12.3706 \| - \| - \| - \| - \| - \| - \|
	\| 24.6780 \| 361 \| 12.4755 \| - \| - \| - \| - \| - \| - \|
	\| 24.9492 \| 362 \| 4.623 \| - \| - \| - \| - \| - \| - \|
	\| 25.2203 \| 363 \| 4.2947 \| - \| - \| - \| - \| - \| - \|
	\| 25.4915 \| 364 \| 4.3993 \| - \| - \| - \| - \| - \| - \|
	\| 25.7627 \| 365 \| 4.4148 \| - \| - \| - \| - \| - \| - \|
	\| 26.0339 \| 366 \| 4.2376 \| - \| - \| - \| - \| - \| - \|
	\| 26.3051 \| 367 \| 4.6334 \| - \| - \| - \| - \| - \| - \|
	\| 26.5763 \| 368 \| 4.7007 \| - \| - \| - \| - \| - \| - \|
	\| 26.8475 \| 369 \| 4.3542 \| - \| - \| - \| - \| - \| - \|
	\| 27.1186 \| 370 \| 4.7036 \| - \| - \| - \| - \| - \| - \|
	\| 27.3898 \| 371 \| 4.2382 \| - \| - \| - \| - \| - \| - \|
	\| 27.6610 \| 372 \| 4.5011 \| - \| - \| - \| - \| - \| - \|
	\| 27.9322 \| 373 \| 4.6292 \| 5.1241 \| 0.0051 \| 0.0056 \| 0.0059 \| 0.0056 \| 0.0059 \|
	\| 25.1525 \| 374 \| 7.3562 \| - \| - \| - \| - \| - \| - \|
	\| 25.4237 \| 375 \| 12.2926 \| - \| - \| - \| - \| - \| - \|
	\| 25.6949 \| 376 \| 12.1694 \| - \| - \| - \| - \| - \| - \|
	\| 25.9661 \| 377 \| 4.7183 \| - \| - \| - \| - \| - \| - \|
	\| 26.2373 \| 378 \| 4.4099 \| - \| - \| - \| - \| - \| - \|
	\| 26.5085 \| 379 \| 4.3366 \| - \| - \| - \| - \| - \| - \|
	\| 26.7797 \| 380 \| 4.4848 \| - \| - \| - \| - \| - \| - \|
	\| 27.0508 \| 381 \| 4.6947 \| - \| - \| - \| - \| - \| - \|
	\| 27.3220 \| 382 \| 4.5683 \| - \| - \| - \| - \| - \| - \|
	\| 27.5932 \| 383 \| 4.7691 \| - \| - \| - \| - \| - \| - \|
	\| 27.8644 \| 384 \| 4.3879 \| - \| - \| - \| - \| - \| - \|
	\| 28.1356 \| 385 \| 4.3461 \| - \| - \| - \| - \| - \| - \|
	\| 28.4068 \| 386 \| 4.4756 \| - \| - \| - \| - \| - \| - \|
	\| 28.6780 \| 387 \| 4.5355 \| - \| - \| - \| - \| - \| - \|
	\| 28.9492 \| 388 \| 4.4837 \| 5.1278 \| 0.0052 \| 0.0056 \| 0.0059 \| 0.0054 \| 0.0059 \|
	\| 26.1695 \| 389 \| 7.9407 \| - \| - \| - \| - \| - \| - \|
	\| 26.4407 \| 390 \| 12.3054 \| - \| - \| - \| - \| - \| - \|
	\| 26.7119 \| 391 \| 11.6158 \| - \| - \| - \| - \| - \| - \|
	\| 26.9831 \| 392 \| 4.5724 \| - \| - \| - \| - \| - \| - \|
	\| 27.2542 \| 393 \| 4.467 \| - \| - \| - \| - \| - \| - \|
	\| 27.5254 \| 394 \| 4.4395 \| - \| - \| - \| - \| - \| - \|
	\| 27.7966 \| 395 \| 4.4111 \| - \| - \| - \| - \| - \| - \|
