Add new SentenceTransformer model

889f18a verified 6 months ago

25.5 kB

	---
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:498970
	- loss:BPRLoss
	base_model: nomic-ai/nomic-embed-text-v2-moe
	widget:
	- source_sentence: what was the start treaty 2010
	sentences:
	- "Strategic Offensive Reductions: The Treaty between the United States of America\
	\ and the Russian Federation on Measures for the Further Reduction and Limitation\
	\ of Strategic Offensive Arms, also known as the New START Treaty, entered into\
	\ force on February 5, 2011.nder the Treaty, the United States and Russia must\
	\ meet the Treatyâ\x80\x99s central limits on strategic arms by February 5, 2018;\
	\ seven years from the date the Treaty entered into force. Each Party has the\
	\ flexibility to determine for itself the structure of its strategic forces within\
	\ the aggregate limits of the Treaty."
	- 'Nuclear pharmacy practice: hour-for-hour credit in a licensed nuclear pharmacy
	or health care facility approved by state or federal agencies to handle radioactive
	materials, to a maximum of 4,000 hours.'
	- 'Signed: 18 June 1979. Entered into Force: Never entered into force; superseded
	by the START I Treaty in 1991. Duration: Until 31 December 1985; unless the Treaty
	is replaced earlier by an agreement further limiting strategic offensive arms.
	Parties: Soviet Union and United States.'
	- source_sentence: is pez a word
	sentences:
	- From dispensers to candy, there's a PEZ for anyone and everyone. Look for these
	PEZ products at your local retailer.rom dispensers to candy, there's a PEZ for
	anyone and everyone. Look for these PEZ products at your local retailer.
	- PEZ was first introduced in 1927 in Vienna, Austria as a breath mint for adults!
	The word PEZ was created using the first, middle and last letter in the German
	word for peppermint P feff E rmin Z.
	- Boonville is a city in Boon Township, Warrick County, Indiana, United States.
	The population was 6,246 at the 2010 census.The city is the county seat of Warrick
	County.oonville was founded in 1818 and named for Jesse Boon, father of Ratliff
	Boon. A post office has been in operation at Boonville since 1820. Boonville was
	incorporated in 1858.
	- source_sentence: us budget deficit by president
	sentences:
	- "By 2022, the government will once again be running trillion-dollar deficits,\
	\ the report said. â\x80\x9CWe still have a lot of work to do,â\x80\x9D said House\
	\ Budget Committee Chairman Paul Ryan. Lawmakers can take some credit for the\
	\ short-term improvement in the budget outlook, the report showed, though the\
	\ strengthening economy helps as well."
	- However, when they are 3 to 4 months old, they become susceptible to the disease,
	so all calves should be vaccinated for blackleg by 4 months of age. A revaccination
	3 to 6 weeks later according to product label directions is necessary to provide
	the best protec-tion.lackleg seldom affects cattle older than 2 years of age,
	most likely due to immunity induced by vaccines or natural exposure. However,
	sporadic cases do occur in cattle older than 2 years and are often associated
	with the reuse of needles for multiple injections.
	- According to this method, Barack Obama's budget is projected to run a deficit
	of $7.3 trillion over his eight years, making him the president with the largest
	budget deficit. George W. Bush is second, with a deficit of $3.29 trillion over
	his eight years.
	- source_sentence: what is a sixth sense
	sentences:
	- 1 Extrasensory perception (ESP), commonly called the sixth sense. 2 Equilibrioception
	(sense of balance) and proprioception (sense of body position), commonly accepted
	physiological senses in addition to the usually considered five senses.
	- 'Glaze or glazing may refer to: 1 Glaze (metallurgy), a layer of compacted sintered
	oxide formed on some metals. 2 Glaze (cooking technique), a coating of a glossy,
	often sweet, mixture applied to food. Glaze (painting technique), a layer of
	paint, thinned with a medium, so as to become somewhat transparent.'
	- Definition of Proprioception. The term proprioception is used to describe the
	sensory information that contributes to the sense of position of self and movement.
	Sir Charles Bell named the sixth sense as the sense of the positions and actions
	of the limbs (McCloskey 1978).eceptors of Proprioception. It is well recognized
	that joint movements activate receptors in the joint, skin and muscle. In turn,
	any of these receptors may play a role in the perception and control of limb movement
	and joint angle.
	- source_sentence: what services are offered by adult day care
	sentences:
	- The Met Life Market survey of 2008 on adult day services states the average cost
	for adult day care services is $64 per day. There has been an increase of 5% in
	these services in the past year.
	- Consumer Guide to Long Term Care. Adult Day Care. Adult day care is a planned
	program offered in a group setting that provides services that improve or maintain
	health or functioning, and social activities for seniors and persons with disabilities.
	- As nouns the difference between tackle and guard is that tackle is (nautical)
	a system of ropes and blocks used to increase the force applied to the free end
	of the rope while guard is a person who, or thing that, protects or watches over
	something. As verbs the difference between tackle and guard
	pipeline_tag: sentence-similarity
	library_name: sentence-transformers
	---

