Add new SentenceTransformer model.

c3c5faa verified about 1 month ago

38.8 kB

	---
	base_model: microsoft/mdeberta-v3-base
	library_name: sentence-transformers
	pipeline_tag: sentence-similarity
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:498970
	- loss:BPRLoss
	widget:
	- source_sentence: meaning of the prefix em
	sentences:
	- Word Origin and History for em- Expand. from French assimilation of en- to following
	labial (see en- (1)). Also a prefix used to form verbs from adjectives and nouns.
	representing Latin ex- assimilated to following -m- (see ex-).
	- Rating Newest Oldest. 1 MO probably has the most insanely complex sales tax in
	the country. Not only is there a state level tax (4.225% for most items and 1.225%
	for grocery foods) but city and county level sales taxes. 2 The sales tax is
	set by county. Go to Missouri Sales Tax website and look up your county.
	- 'Prefixes: Un, Dis, Im, Mis. A prefix is placed at the beginning of a word to
	change its meaning. For example, the suffix re- means either again or back as
	in return, repeat or refurbish. The following 4 prefixes are easy to confuse because
	they all have a negative meaning. un-.'
	- source_sentence: is woolwich london safe
	sentences:
	- SE18 has four train stations Plumstead, Woolwich Arsenal and Woolwich Dockyard.
	Plumstead and Woolwich Arsenal are situated in Zone 4, Woolwich Dockyard in Zone
	3.Approximately just under 30 minutes to Charing Cross from all Stations. Trains
	are operated buy South-eastern. Train timetables are available at southeasternrailway.co.uk.here
	is no shortage of schools, libraries and colleges in SE18. A short walk from Plumstead
	station is Greenwich Community College offering a wide range of courses from cookery
	to languages. Notable schools include the newly re-built Foxfield Primary, Saint
	Pauls and Plumstead Mannor.
	- "In its heyday Woolwich was known better known as the home of Arsenal Football\
	\ Club, the first McDonalds in the UK and the base for the British Armyâ\x80\x99\
	s artillery. At present, it is safe to say the town would not be found in any\
	\ London travel guide."
	- Income and Qualifications. Car sales consultants often have compensation packages
	that include salary, commissions and bonuses. For example, Ford Motor sales reps
	earned an average base salary of $37,000, according to Glassdoor -- with the rest
	of their $54,600 in earnings comprised of commissions and benefits.
	- source_sentence: who is christopher kyle
	sentences:
	- Kyle Kulinski is an American Political Activist, progressive talk radio host,
	social democratic political commentator, and the co-founder of Justice Democrats.
	He is the host and producer of the YouTube show Secular Talk, an affiliate of
	The Young Turks network.
	- A passport card is valid for travel to and from Canada, Mexico, the Caribbean
	and Bermuda at land border crossings and sea ports-of-entry. It is not valid for
	air travel. It is valid for 10 years for adults and 5 years for minors under 16.
	A first passport book costs $135 for adults and $105 for minors under the age
	of 16. It costs $110 to renew. A first passport card costs $55 for adults and
	$40 for minors under the age of 16. It costs $30 to renew. The cost when applying
	for both is $165 for adults and $120 for minors.
	- Chris Kyle American Sniper. Christopher Scott Kyle was born and raised in Texas
	and was a United States Navy SEAL from 1999 to 2009. He is currently known as
	the most successful sniper in American military history. According to his book
	American Sniper, he had 160 confirmed kills (which was from 255 claimed kills).
	- source_sentence: do potato chips have sugar
	sentences:
	- Glycemic Index. White potatoes, whether you have them mashed, baked, as french
	fries or potato chips, have a high glycemic index, which means that their carbohydrates
	are quickly turned into sugar, which elevates your blood sugar levels after your
	meal.ating sweet potatoes in moderate amounts will help you keep your blood sugar
	levels in the healthy range even if you have diabetes. A medium sweet potato contains
	26 grams of carbohydrates, of which 3.8 grams are dietary fiber, while a cup of
	mashed sweet potatoes has 58 grams of carbohydrates and 8.2 grams of fiber.
	- So before tying that knot in the morning, consider what personality traits you
	are conveying through the color of your tie. Reds are a power color, symbolizing
	wealth, strength, and passion. Many cultures also find special meaning in the
	color red, such as good luck.
	- "Corn chips have a glycemic index score of 42, which is in the low range and indicates\
	\ they wonâ\x80\x99t spike your blood sugar. Of the total carbohydrates, 1.5 grams\
	\ are dietary fiber, 16 grams are complex carbs in the form of starches and only\
	\ 0.3 grams are sugar."
	- source_sentence: definition of stoop
	sentences:
	- Definition of stoop written for English Language Learners from the Merriam-Webster
	Learner's Dictionary with audio pronunciations, usage examples, and count/noncount
	noun labels. Learner's Dictionary mobile search
	- "Define stoop: to bend the body or a part of the body forward and downward sometimes\
	\ simultaneously bending the knees â\x80\x94 stoop in a sentence to bend the body\
	\ or a part of the body forward and downward sometimes simultaneously bending\
	\ the kneesâ\x80¦ See the full definition"
	- Blood plasma is the yellow liquid in which blood cells float. Plasma is made up
	of nutrients, electrolytes (salts), gases, non-protein hormones, waste, lipids,
	and proteins.These proteins are albumin, antibodies (also called immunoglobulins),
	clotting factors, and protein hormones.lood plasma is the yellow liquid in which
	blood cells float. Plasma is made up of nutrients, electrolytes (salts), gases,
	non-protein hormones, waste, lipids, and proteins.
	---