	\| 28.0678 \| 396 \| 4.5565 \| - \| - \| - \| - \| - \| - \|
	\| 28.3390 \| 397 \| 4.6063 \| - \| - \| - \| - \| - \| - \|
	\| 28.6102 \| 398 \| 4.5312 \| - \| - \| - \| - \| - \| - \|
	\| 28.8814 \| 399 \| 4.5436 \| - \| - \| - \| - \| - \| - \|
	\| 29.1525 \| 400 \| 4.5366 \| - \| - \| - \| - \| - \| - \|
	\| 29.4237 \| 401 \| 4.4488 \| - \| - \| - \| - \| - \| - \|
	\| 29.6949 \| 402 \| 4.5641 \| - \| - \| - \| - \| - \| - \|
	\| 29.9661 \| 403 \| 4.2491 \| 5.1303 \| 0.0053 \| 0.0057 \| 0.0060 \| 0.0055 \| 0.0060 \|
	\| 27.1864 \| 404 \| 8.574 \| - \| - \| - \| - \| - \| - \|
	\| 27.4576 \| 405 \| 12.2836 \| - \| - \| - \| - \| - \| - \|
	\| 27.7288 \| 406 \| 11.1935 \| - \| - \| - \| - \| - \| - \|
	\| 28.0 \| 407 \| 4.5464 \| - \| - \| - \| - \| - \| - \|
	\| 28.2712 \| 408 \| 4.3132 \| - \| - \| - \| - \| - \| - \|
	\| 28.5424 \| 409 \| 4.3553 \| - \| - \| - \| - \| - \| - \|
	\| 28.8136 \| 410 \| 4.4679 \| - \| - \| - \| - \| - \| - \|
	\| 29.0847 \| 411 \| 4.7705 \| - \| - \| - \| - \| - \| - \|
	\| 29.3559 \| 412 \| 4.5667 \| - \| - \| - \| - \| - \| - \|
	\| 29.6271 \| 413 \| 4.6547 \| - \| - \| - \| - \| - \| - \|
	\| 29.8983 \| 414 \| 4.6709 \| - \| - \| - \| - \| - \| - \|
	\| 30.1695 \| 415 \| 4.784 \| - \| - \| - \| - \| - \| - \|
	\| 30.4407 \| 416 \| 4.4368 \| - \| - \| - \| - \| - \| - \|
	\| 30.7119 \| 417 \| 4.6159 \| - \| - \| - \| - \| - \| - \|
	\| 30.9831 \| 418 \| 4.0117 \| 5.1322 \| 0.0050 \| 0.0057 \| 0.0059 \| 0.0054 \| 0.0059 \|
	\| 28.2034 \| 419 \| 9.2905 \| - \| - \| - \| - \| - \| - \|
	\| 28.4746 \| 420 \| 12.2439 \| - \| - \| - \| - \| - \| - \|
	\| 28.7458 \| 421 \| 10.722 \| - \| - \| - \| - \| - \| - \|
	\| 29.0169 \| 422 \| 4.6608 \| - \| - \| - \| - \| - \| - \|
	\| 29.2881 \| 423 \| 4.5196 \| - \| - \| - \| - \| - \| - \|
	\| 29.5593 \| 424 \| 4.4313 \| - \| - \| - \| - \| - \| - \|
	\| 29.8305 \| 425 \| 4.513 \| - \| - \| - \| - \| - \| - \|
	\| 30.1017 \| 426 \| 4.5812 \| - \| - \| - \| - \| - \| - \|
	\| 30.3729 \| 427 \| 4.5275 \| - \| - \| - \| - \| - \| - \|
	\| 30.6441 \| 428 \| 4.8022 \| - \| - \| - \| - \| - \| - \|
	\| 30.9153 \| 429 \| 4.5171 \| - \| - \| - \| - \| - \| - \|
	\| 31.1864 \| 430 \| 4.5968 \| - \| - \| - \| - \| - \| - \|