	# SentenceTransformer based on nomic-ai/nomic-embed-text-v2-moe

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nomic-ai/nomic-embed-text-v2-moe](https://huggingface.co/nomic-ai/nomic-embed-text-v2-moe). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

	## Model Details

	### Model Description
	- Model Type: Sentence Transformer
	- Base model: [nomic-ai/nomic-embed-text-v2-moe](https://huggingface.co/nomic-ai/nomic-embed-text-v2-moe) <!-- at revision f6a8873b415144a69ffc529ec1e234d1e00ee765 -->
	- Maximum Sequence Length: 8192 tokens
	- Output Dimensionality: 768 dimensions
	- Similarity Function: Cosine Similarity
	<!-- - Training Dataset: Unknown -->
	<!-- - Language: Unknown -->
	<!-- - License: Unknown -->

	### Model Sources

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

	### Full Model Architecture

	```
	SentenceTransformer(
	(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: NomicBertModel
	(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/nomic-embed-text-v2-moe-msmarco-bpr")
	# Run inference
	sentences = [
	'what services are offered by adult day care',
	'Consumer Guide to Long Term Care. Adult Day Care. Adult day care is a planned program offered in a group setting that provides services that improve or maintain health or functioning, and social activities for seniors and persons with disabilities.',
	'The Met Life Market survey of 2008 on adult day services states the average cost for adult day care services is $64 per day. There has been an increase of 5% in these services in the past year.',
	]
	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: 9.75 tokens</li><li>max: 24 tokens</li></ul> \| <ul><li>min: 24 tokens</li><li>mean: 89.23 tokens</li><li>max: 241 tokens</li></ul> \| <ul><li>min: 20 tokens</li><li>mean: 86.66 tokens</li><li>max: 280 tokens</li></ul> \|
	* Samples:
	\| sentence_0 \| sentence_1 \| sentence_2 \|
	\|:------------------------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>what the history of bluetooth</code> \| <code>When asked about the name Bluetooth, I explained that Bluetooth was borrowed from the 10th century, second King of Denmark, King Harald Bluetooth; who was famous for uniting Scandinavia just as we intended to unite the PC and cellular industries with a short-range wireless link.</code> \| <code>Technology: 1 How secure is a Bluetooth network? 2 What is Frequency-Hopping Spread Spectrum (FHSS)? 3 Will other RF (Radio Frequency) devices interfere with Bluetooth Devices? 4 Will Bluetooth and Wireless LAN (WLAN) interfere with each other? 5 What is the data throughput speed of a Bluetooth connection? 6 What is the range of Bluetooth 7 ... What kind of ...</code> \|
	\| <code>how thin can a concrete slab be</code> \| <code>Another issue that must be addressed is the added weight of the thin-slab. Poured gypsum thin-slabs typically add 13 to 15 pounds per square foot to the dead loading of a floor structure. Standard weight concrete thin slabs add about 18 pounds per square foot (at 1.5 thickness).</code> \| <code>Find the Area in square feet: We will use a concrete slab pour for our example. Letâs say that we need to figure out the yardage for a slab that will be 15 feet long by 10 feet wide and 4 inches thick. First we find the area by multiplying the length times the width. 1 15 feet X 10 feet = 150 square feet.</code> \|