	# SentenceTransformer based on microsoft/mdeberta-v3-base

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

	## Model Details

	### Model Description
	- Model Type: Sentence Transformer
	- Base model: [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) <!-- at revision a0484667b22365f84929a935b5e50a51f71f159d -->
	- Maximum Sequence Length: 1024 tokens
	- Output Dimensionality: 768 tokens
	- Similarity Function: Cosine Similarity
	<!-- - Training Dataset: Unknown -->
	<!-- - Language: Unknown -->
	<!-- - License: Unknown -->

	### Model Sources

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

	### Full Model Architecture

	```
	SentenceTransformer(
	(0): Transformer({'max_seq_length': 1024, 'do_lower_case': False}) with Transformer model: DebertaV2Model
	(1): Pooling({'word_embedding_dimension': 768, '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("BlackBeenie/mdeberta-v3-base-msmarco-v3-bpr")
	# Run inference
	sentences = [
	'definition of stoop',
	'Define stoop: to bend the body or a part of the body forward and downward sometimes simultaneously bending the knees â\x80\x94 stoop in a sentence to bend the body or a part of the body forward and downward sometimes simultaneously bending the kneesâ\x80¦ See the full definition',
	"Definition of stoop written for English Language Learners from the Merriam-Webster Learner's Dictionary with audio pronunciations, usage examples, and count/noncount noun labels. Learner's Dictionary mobile search",
	]
	embeddings = model.encode(sentences)
	print(embeddings.shape)
	# [3, 768]

	# Get the similarity scores for the embeddings
	similarities = model.similarity(embeddings, embeddings)
	print(similarities.shape)
	# [3, 3]
	```

	<!--
	### Direct Usage (Transformers)

	<details><summary>Click to see the direct usage in Transformers</summary>

	</details>
	-->

	<!--
	### Downstream Usage (Sentence Transformers)

	You can finetune this model on your own dataset.

	<details><summary>Click to expand</summary>

	</details>
	-->

	<!--
	### Out-of-Scope Use

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

	<!--
	## 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: 498,970 training samples
	* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence_0 \| sentence_1 \| sentence_2 \|
	\|:--------\|:----------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------\|
	\| type \| string \| string \| string \|
	\| details \| <ul><li>min: 4 tokens</li><li>mean: 10.61 tokens</li><li>max: 40 tokens</li></ul> \| <ul><li>min: 17 tokens</li><li>mean: 96.41 tokens</li><li>max: 259 tokens</li></ul> \| <ul><li>min: 14 tokens</li><li>mean: 92.21 tokens</li><li>max: 250 tokens</li></ul> \|
	* Samples:
	\| sentence_0 \| sentence_1 \| sentence_2 \|
	\|:-------------------------------------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>how much does it cost to paint a interior house</code> \| <code>Interior House Painting Cost Factors. Generally, it will take a minimum of two gallons of paint to cover a room. At the highest end, paint will cost anywhere between $30 and $60 per gallon and come in three different finishes: flat, semi-gloss or high-gloss.Flat finishes are the least shiny and are best suited for areas requiring frequent cleaning.rovide a few details about your project and receive competitive quotes from local pros. The average national cost to paint a home interior is $1,671, with most homeowners spending between $966 and $2,426.</code> \| <code>How Much to Charge to Paint the Interior of a House (and how much not to charge) Let me give you an example - stay with me here. Imagine you drop all of your painting estimates by 20% to win more jobs. Maybe you'll close $10,000 in sales instead of $6,000 (because you had a better price - you landed an extra job)...</code> \|
	\| <code>when is s corp taxes due</code> \| <code>If you form a corporate entity for your small business, regardless of whether it's taxed as a C or S corporation, a tax return must be filed with the Internal Revenue Service on its due date each year. Corporate tax returns are always due on the 15th day of the third month following the close of the tax year. The actual day that the tax return filing deadline falls on, however, isn't the same for every corporation.</code> \| <code>In Summary. 1 S-corporations are pass-through entities. 2 Form 1120S is the form used for an S-corpâs annual tax return. 3 Shareholders do not have to pay self-employment tax on their share of an S-corpâs profits.</code> \|
	\| <code>what are disaccharides</code> \| <code>Disaccharides are formed when two monosaccharides are joined together and a molecule of water is removed, a process known as dehydration reaction. For example; milk sugar (lactose) is made from glucose and galactose whereas the sugar from sugar cane and sugar beets (sucrose) is made from glucose and fructose.altose, another notable disaccharide, is made up of two glucose molecules. The two monosaccharides are bonded via a dehydration reaction (also called a condensation reaction or dehydration synthesis) that leads to the loss of a molecule of water and formation of a glycosidic bond.</code> \| <code>No. Sugars and starches are types of carbohydrates,(ex: monosaccharides, disaccharides) Lipids are much different.o. Sugars and starches are types of carbohydrates,(ex: monosaccharides, disaccharides) Lipids are much different.</code> \|
	* Loss: <code>beir.losses.bpr_loss.BPRLoss</code>

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

	- `eval_strategy`: steps
	- `per_device_train_batch_size`: 32
	- `per_device_eval_batch_size`: 32
	- `num_train_epochs`: 15
	- `fp16`: True
	- `multi_dataset_batch_sampler`: round_robin