	\| 31.4576 \| 431 \| 4.2145 \| - \| - \| - \| - \| - \| - \|
	\| 31.7288 \| 432 \| 4.7041 \| - \| - \| - \| - \| - \| - \|
	\| 32.0 \| 433 \| 3.6187 \| 5.1356 \| 0.0051 \| 0.0057 \| 0.0059 \| 0.0055 \| 0.0059 \|
	\| 29.2203 \| 434 \| 10.0897 \| - \| - \| - \| - \| - \| - \|
	\| 29.4915 \| 435 \| 12.2909 \| - \| - \| - \| - \| - \| - \|
	\| 29.7627 \| 436 \| 10.1362 \| - \| - \| - \| - \| - \| - \|
	\| 30.0339 \| 437 \| 4.5172 \| - \| - \| - \| - \| - \| - \|
	\| 30.3051 \| 438 \| 4.3273 \| - \| - \| - \| - \| - \| - \|
	\| 30.5763 \| 439 \| 4.5272 \| - \| - \| - \| - \| - \| - \|
	\| 30.8475 \| 440 \| 4.376 \| - \| - \| - \| - \| - \| - \|
	\| 31.1186 \| 441 \| 4.5803 \| - \| - \| - \| - \| - \| - \|
	\| 31.3898 \| 442 \| 4.5654 \| - \| - \| - \| - \| - \| - \|
	\| 31.6610 \| 443 \| 4.5024 \| - \| - \| - \| - \| - \| - \|
	\| 31.9322 \| 444 \| 4.5889 \| - \| - \| - \| - \| - \| - \|
	\| 32.2034 \| 445 \| 4.6489 \| - \| - \| - \| - \| - \| - \|
	\| 32.4746 \| 446 \| 4.4505 \| - \| - \| - \| - \| - \| - \|
	\| 32.7458 \| 447 \| 4.7026 \| - \| - \| - \| - \| - \| - \|
	\| 33.0169 \| 448 \| 3.4719 \| 5.1368 \| 0.0050 \| 0.0056 \| 0.0059 \| 0.0052 \| 0.0059 \|
	\| 30.2373 \| 449 \| 10.7633 \| - \| - \| - \| - \| - \| - \|
	\| 30.5085 \| 450 \| 12.3203 \| - \| - \| - \| - \| - \| - \|
	\| 30.7797 \| 451 \| 9.7535 \| - \| - \| - \| - \| - \| - \|
	\| 31.0508 \| 452 \| 4.7462 \| - \| - \| - \| - \| - \| - \|
	\| 31.3220 \| 453 \| 4.4271 \| - \| - \| - \| - \| - \| - \|
	\| 31.5932 \| 454 \| 4.4347 \| - \| - \| - \| - \| - \| - \|
	\| 31.8644 \| 455 \| 4.6443 \| - \| - \| - \| - \| - \| - \|
	\| 32.1356 \| 456 \| 4.6344 \| - \| - \| - \| - \| - \| - \|
	\| 32.4068 \| 457 \| 4.6518 \| - \| - \| - \| - \| - \| - \|
	\| 32.6780 \| 458 \| 4.6437 \| - \| - \| - \| - \| - \| - \|
	\| 32.9492 \| 459 \| 4.6168 \| - \| - \| - \| - \| - \| - \|
	\| 33.2203 \| 460 \| 4.4948 \| - \| - \| - \| - \| - \| - \|
	\| 33.4915 \| 461 \| 4.5268 \| - \| - \| - \| - \| - \| - \|
	\| 33.7627 \| 462 \| 4.4844 \| - \| - \| - \| - \| - \| - \|
	\| 34.0339 \| 463 \| 3.276 \| 5.1384 \| 0.0051 \| 0.0057 \| 0.0060 \| 0.0053 \| 0.0060 \|
	\| 31.2542 \| 464 \| 11.5311 \| - \| - \| - \| - \| - \| - \|
	\| 31.5254 \| 465 \| 12.3812 \| - \| - \| - \| - \| - \| - \|