	\| <code>how long to cook eggs to hard boil</code> \| <code>This method works best if the eggs are in a single layer, but you can double them up as well, you'll just need to add more time to the steaming time. 3 Set your timer for 6 minutes for soft boiled, 10 minutes for hard boiled with a still translucent and bright yolk, or 12-15 minutes for cooked-through hard boiled.</code> \| <code>Hard-Steamed Eggs. Fill a pot that can comfortably hold your steamer with the lid on with 1 to 2 inches of water. Bring to a rolling boil, 212 degrees Fahrenheit. Place your eggs in a metal steamer, and lower the basket into the pot. The eggs should sit above the boiling water. Cover and cook for 12 minutes. Hard-steamed eggs, like hard-boiled eggs, are eggs that are cooked until the egg yolk is fully set and has turned to a chalky texture.</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`: 5
	- `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`: 5
	- `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`: None
	- `hub_always_push`: False
	- `gradient_checkpointing`: False
	- `gradient_checkpointing_kwargs`: None
	- `include_inputs_for_metrics`: False
	- `include_for_metrics`: []
	- `eval_do_concat_batches`: True
	- `fp16_backend`: auto
	- `push_to_hub_model_id`: None
	- `push_to_hub_organization`: None
	- `mp_parameters`:
	- `auto_find_batch_size`: False
	- `full_determinism`: False
	- `torchdynamo`: None
	- `ray_scope`: last
	- `ddp_timeout`: 1800
	- `torch_compile`: False
	- `torch_compile_backend`: None
	- `torch_compile_mode`: None
	- `dispatch_batches`: None
	- `split_batches`: None
	- `include_tokens_per_second`: False
	- `include_num_input_tokens_seen`: False
	- `neftune_noise_alpha`: None
	- `optim_target_modules`: None
	- `batch_eval_metrics`: False
	- `eval_on_start`: False
	- `use_liger_kernel`: False
	- `eval_use_gather_object`: False
	- `average_tokens_across_devices`: False
	- `prompts`: None
	- `batch_sampler`: batch_sampler
	- `multi_dataset_batch_sampler`: round_robin

	</details>

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

	\| Epoch \| Step \| Training Loss \|
	\|:------:\|:-----:\|:-------------:\|
	\| 0.0321 \| 500 \| 0.3396 \|
	\| 0.0641 \| 1000 \| 0.2094 \|
	\| 0.0962 \| 1500 \| 0.21 \|
	\| 0.1283 \| 2000 \| 0.1955 \|
	\| 0.1603 \| 2500 \| 0.1989 \|
	\| 0.1924 \| 3000 \| 0.1851 \|
	\| 0.2245 \| 3500 \| 0.1839 \|
	\| 0.2565 \| 4000 \| 0.1859 \|
	\| 0.2886 \| 4500 \| 0.1892 \|
	\| 0.3207 \| 5000 \| 0.1865 \|
	\| 0.3527 \| 5500 \| 0.1773 \|
	\| 0.3848 \| 6000 \| 0.1796 \|
	\| 0.4169 \| 6500 \| 0.1929 \|
	\| 0.4489 \| 7000 \| 0.1829 \|
	\| 0.4810 \| 7500 \| 0.172 \|
	\| 0.5131 \| 8000 \| 0.1792 \|
	\| 0.5451 \| 8500 \| 0.1747 \|
	\| 0.5772 \| 9000 \| 0.1802 \|
	\| 0.6092 \| 9500 \| 0.1856 \|
	\| 0.6413 \| 10000 \| 0.1751 \|
	\| 0.6734 \| 10500 \| 0.173 \|
	\| 0.7054 \| 11000 \| 0.1774 \|
	\| 0.7375 \| 11500 \| 0.1722 \|
	\| 0.7696 \| 12000 \| 0.1825 \|
	\| 0.8016 \| 12500 \| 0.1714 \|
	\| 0.8337 \| 13000 \| 0.1732 \|
	\| 0.8658 \| 13500 \| 0.167 \|