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

	- `overwrite_output_dir`: False
	- `do_predict`: False
	- `eval_strategy`: steps
	- `prediction_loss_only`: True
	- `per_device_train_batch_size`: 32
	- `per_device_eval_batch_size`: 32
	- `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`: 5e-05
	- `weight_decay`: 0.0
	- `adam_beta1`: 0.9
	- `adam_beta2`: 0.999
	- `adam_epsilon`: 1e-08
	- `max_grad_norm`: 1
	- `num_train_epochs`: 15
	- `max_steps`: -1
	- `lr_scheduler_type`: linear
	- `lr_scheduler_kwargs`: {}
	- `warmup_ratio`: 0.0
	- `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`: False
	- `hub_always_push`: False
	- `gradient_checkpointing`: False
	- `gradient_checkpointing_kwargs`: None
	- `include_inputs_for_metrics`: False
	- `eval_do_concat_batches`: True
	- `fp16_backend`: auto
	- `push_to_hub_model_id`: None
	- `push_to_hub_organization`: None
	- `mp_parameters`:
	- `auto_find_batch_size`: False
	- `full_determinism`: False
	- `torchdynamo`: None
	- `ray_scope`: last
	- `ddp_timeout`: 1800
	- `torch_compile`: False
	- `torch_compile_backend`: None
	- `torch_compile_mode`: None
	- `dispatch_batches`: None
	- `split_batches`: None
	- `include_tokens_per_second`: False
	- `include_num_input_tokens_seen`: False
	- `neftune_noise_alpha`: None
	- `optim_target_modules`: None
	- `batch_eval_metrics`: False
	- `eval_on_start`: False
	- `eval_use_gather_object`: False
	- `batch_sampler`: batch_sampler
	- `multi_dataset_batch_sampler`: round_robin