	\| 31.7966 \| 466 \| 9.1499 \| - \| - \| - \| - \| - \| - \|
	\| 32.0678 \| 467 \| 4.7032 \| - \| - \| - \| - \| - \| - \|
	\| 32.3390 \| 468 \| 4.2429 \| - \| - \| - \| - \| - \| - \|
	\| 32.6102 \| 469 \| 4.549 \| - \| - \| - \| - \| - \| - \|
	\| 32.8814 \| 470 \| 4.7083 \| - \| - \| - \| - \| - \| - \|
	\| 33.1525 \| 471 \| 4.5348 \| - \| - \| - \| - \| - \| - \|
	\| 33.4237 \| 472 \| 4.472 \| - \| - \| - \| - \| - \| - \|
	\| 33.6949 \| 473 \| 4.5818 \| - \| - \| - \| - \| - \| - \|
	\| 33.9661 \| 474 \| 4.5534 \| - \| - \| - \| - \| - \| - \|
	\| 34.2373 \| 475 \| 4.5743 \| - \| - \| - \| - \| - \| - \|
	\| 34.5085 \| 476 \| 4.54 \| - \| - \| - \| - \| - \| - \|
	\| 34.7797 \| 477 \| 4.681 \| - \| - \| - \| - \| - \| - \|
	\| 35.0508 \| 478 \| 2.9902 \| 5.1397 \| 0.0052 \| 0.0057 \| 0.0059 \| 0.0053 \| 0.0059 \|
	\| 32.2712 \| 479 \| 12.3174 \| - \| - \| - \| - \| - \| - \|
	\| 32.5424 \| 480 \| 12.2996 \| - \| - \| - \| - \| - \| - \|
	\| 32.8136 \| 481 \| 8.7153 \| - \| - \| - \| - \| - \| - \|
	\| 33.0847 \| 482 \| 4.5692 \| - \| - \| - \| - \| - \| - \|
	\| 33.3559 \| 483 \| 4.3255 \| - \| - \| - \| - \| - \| - \|
	\| 33.6271 \| 484 \| 4.4515 \| - \| - \| - \| - \| - \| - \|
	\| 33.8983 \| 485 \| 4.6708 \| - \| - \| - \| - \| - \| - \|
	\| 34.1695 \| 486 \| 4.2648 \| - \| - \| - \| - \| - \| - \|
	\| 34.4407 \| 487 \| 4.6268 \| - \| - \| - \| - \| - \| - \|
	\| 34.7119 \| 488 \| 4.703 \| - \| - \| - \| - \| - \| - \|
	\| 34.9831 \| 489 \| 4.6269 \| - \| - \| - \| - \| - \| - \|
	\| 35.2542 \| 490 \| 4.6464 \| - \| - \| - \| - \| - \| - \|
	\| 35.5254 \| 491 \| 4.4952 \| - \| - \| - \| - \| - \| - \|
	\| 35.7966 \| 492 \| 4.6097 \| 5.1406 \| 0.0052 \| 0.0058 \| 0.0058 \| 0.0054 \| 0.0058 \|
	\| 33.0169 \| 493 \| 3.2718 \| - \| - \| - \| - \| - \| - \|
	\| 33.2881 \| 494 \| 12.3329 \| - \| - \| - \| - \| - \| - \|
	\| 33.5593 \| 495 \| 12.3503 \| - \| - \| - \| - \| - \| - \|
	\| 33.8305 \| 496 \| 8.1544 \| - \| - \| - \| - \| - \| - \|
	\| 34.1017 \| 497 \| 4.4684 \| - \| - \| - \| - \| - \| - \|
	\| 34.3729 \| 498 \| 4.4062 \| - \| - \| - \| - \| - \| - \|
	\| 34.6441 \| 499 \| 4.2644 \| - \| - \| - \| - \| - \| - \|
	\| 34.9153 \| 500 \| 4.5294 \| - \| - \| - \| - \| - \| - \|
	\| 35.1864 \| 501 \| 4.673 \| - \| - \| - \| - \| - \| - \|
	\| 35.4576 \| 502 \| 4.4884 \| - \| - \| - \| - \| - \| - \|
	\| 35.7288 \| 503 \| 4.5989 \| - \| - \| - \| - \| - \| - \|
	\| 36.0 \| 504 \| 4.6182 \| - \| - \| - \| - \| - \| - \|
	\| 36.2712 \| 505 \| 4.6487 \| - \| - \| - \| - \| - \| - \|
	\| 36.5424 \| 506 \| 4.6436 \| - \| - \| - \| - \| - \| - \|
	\| 36.8136 \| 507 \| 4.6059 \| 5.1417 \| 0.0051 \| 0.0057 \| 0.0059 \| 0.0052 \| 0.0059 \|
	\| 34.0339 \| 508 \| 3.7589 \| - \| - \| - \| - \| - \| - \|
	\| 34.3051 \| 509 \| 12.2815 \| - \| - \| - \| - \| - \| - \|
	\| 34.5763 \| 510 \| 12.5481 \| - \| - \| - \| - \| - \| - \|
	\| 34.8475 \| 511 \| 7.6339 \| - \| - \| - \| - \| - \| - \|
	\| 35.1186 \| 512 \| 4.5528 \| - \| - \| - \| - \| - \| - \|
	\| 35.3898 \| 513 \| 4.3266 \| - \| - \| - \| - \| - \| - \|
	\| 35.6610 \| 514 \| 4.3093 \| - \| - \| - \| - \| - \| - \|
	\| 35.9322 \| 515 \| 4.7401 \| - \| - \| - \| - \| - \| - \|
	\| 36.2034 \| 516 \| 4.523 \| - \| - \| - \| - \| - \| - \|
	\| 36.4746 \| 517 \| 4.5255 \| - \| - \| - \| - \| - \| - \|
	\| 36.7458 \| 518 \| 4.5058 \| - \| - \| - \| - \| - \| - \|
	\| 37.0169 \| 519 \| 4.5614 \| - \| - \| - \| - \| - \| - \|
	\| 37.2881 \| 520 \| 4.5323 \| - \| - \| - \| - \| - \| - \|
	\| 37.5593 \| 521 \| 4.5739 \| - \| - \| - \| - \| - \| - \|
	\| 37.8305 \| 522 \| 4.6501 \| 5.1427 \| 0.0052 \| 0.0058 \| 0.0059 \| 0.0053 \| 0.0059 \|
	\| 35.0508 \| 523 \| 4.2083 \| - \| - \| - \| - \| - \| - \|
	\| 35.3220 \| 524 \| 12.2888 \| - \| - \| - \| - \| - \| - \|
	\| 35.5932 \| 525 \| 12.4709 \| - \| - \| - \| - \| - \| - \|
	\| 35.8644 \| 526 \| 7.3926 \| - \| - \| - \| - \| - \| - \|
	\| 36.1356 \| 527 \| 4.4719 \| - \| - \| - \| - \| - \| - \|
	\| 36.4068 \| 528 \| 4.5033 \| - \| - \| - \| - \| - \| - \|
	\| 36.6780 \| 529 \| 4.388 \| - \| - \| - \| - \| - \| - \|
	\| 36.9492 \| 530 \| 4.5606 \| - \| - \| - \| - \| - \| - \|
	\| 37.2203 \| 531 \| 4.6936 \| - \| - \| - \| - \| - \| - \|
	\| 37.4915 \| 532 \| 4.6008 \| - \| - \| - \| - \| - \| - \|
	\| 37.7627 \| 533 \| 4.6973 \| - \| - \| - \| - \| - \| - \|
	\| 38.0339 \| 534 \| 4.4194 \| - \| - \| - \| - \| - \| - \|
	\| 38.3051 \| 535 \| 4.5616 \| - \| - \| - \| - \| - \| - \|
	\| 38.5763 \| 536 \| 4.6307 \| - \| - \| - \| - \| - \| - \|