	</details>

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

	\| Epoch \| Step \| Training Loss \|
	\|:-------:\|:------:\|:-------------:\|
	\| 0.0321 \| 500 \| 7.0196 \|
	\| 0.0641 \| 1000 \| 2.0193 \|
	\| 0.0962 \| 1500 \| 1.4466 \|
	\| 0.1283 \| 2000 \| 1.1986 \|
	\| 0.1603 \| 2500 \| 1.0912 \|
	\| 0.1924 \| 3000 \| 1.0179 \|
	\| 0.2245 \| 3500 \| 0.9659 \|
	\| 0.2565 \| 4000 \| 0.9229 \|
	\| 0.2886 \| 4500 \| 0.9034 \|
	\| 0.3207 \| 5000 \| 0.871 \|
	\| 0.3527 \| 5500 \| 0.8474 \|
	\| 0.3848 \| 6000 \| 0.8247 \|
	\| 0.4169 \| 6500 \| 0.8377 \|
	\| 0.4489 \| 7000 \| 0.8119 \|
	\| 0.4810 \| 7500 \| 0.8042 \|
	\| 0.5131 \| 8000 \| 0.7831 \|
	\| 0.5451 \| 8500 \| 0.7667 \|
	\| 0.5772 \| 9000 \| 0.7653 \|
	\| 0.6092 \| 9500 \| 0.7502 \|
	\| 0.6413 \| 10000 \| 0.7615 \|
	\| 0.6734 \| 10500 \| 0.7435 \|
	\| 0.7054 \| 11000 \| 0.7346 \|
	\| 0.7375 \| 11500 \| 0.718 \|
	\| 0.7696 \| 12000 \| 0.711 \|
	\| 0.8016 \| 12500 \| 0.6963 \|
	\| 0.8337 \| 13000 \| 0.6969 \|
	\| 0.8658 \| 13500 \| 0.6937 \|
	\| 0.8978 \| 14000 \| 0.6721 \|
	\| 0.9299 \| 14500 \| 0.6902 \|
	\| 0.9620 \| 15000 \| 0.6783 \|
	\| 0.9940 \| 15500 \| 0.6669 \|
	\| 1.0 \| 15593 \| - \|
	\| 1.0261 \| 16000 \| 0.689 \|
	\| 1.0582 \| 16500 \| 0.6549 \|
	\| 1.0902 \| 17000 \| 0.6354 \|
	\| 1.1223 \| 17500 \| 0.6013 \|
	\| 1.1544 \| 18000 \| 0.6091 \|
	\| 1.1864 \| 18500 \| 0.5907 \|
	\| 1.2185 \| 19000 \| 0.5979 \|
	\| 1.2506 \| 19500 \| 0.5724 \|
	\| 1.2826 \| 20000 \| 0.5718 \|
	\| 1.3147 \| 20500 \| 0.5851 \|
	\| 1.3468 \| 21000 \| 0.5716 \|
	\| 1.3788 \| 21500 \| 0.5568 \|
	\| 1.4109 \| 22000 \| 0.5502 \|
	\| 1.4430 \| 22500 \| 0.5591 \|
	\| 1.4750 \| 23000 \| 0.5688 \|
	\| 1.5071 \| 23500 \| 0.5484 \|
	\| 1.5392 \| 24000 \| 0.531 \|
	\| 1.5712 \| 24500 \| 0.5445 \|
	\| 1.6033 \| 25000 \| 0.5269 \|
	\| 1.6353 \| 25500 \| 0.55 \|
	\| 1.6674 \| 26000 \| 0.537 \|
	\| 1.6995 \| 26500 \| 0.5259 \|
	\| 1.7315 \| 27000 \| 0.5153 \|
	\| 1.7636 \| 27500 \| 0.5184 \|
	\| 1.7957 \| 28000 \| 0.5154 \|
	\| 1.8277 \| 28500 \| 0.5279 \|
	\| 1.8598 \| 29000 \| 0.5267 \|
	\| 1.8919 \| 29500 \| 0.4938 \|
	\| 1.9239 \| 30000 \| 0.5088 \|
	\| 1.9560 \| 30500 \| 0.516 \|
	\| 1.9881 \| 31000 \| 0.4998 \|
	\| 2.0 \| 31186 \| - \|
	\| 2.0201 \| 31500 \| 0.5252 \|
	\| 2.0522 \| 32000 \| 0.4998 \|
	\| 2.0843 \| 32500 \| 0.484 \|
	\| 2.1163 \| 33000 \| 0.4612 \|
	\| 2.1484 \| 33500 \| 0.4617 \|
	\| 2.1805 \| 34000 \| 0.4441 \|
	\| 2.2125 \| 34500 \| 0.4653 \|
	\| 2.2446 \| 35000 \| 0.4592 \|
	\| 2.2767 \| 35500 \| 0.4347 \|
	\| 2.3087 \| 36000 \| 0.4557 \|
	\| 2.3408 \| 36500 \| 0.4401 \|
	\| 2.3729 \| 37000 \| 0.436 \|
	\| 2.4049 \| 37500 \| 0.4315 \|
	\| 2.4370 \| 38000 \| 0.4447 \|
	\| 2.4691 \| 38500 \| 0.4258 \|
	\| 2.5011 \| 39000 \| 0.4275 \|
	\| 2.5332 \| 39500 \| 0.4142 \|
	\| 2.5653 \| 40000 \| 0.434 \|
	\| 2.5973 \| 40500 \| 0.4222 \|
	\| 2.6294 \| 41000 \| 0.4284 \|
	\| 2.6615 \| 41500 \| 0.4187 \|
	\| 2.6935 \| 42000 \| 0.4156 \|
	\| 2.7256 \| 42500 \| 0.4054 \|
	\| 2.7576 \| 43000 \| 0.4182 \|
	\| 2.7897 \| 43500 \| 0.4142 \|
	\| 2.8218 \| 44000 \| 0.4152 \|
	\| 2.8538 \| 44500 \| 0.421 \|
	\| 2.8859 \| 45000 \| 0.403 \|
	\| 2.9180 \| 45500 \| 0.4003 \|
	\| 2.9500 \| 46000 \| 0.4032 \|
	\| 2.9821 \| 46500 \| 0.4072 \|
	\| 3.0 \| 46779 \| - \|
	\| 3.0142 \| 47000 \| 0.4137 \|
	\| 3.0462 \| 47500 \| 0.4151 \|
	\| 3.0783 \| 48000 \| 0.3959 \|
	\| 3.1104 \| 48500 \| 0.3808 \|
	\| 3.1424 \| 49000 \| 0.3701 \|
	\| 3.1745 \| 49500 \| 0.3716 \|
	\| 3.2066 \| 50000 \| 0.387 \|
	\| 3.2386 \| 50500 \| 0.3747 \|
	\| 3.2707 \| 51000 \| 0.3488 \|
	\| 3.3028 \| 51500 \| 0.3795 \|
	\| 3.3348 \| 52000 \| 0.3511 \|
	\| 3.3669 \| 52500 \| 0.3469 \|
	\| 3.3990 \| 53000 \| 0.3475 \|
	\| 3.4310 \| 53500 \| 0.3669 \|
	\| 3.4631 \| 54000 \| 0.3428 \|
	\| 3.4952 \| 54500 \| 0.3597 \|
	\| 3.5272 \| 55000 \| 0.3525 \|
	\| 3.5593 \| 55500 \| 0.3502 \|
	\| 3.5914 \| 56000 \| 0.3446 \|
	\| 3.6234 \| 56500 \| 0.3563 \|
	\| 3.6555 \| 57000 \| 0.34 \|
	\| 3.6876 \| 57500 \| 0.3385 \|
	\| 3.7196 \| 58000 \| 0.335 \|
	\| 3.7517 \| 58500 \| 0.3344 \|
	\| 3.7837 \| 59000 \| 0.3361 \|
	\| 3.8158 \| 59500 \| 0.3285 \|
	\| 3.8479 \| 60000 \| 0.3429 \|
	\| 3.8799 \| 60500 \| 0.3162 \|
	\| 3.9120 \| 61000 \| 0.3279 \|
	\| 3.9441 \| 61500 \| 0.3448 \|
	\| 3.9761 \| 62000 \| 0.322 \|
	\| 4.0 \| 62372 \| - \|
	\| 4.0082 \| 62500 \| 0.3356 \|
	\| 4.0403 \| 63000 \| 0.3416 \|
	\| 4.0723 \| 63500 \| 0.3195 \|
	\| 4.1044 \| 64000 \| 0.3033 \|
	\| 4.1365 \| 64500 \| 0.2957 \|
	\| 4.1685 \| 65000 \| 0.312 \|
	\| 4.2006 \| 65500 \| 0.3135 \|
	\| 4.2327 \| 66000 \| 0.3193 \|
	\| 4.2647 \| 66500 \| 0.2919 \|
	\| 4.2968 \| 67000 \| 0.3078 \|
	\| 4.3289 \| 67500 \| 0.302 \|
	\| 4.3609 \| 68000 \| 0.2973 \|
	\| 4.3930 \| 68500 \| 0.2725 \|
	\| 4.4251 \| 69000 \| 0.3013 \|
	\| 4.4571 \| 69500 \| 0.2936 \|
	\| 4.4892 \| 70000 \| 0.3009 \|
	\| 4.5213 \| 70500 \| 0.2941 \|
	\| 4.5533 \| 71000 \| 0.2957 \|
	\| 4.5854 \| 71500 \| 0.288 \|
	\| 4.6175 \| 72000 \| 0.3032 \|
	\| 4.6495 \| 72500 \| 0.2919 \|
	\| 4.6816 \| 73000 \| 0.2843 \|
	\| 4.7137 \| 73500 \| 0.2862 \|
	\| 4.7457 \| 74000 \| 0.2789 \|
	\| 4.7778 \| 74500 \| 0.2843 \|
	\| 4.8099 \| 75000 \| 0.2816 \|
	\| 4.8419 \| 75500 \| 0.2813 \|
	\| 4.8740 \| 76000 \| 0.2839 \|
	\| 4.9060 \| 76500 \| 0.2619 \|
	\| 4.9381 \| 77000 \| 0.2877 \|
	\| 4.9702 \| 77500 \| 0.2693 \|
	\| 5.0 \| 77965 \| - \|
	\| 5.0022 \| 78000 \| 0.2738 \|
	\| 5.0343 \| 78500 \| 0.286 \|
	\| 5.0664 \| 79000 \| 0.2754 \|
	\| 5.0984 \| 79500 \| 0.2561 \|
	\| 5.1305 \| 80000 \| 0.2498 \|
	\| 5.1626 \| 80500 \| 0.2563 \|
	\| 5.1946 \| 81000 \| 0.2618 \|
	\| 5.2267 \| 81500 \| 0.265 \|
	\| 5.2588 \| 82000 \| 0.245 \|
	\| 5.2908 \| 82500 \| 0.2551 \|
	\| 5.3229 \| 83000 \| 0.2653 \|
	\| 5.3550 \| 83500 \| 0.2453 \|
	\| 5.3870 \| 84000 \| 0.24 \|
	\| 5.4191 \| 84500 \| 0.2478 \|
	\| 5.4512 \| 85000 \| 0.2444 \|
	\| 5.4832 \| 85500 \| 0.2464 \|
	\| 5.5153 \| 86000 \| 0.2327 \|
	\| 5.5474 \| 86500 \| 0.2376 \|
	\| 5.5794 \| 87000 \| 0.2469 \|
	\| 5.6115 \| 87500 \| 0.2488 \|
	\| 5.6436 \| 88000 \| 0.2467 \|
	\| 5.6756 \| 88500 \| 0.2409 \|
	\| 5.7077 \| 89000 \| 0.2287 \|
	\| 5.7398 \| 89500 \| 0.2288 \|
	\| 5.7718 \| 90000 \| 0.2399 \|
	\| 5.8039 \| 90500 \| 0.2341 \|
	\| 5.8360 \| 91000 \| 0.2352 \|
	\| 5.8680 \| 91500 \| 0.2196 \|
	\| 5.9001 \| 92000 \| 0.2196 \|
	\| 5.9321 \| 92500 \| 0.2246 \|
	\| 5.9642 \| 93000 \| 0.2411 \|
	\| 5.9963 \| 93500 \| 0.2279 \|
	\| 6.0 \| 93558 \| - \|
	\| 6.0283 \| 94000 \| 0.2489 \|
	\| 6.0604 \| 94500 \| 0.2339 \|
	\| 6.0925 \| 95000 \| 0.224 \|
	\| 6.1245 \| 95500 \| 0.209 \|
	\| 6.1566 \| 96000 \| 0.2262 \|
	\| 6.1887 \| 96500 \| 0.2221 \|
	\| 6.2207 \| 97000 \| 0.214 \|
	\| 6.2528 \| 97500 \| 0.21 \|
	\| 6.2849 \| 98000 \| 0.2072 \|
	\| 6.3169 \| 98500 \| 0.2204 \|