	\| 38.8475 \| 537 \| 4.8322 \| 5.1442 \| 0.0051 \| 0.0057 \| 0.0059 \| 0.0053 \| 0.0059 \|
	\| 36.0678 \| 538 \| 4.8388 \| - \| - \| - \| - \| - \| - \|
	\| 36.3390 \| 539 \| 12.2334 \| - \| - \| - \| - \| - \| - \|
	\| 36.6102 \| 540 \| 12.4205 \| - \| - \| - \| - \| - \| - \|
	\| 36.8814 \| 541 \| 6.9051 \| - \| - \| - \| - \| - \| - \|
	\| 37.1525 \| 542 \| 4.6011 \| - \| - \| - \| - \| - \| - \|
	\| 37.4237 \| 543 \| 4.4701 \| - \| - \| - \| - \| - \| - \|
	\| 37.6949 \| 544 \| 4.421 \| - \| - \| - \| - \| - \| - \|
	\| 37.9661 \| 545 \| 4.6877 \| - \| - \| - \| - \| - \| - \|
	\| 38.2373 \| 546 \| 4.6348 \| - \| - \| - \| - \| - \| - \|
	\| 38.5085 \| 547 \| 4.5822 \| - \| - \| - \| - \| - \| - \|
	\| 38.7797 \| 548 \| 4.5697 \| - \| - \| - \| - \| - \| - \|
	\| 39.0508 \| 549 \| 4.3118 \| - \| - \| - \| - \| - \| - \|
	\| 39.3220 \| 550 \| 4.5131 \| - \| - \| - \| - \| - \| - \|
	\| 39.5932 \| 551 \| 4.4879 \| - \| - \| - \| - \| - \| - \|
	\| 39.8644 \| 552 \| 4.5945 \| 5.1429 \| 0.0052 \| 0.0056 \| 0.0059 \| 0.0054 \| 0.0059 \|
	\| 37.0847 \| 553 \| 5.4083 \| - \| - \| - \| - \| - \| - \|
	\| 37.3559 \| 554 \| 12.2092 \| - \| - \| - \| - \| - \| - \|
	\| 37.6271 \| 555 \| 12.5043 \| - \| - \| - \| - \| - \| - \|
	\| 37.8983 \| 556 \| 6.1239 \| - \| - \| - \| - \| - \| - \|
	\| 38.1695 \| 557 \| 4.2932 \| - \| - \| - \| - \| - \| - \|
	\| 38.4407 \| 558 \| 4.3845 \| - \| - \| - \| - \| - \| - \|
	\| 38.7119 \| 559 \| 4.5619 \| - \| - \| - \| - \| - \| - \|
	\| 38.9831 \| 560 \| 4.6936 \| - \| - \| - \| - \| - \| - \|
	\| 39.2542 \| 561 \| 4.6636 \| - \| - \| - \| - \| - \| - \|
	\| 39.5254 \| 562 \| 4.7964 \| - \| - \| - \| - \| - \| - \|
	\| 39.7966 \| 563 \| 4.613 \| - \| - \| - \| - \| - \| - \|
	\| 40.0678 \| 564 \| 4.5856 \| - \| - \| - \| - \| - \| - \|
	\| 40.3390 \| 565 \| 4.4605 \| - \| - \| - \| - \| - \| - \|
	\| 40.6102 \| 566 \| 4.5461 \| - \| - \| - \| - \| - \| - \|
	\| 40.8814 \| 567 \| 4.7145 \| 5.1454 \| 0.0052 \| 0.0056 \| 0.0059 \| 0.0052 \| 0.0059 \|
	\| 38.1017 \| 568 \| 5.8311 \| - \| - \| - \| - \| - \| - \|
	\| 38.3729 \| 569 \| 12.2142 \| - \| - \| - \| - \| - \| - \|
	\| 38.6441 \| 570 \| 12.4489 \| - \| - \| - \| - \| - \| - \|
	\| 38.9153 \| 571 \| 5.7328 \| - \| - \| - \| - \| - \| - \|