	\| 6.3490 \| 99000 \| 0.2041 \|
	\| 6.3811 \| 99500 \| 0.2067 \|
	\| 6.4131 \| 100000 \| 0.2102 \|
	\| 6.4452 \| 100500 \| 0.2031 \|
	\| 6.4773 \| 101000 \| 0.2107 \|
	\| 6.5093 \| 101500 \| 0.2009 \|
	\| 6.5414 \| 102000 \| 0.2057 \|
	\| 6.5735 \| 102500 \| 0.1979 \|
	\| 6.6055 \| 103000 \| 0.1994 \|
	\| 6.6376 \| 103500 \| 0.2065 \|
	\| 6.6697 \| 104000 \| 0.1958 \|
	\| 6.7017 \| 104500 \| 0.2074 \|
	\| 6.7338 \| 105000 \| 0.1941 \|
	\| 6.7659 \| 105500 \| 0.2035 \|
	\| 6.7979 \| 106000 \| 0.2003 \|
	\| 6.8300 \| 106500 \| 0.2083 \|
	\| 6.8621 \| 107000 \| 0.1921 \|
	\| 6.8941 \| 107500 \| 0.1893 \|
	\| 6.9262 \| 108000 \| 0.2014 \|
	\| 6.9583 \| 108500 \| 0.192 \|
	\| 6.9903 \| 109000 \| 0.1921 \|
	\| 7.0 \| 109151 \| - \|
	\| 7.0224 \| 109500 \| 0.2141 \|
	\| 7.0544 \| 110000 \| 0.1868 \|
	\| 7.0865 \| 110500 \| 0.1815 \|
	\| 7.1186 \| 111000 \| 0.1793 \|
	\| 7.1506 \| 111500 \| 0.1812 \|
	\| 7.1827 \| 112000 \| 0.1853 \|
	\| 7.2148 \| 112500 \| 0.1922 \|
	\| 7.2468 \| 113000 \| 0.179 \|
	\| 7.2789 \| 113500 \| 0.1707 \|
	\| 7.3110 \| 114000 \| 0.1829 \|
	\| 7.3430 \| 114500 \| 0.1743 \|
	\| 7.3751 \| 115000 \| 0.1787 \|
	\| 7.4072 \| 115500 \| 0.1815 \|
	\| 7.4392 \| 116000 \| 0.1776 \|
	\| 7.4713 \| 116500 \| 0.1773 \|
	\| 7.5034 \| 117000 \| 0.1753 \|
	\| 7.5354 \| 117500 \| 0.1816 \|
	\| 7.5675 \| 118000 \| 0.1795 \|
	\| 7.5996 \| 118500 \| 0.178 \|
	\| 7.6316 \| 119000 \| 0.177 \|
	\| 7.6637 \| 119500 \| 0.175 \|
	\| 7.6958 \| 120000 \| 0.1701 \|
	\| 7.7278 \| 120500 \| 0.1686 \|
	\| 7.7599 \| 121000 \| 0.1727 \|
	\| 7.7920 \| 121500 \| 0.1733 \|
	\| 7.8240 \| 122000 \| 0.1707 \|
	\| 7.8561 \| 122500 \| 0.1729 \|
	\| 7.8882 \| 123000 \| 0.1569 \|
	\| 7.9202 \| 123500 \| 0.1657 \|
	\| 7.9523 \| 124000 \| 0.1773 \|
	\| 7.9844 \| 124500 \| 0.1625 \|
	\| 8.0 \| 124744 \| - \|
	\| 8.0164 \| 125000 \| 0.1824 \|
	\| 8.0485 \| 125500 \| 0.1852 \|
	\| 8.0805 \| 126000 \| 0.1701 \|
	\| 8.1126 \| 126500 \| 0.1573 \|
	\| 8.1447 \| 127000 \| 0.1614 \|
	\| 8.1767 \| 127500 \| 0.1624 \|
	\| 8.2088 \| 128000 \| 0.1575 \|
	\| 8.2409 \| 128500 \| 0.1481 \|
	\| 8.2729 \| 129000 \| 0.1537 \|
	\| 8.3050 \| 129500 \| 0.1616 \|
	\| 8.3371 \| 130000 \| 0.1544 \|
	\| 8.3691 \| 130500 \| 0.1511 \|
	\| 8.4012 \| 131000 \| 0.1569 \|
	\| 8.4333 \| 131500 \| 0.1535 \|
	\| 8.4653 \| 132000 \| 0.1489 \|
	\| 8.4974 \| 132500 \| 0.1593 \|
	\| 8.5295 \| 133000 \| 0.1552 \|
	\| 8.5615 \| 133500 \| 0.1578 \|
	\| 8.5936 \| 134000 \| 0.1501 \|
	\| 8.6257 \| 134500 \| 0.156 \|
	\| 8.6577 \| 135000 \| 0.1455 \|
	\| 8.6898 \| 135500 \| 0.1524 \|
	\| 8.7219 \| 136000 \| 0.1344 \|
	\| 8.7539 \| 136500 \| 0.1513 \|
	\| 8.7860 \| 137000 \| 0.141 \|
	\| 8.8181 \| 137500 \| 0.1518 \|
	\| 8.8501 \| 138000 \| 0.1468 \|
	\| 8.8822 \| 138500 \| 0.1416 \|
	\| 8.9143 \| 139000 \| 0.1434 \|
	\| 8.9463 \| 139500 \| 0.1495 \|
	\| 8.9784 \| 140000 \| 0.1364 \|
	\| 9.0 \| 140337 \| - \|
	\| 9.0105 \| 140500 \| 0.1507 \|
	\| 9.0425 \| 141000 \| 0.1496 \|
	\| 9.0746 \| 141500 \| 0.1475 \|
	\| 9.1067 \| 142000 \| 0.1348 \|
	\| 9.1387 \| 142500 \| 0.1282 \|
	\| 9.1708 \| 143000 \| 0.1362 \|
	\| 9.2028 \| 143500 \| 0.1364 \|
	\| 9.2349 \| 144000 \| 0.1385 \|
	\| 9.2670 \| 144500 \| 0.1309 \|
	\| 9.2990 \| 145000 \| 0.1324 \|
	\| 9.3311 \| 145500 \| 0.1354 \|
	\| 9.3632 \| 146000 \| 0.1283 \|
	\| 9.3952 \| 146500 \| 0.1239 \|
	\| 9.4273 \| 147000 \| 0.126 \|
	\| 9.4594 \| 147500 \| 0.1232 \|
	\| 9.4914 \| 148000 \| 0.1269 \|
	\| 9.5235 \| 148500 \| 0.1269 \|
	\| 9.5556 \| 149000 \| 0.1299 \|
	\| 9.5876 \| 149500 \| 0.1367 \|
	\| 9.6197 \| 150000 \| 0.1354 \|
	\| 9.6518 \| 150500 \| 0.1239 \|
	\| 9.6838 \| 151000 \| 0.1311 \|
	\| 9.7159 \| 151500 \| 0.1235 \|
	\| 9.7480 \| 152000 \| 0.129 \|
	\| 9.7800 \| 152500 \| 0.1244 \|
	\| 9.8121 \| 153000 \| 0.1201 \|
	\| 9.8442 \| 153500 \| 0.1332 \|
	\| 9.8762 \| 154000 \| 0.1189 \|
	\| 9.9083 \| 154500 \| 0.1221 \|
	\| 9.9404 \| 155000 \| 0.1228 \|
	\| 9.9724 \| 155500 \| 0.1173 \|
	\| 10.0 \| 155930 \| - \|
	\| 10.0045 \| 156000 \| 0.1347 \|
	\| 10.0366 \| 156500 \| 0.1384 \|
	\| 10.0686 \| 157000 \| 0.1402 \|
	\| 10.1007 \| 157500 \| 0.1161 \|
	\| 10.1328 \| 158000 \| 0.1141 \|
	\| 10.1648 \| 158500 \| 0.1199 \|
	\| 10.1969 \| 159000 \| 0.1328 \|
	\| 10.2289 \| 159500 \| 0.1263 \|
	\| 10.2610 \| 160000 \| 0.1143 \|
	\| 10.2931 \| 160500 \| 0.1207 \|
	\| 10.3251 \| 161000 \| 0.1119 \|
	\| 10.3572 \| 161500 \| 0.114 \|
	\| 10.3893 \| 162000 \| 0.114 \|
	\| 10.4213 \| 162500 \| 0.1118 \|
	\| 10.4534 \| 163000 \| 0.1228 \|
	\| 10.4855 \| 163500 \| 0.1209 \|
	\| 10.5175 \| 164000 \| 0.1153 \|
	\| 10.5496 \| 164500 \| 0.118 \|
	\| 10.5817 \| 165000 \| 0.1118 \|
	\| 10.6137 \| 165500 \| 0.1206 \|
	\| 10.6458 \| 166000 \| 0.1108 \|
	\| 10.6779 \| 166500 \| 0.1084 \|
	\| 10.7099 \| 167000 \| 0.1127 \|
	\| 10.7420 \| 167500 \| 0.1001 \|
	\| 10.7741 \| 168000 \| 0.1073 \|
	\| 10.8061 \| 168500 \| 0.1174 \|
	\| 10.8382 \| 169000 \| 0.1143 \|
	\| 10.8703 \| 169500 \| 0.1158 \|
	\| 10.9023 \| 170000 \| 0.1099 \|
	\| 10.9344 \| 170500 \| 0.0998 \|
	\| 10.9665 \| 171000 \| 0.1009 \|
	\| 10.9985 \| 171500 \| 0.1167 \|
	\| 11.0 \| 171523 \| - \|
	\| 11.0306 \| 172000 \| 0.1161 \|
	\| 11.0627 \| 172500 \| 0.1126 \|
	\| 11.0947 \| 173000 \| 0.1046 \|
	\| 11.1268 \| 173500 \| 0.1054 \|
	\| 11.1589 \| 174000 \| 0.1063 \|
	\| 11.1909 \| 174500 \| 0.1136 \|
	\| 11.2230 \| 175000 \| 0.108 \|
	\| 11.2551 \| 175500 \| 0.1014 \|
	\| 11.2871 \| 176000 \| 0.1036 \|
	\| 11.3192 \| 176500 \| 0.1043 \|
	\| 11.3512 \| 177000 \| 0.0973 \|
	\| 11.3833 \| 177500 \| 0.0934 \|
	\| 11.4154 \| 178000 \| 0.095 \|
	\| 11.4474 \| 178500 \| 0.1032 \|
	\| 11.4795 \| 179000 \| 0.1089 \|
	\| 11.5116 \| 179500 \| 0.098 \|
	\| 11.5436 \| 180000 \| 0.099 \|
	\| 11.5757 \| 180500 \| 0.1007 \|
	\| 11.6078 \| 181000 \| 0.096 \|
	\| 11.6398 \| 181500 \| 0.0986 \|
	\| 11.6719 \| 182000 \| 0.1033 \|
	\| 11.7040 \| 182500 \| 0.0899 \|
	\| 11.7360 \| 183000 \| 0.0946 \|
	\| 11.7681 \| 183500 \| 0.0943 \|
	\| 11.8002 \| 184000 \| 0.0954 \|
	\| 11.8322 \| 184500 \| 0.0955 \|
	\| 11.8643 \| 185000 \| 0.0924 \|
	\| 11.8964 \| 185500 \| 0.0847 \|
	\| 11.9284 \| 186000 \| 0.0914 \|
	\| 11.9605 \| 186500 \| 0.0918 \|
	\| 11.9926 \| 187000 \| 0.099 \|
	\| 12.0 \| 187116 \| - \|
	\| 12.0246 \| 187500 \| 0.1029 \|
	\| 12.0567 \| 188000 \| 0.1032 \|
	\| 12.0888 \| 188500 \| 0.0864 \|
	\| 12.1208 \| 189000 \| 0.0921 \|
	\| 12.1529 \| 189500 \| 0.0959 \|
	\| 12.1850 \| 190000 \| 0.0846 \|
	\| 12.2170 \| 190500 \| 0.0924 \|
	\| 12.2491 \| 191000 \| 0.0897 \|
	\| 12.2812 \| 191500 \| 0.0858 \|
	\| 12.3132 \| 192000 \| 0.0851 \|
	\| 12.3453 \| 192500 \| 0.0925 \|
	\| 12.3773 \| 193000 \| 0.0963 \|
	\| 12.4094 \| 193500 \| 0.0867 \|
	\| 12.4415 \| 194000 \| 0.0929 \|
	\| 12.4735 \| 194500 \| 0.0904 \|