	\| 39.1864 \| 572 \| 4.4402 \| - \| - \| - \| - \| - \| - \|
	\| 39.4576 \| 573 \| 4.1806 \| - \| - \| - \| - \| - \| - \|
	\| 39.7288 \| 574 \| 4.6327 \| - \| - \| - \| - \| - \| - \|
	\| 40.0 \| 575 \| 4.2768 \| - \| - \| - \| - \| - \| - \|
	\| 40.2712 \| 576 \| 4.4669 \| - \| - \| - \| - \| - \| - \|
	\| 40.5424 \| 577 \| 4.8094 \| - \| - \| - \| - \| - \| - \|
	\| 40.8136 \| 578 \| 4.5773 \| - \| - \| - \| - \| - \| - \|
	\| 41.0847 \| 579 \| 4.439 \| - \| - \| - \| - \| - \| - \|
	\| 41.3559 \| 580 \| 4.5718 \| - \| - \| - \| - \| - \| - \|
	\| 41.6271 \| 581 \| 4.5955 \| - \| - \| - \| - \| - \| - \|
	\| 41.8983 \| 582 \| 4.5043 \| 5.1443 \| 0.0051 \| 0.0056 \| 0.0059 \| 0.0054 \| 0.0059 \|
	\| 39.1186 \| 583 \| 6.359 \| - \| - \| - \| - \| - \| - \|
	\| 39.3898 \| 584 \| 12.212 \| - \| - \| - \| - \| - \| - \|
	\| 39.6610 \| 585 \| 12.538 \| - \| - \| - \| - \| - \| - \|
	\| 39.9322 \| 586 \| 5.0971 \| - \| - \| - \| - \| - \| - \|
	\| 40.2034 \| 587 \| 4.4783 \| - \| - \| - \| - \| - \| - \|
	\| 40.4746 \| 588 \| 4.394 \| - \| - \| - \| - \| - \| - \|
	\| 40.7458 \| 589 \| 4.4847 \| - \| - \| - \| - \| - \| - \|
	\| 41.0169 \| 590 \| 4.4116 \| - \| - \| - \| - \| - \| - \|
	\| 41.2881 \| 591 \| 4.3979 \| - \| - \| - \| - \| - \| - \|
	\| 41.5593 \| 592 \| 4.6652 \| - \| - \| - \| - \| - \| - \|
	\| 41.8305 \| 593 \| 4.3939 \| - \| - \| - \| - \| - \| - \|
	\| 42.1017 \| 594 \| 4.5555 \| - \| - \| - \| - \| - \| - \|
	\| 42.3729 \| 595 \| 4.4966 \| - \| - \| - \| - \| - \| - \|
	\| 42.6441 \| 596 \| 4.6267 \| - \| - \| - \| - \| - \| - \|
	\| 42.9153 \| 597 \| 4.5834 \| 5.1446 \| 0.0051 \| 0.0057 \| 0.0058 \| 0.0052 \| 0.0058 \|
	\| 40.1356 \| 598 \| 6.7009 \| - \| - \| - \| - \| - \| - \|
	\| 40.4068 \| 599 \| 12.2755 \| - \| - \| - \| - \| - \| - \|
	\| 40.6780 \| 600 \| 12.4465 \| 5.1447 \| 0.0052 \| 0.0057 \| 0.0059 \| 0.0052 \| 0.0059 \|

	* The bold row denotes the saved checkpoint.
	</details>

	### Framework Versions
	- Python: 3.10.12
	- Sentence Transformers: 3.0.1
	- Transformers: 4.41.2
	- PyTorch: 2.1.2+cu121
	- Accelerate: 0.31.0
	- Datasets: 2.19.1
	- 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",
	}
	```

	#### 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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