	\| 12.5056 \| 195000 \| 0.0854 \|
	\| 12.5377 \| 195500 \| 0.0876 \|
	\| 12.5697 \| 196000 \| 0.0899 \|
	\| 12.6018 \| 196500 \| 0.09 \|
	\| 12.6339 \| 197000 \| 0.0921 \|
	\| 12.6659 \| 197500 \| 0.0829 \|
	\| 12.6980 \| 198000 \| 0.0952 \|
	\| 12.7301 \| 198500 \| 0.087 \|
	\| 12.7621 \| 199000 \| 0.086 \|
	\| 12.7942 \| 199500 \| 0.0836 \|
	\| 12.8263 \| 200000 \| 0.0845 \|
	\| 12.8583 \| 200500 \| 0.0808 \|
	\| 12.8904 \| 201000 \| 0.0771 \|
	\| 12.9225 \| 201500 \| 0.0815 \|
	\| 12.9545 \| 202000 \| 0.0901 \|
	\| 12.9866 \| 202500 \| 0.0871 \|
	\| 13.0 \| 202709 \| - \|
	\| 13.0187 \| 203000 \| 0.088 \|
	\| 13.0507 \| 203500 \| 0.089 \|
	\| 13.0828 \| 204000 \| 0.081 \|
	\| 13.1149 \| 204500 \| 0.0739 \|
	\| 13.1469 \| 205000 \| 0.0825 \|
	\| 13.1790 \| 205500 \| 0.0855 \|
	\| 13.2111 \| 206000 \| 0.0788 \|
	\| 13.2431 \| 206500 \| 0.0769 \|
	\| 13.2752 \| 207000 \| 0.0706 \|
	\| 13.3073 \| 207500 \| 0.0821 \|
	\| 13.3393 \| 208000 \| 0.0752 \|
	\| 13.3714 \| 208500 \| 0.0746 \|
	\| 13.4035 \| 209000 \| 0.066 \|
	\| 13.4355 \| 209500 \| 0.0779 \|
	\| 13.4676 \| 210000 \| 0.0755 \|
	\| 13.4996 \| 210500 \| 0.0829 \|
	\| 13.5317 \| 211000 \| 0.0731 \|
	\| 13.5638 \| 211500 \| 0.086 \|
	\| 13.5958 \| 212000 \| 0.078 \|
	\| 13.6279 \| 212500 \| 0.0724 \|
	\| 13.6600 \| 213000 \| 0.0696 \|
	\| 13.6920 \| 213500 \| 0.0789 \|
	\| 13.7241 \| 214000 \| 0.0657 \|
	\| 13.7562 \| 214500 \| 0.0767 \|
	\| 13.7882 \| 215000 \| 0.0728 \|
	\| 13.8203 \| 215500 \| 0.071 \|
	\| 13.8524 \| 216000 \| 0.0733 \|
	\| 13.8844 \| 216500 \| 0.0621 \|
	\| 13.9165 \| 217000 \| 0.0677 \|
	\| 13.9486 \| 217500 \| 0.0761 \|
	\| 13.9806 \| 218000 \| 0.0669 \|
	\| 14.0 \| 218302 \| - \|
	\| 14.0127 \| 218500 \| 0.0848 \|
	\| 14.0448 \| 219000 \| 0.0647 \|
	\| 14.0768 \| 219500 \| 0.0717 \|
	\| 14.1089 \| 220000 \| 0.0653 \|
	\| 14.1410 \| 220500 \| 0.0615 \|
	\| 14.1730 \| 221000 \| 0.0711 \|
	\| 14.2051 \| 221500 \| 0.0674 \|
	\| 14.2372 \| 222000 \| 0.0674 \|
	\| 14.2692 \| 222500 \| 0.0657 \|
	\| 14.3013 \| 223000 \| 0.0727 \|
	\| 14.3334 \| 223500 \| 0.0709 \|
	\| 14.3654 \| 224000 \| 0.061 \|
	\| 14.3975 \| 224500 \| 0.0638 \|
	\| 14.4296 \| 225000 \| 0.0704 \|
	\| 14.4616 \| 225500 \| 0.0623 \|
	\| 14.4937 \| 226000 \| 0.065 \|
	\| 14.5257 \| 226500 \| 0.0657 \|
	\| 14.5578 \| 227000 \| 0.0634 \|
	\| 14.5899 \| 227500 \| 0.0555 \|
	\| 14.6219 \| 228000 \| 0.0647 \|
	\| 14.6540 \| 228500 \| 0.0616 \|
	\| 14.6861 \| 229000 \| 0.0645 \|
	\| 14.7181 \| 229500 \| 0.0649 \|
	\| 14.7502 \| 230000 \| 0.0612 \|
	\| 14.7823 \| 230500 \| 0.0646 \|
	\| 14.8143 \| 231000 \| 0.0571 \|
	\| 14.8464 \| 231500 \| 0.0561 \|
	\| 14.8785 \| 232000 \| 0.0598 \|
	\| 14.9105 \| 232500 \| 0.0634 \|
	\| 14.9426 \| 233000 \| 0.0657 \|
	\| 14.9747 \| 233500 \| 0.0644 \|
	\| 15.0 \| 233895 \| - \|

	</details>

	### Framework Versions
	- Python: 3.10.12
	- Sentence Transformers: 3.1.1
	- Transformers: 4.44.2
	- PyTorch: 2.4.1+cu121
	- Accelerate: 0.34.2
	- Datasets: 3.0.0
	- Tokenizers: 0.19.1

	## Citation

	### BibTeX

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

	<!--
	## Glossary

	Clearly define terms in order to be accessible across audiences.
	-->

	<!--
	## Model Card Authors

	Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.
	-->

	<!--
	## Model Card Contact

	Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.
	-->