	\| 0.8978 \| 14000 \| 0.1792 \|
	\| 0.9299 \| 14500 \| 0.1697 \|
	\| 0.9620 \| 15000 \| 0.1682 \|
	\| 0.9940 \| 15500 \| 0.1764 \|
	\| 1.0 \| 15593 \| - \|
	\| 1.0261 \| 16000 \| 0.0875 \|
	\| 1.0582 \| 16500 \| 0.0798 \|
	\| 1.0902 \| 17000 \| 0.0764 \|
	\| 1.1223 \| 17500 \| 0.0783 \|
	\| 1.1544 \| 18000 \| 0.0759 \|
	\| 1.1864 \| 18500 \| 0.0834 \|
	\| 1.2185 \| 19000 \| 0.082 \|
	\| 1.2506 \| 19500 \| 0.0827 \|
	\| 1.2826 \| 20000 \| 0.0876 \|
	\| 1.3147 \| 20500 \| 0.0819 \|
	\| 1.3468 \| 21000 \| 0.0841 \|
	\| 1.3788 \| 21500 \| 0.0815 \|
	\| 1.4109 \| 22000 \| 0.0819 \|
	\| 1.4430 \| 22500 \| 0.0883 \|
	\| 1.4750 \| 23000 \| 0.0826 \|
	\| 1.5071 \| 23500 \| 0.0837 \|
	\| 1.5392 \| 24000 \| 0.086 \|
	\| 1.5712 \| 24500 \| 0.0806 \|
	\| 1.6033 \| 25000 \| 0.0918 \|
	\| 1.6353 \| 25500 \| 0.0885 \|
	\| 1.6674 \| 26000 \| 0.0885 \|
	\| 1.6995 \| 26500 \| 0.088 \|
	\| 1.7315 \| 27000 \| 0.0843 \|
	\| 1.7636 \| 27500 \| 0.0915 \|
	\| 1.7957 \| 28000 \| 0.0843 \|
	\| 1.8277 \| 28500 \| 0.0868 \|
	\| 1.8598 \| 29000 \| 0.0857 \|
	\| 1.8919 \| 29500 \| 0.0931 \|
	\| 1.9239 \| 30000 \| 0.0852 \|
	\| 1.9560 \| 30500 \| 0.0913 \|
	\| 1.9881 \| 31000 \| 0.0857 \|
	\| 2.0 \| 31186 \| - \|
	\| 2.0201 \| 31500 \| 0.0547 \|
	\| 2.0522 \| 32000 \| 0.0459 \|
	\| 2.0843 \| 32500 \| 0.0451 \|
	\| 2.1163 \| 33000 \| 0.0407 \|
	\| 2.1484 \| 33500 \| 0.0469 \|
	\| 2.1805 \| 34000 \| 0.0459 \|
	\| 2.2125 \| 34500 \| 0.0508 \|
	\| 2.2446 \| 35000 \| 0.0508 \|
	\| 2.2767 \| 35500 \| 0.0518 \|
	\| 2.3087 \| 36000 \| 0.0552 \|
	\| 2.3408 \| 36500 \| 0.0491 \|
	\| 2.3729 \| 37000 \| 0.0575 \|
	\| 2.4049 \| 37500 \| 0.0558 \|
	\| 2.4370 \| 38000 \| 0.0475 \|
	\| 2.4691 \| 38500 \| 0.0486 \|
	\| 2.5011 \| 39000 \| 0.0536 \|
	\| 2.5332 \| 39500 \| 0.0559 \|
	\| 2.5653 \| 40000 \| 0.0524 \|
	\| 2.5973 \| 40500 \| 0.0496 \|
	\| 2.6294 \| 41000 \| 0.0486 \|
	\| 2.6615 \| 41500 \| 0.0526 \|
	\| 2.6935 \| 42000 \| 0.0443 \|
	\| 2.7256 \| 42500 \| 0.058 \|
	\| 2.7576 \| 43000 \| 0.0543 \|
	\| 2.7897 \| 43500 \| 0.0527 \|
	\| 2.8218 \| 44000 \| 0.0528 \|
	\| 2.8538 \| 44500 \| 0.0573 \|
	\| 2.8859 \| 45000 \| 0.0628 \|
	\| 2.9180 \| 45500 \| 0.0443 \|
	\| 2.9500 \| 46000 \| 0.0531 \|
	\| 2.9821 \| 46500 \| 0.0554 \|
	\| 3.0 \| 46779 \| - \|
	\| 3.0142 \| 47000 \| 0.0346 \|
	\| 3.0462 \| 47500 \| 0.0288 \|
	\| 3.0783 \| 48000 \| 0.0219 \|
	\| 3.1104 \| 48500 \| 0.0259 \|
	\| 3.1424 \| 49000 \| 0.0237 \|
	\| 3.1745 \| 49500 \| 0.0307 \|
	\| 3.2066 \| 50000 \| 0.0234 \|
	\| 3.2386 \| 50500 \| 0.0312 \|
	\| 3.2707 \| 51000 \| 0.0297 \|
	\| 3.3028 \| 51500 \| 0.0299 \|
	\| 3.3348 \| 52000 \| 0.0326 \|
	\| 3.3669 \| 52500 \| 0.0266 \|
	\| 3.3990 \| 53000 \| 0.0296 \|
	\| 3.4310 \| 53500 \| 0.0289 \|
	\| 3.4631 \| 54000 \| 0.0216 \|
	\| 3.4952 \| 54500 \| 0.0289 \|
	\| 3.5272 \| 55000 \| 0.033 \|
	\| 3.5593 \| 55500 \| 0.0248 \|
	\| 3.5914 \| 56000 \| 0.0246 \|
	\| 3.6234 \| 56500 \| 0.0287 \|
	\| 3.6555 \| 57000 \| 0.0267 \|
	\| 3.6876 \| 57500 \| 0.0285 \|
	\| 3.7196 \| 58000 \| 0.0288 \|
	\| 3.7517 \| 58500 \| 0.0283 \|
	\| 3.7837 \| 59000 \| 0.0283 \|
	\| 3.8158 \| 59500 \| 0.029 \|
	\| 3.8479 \| 60000 \| 0.0327 \|
	\| 3.8799 \| 60500 \| 0.0239 \|
	\| 3.9120 \| 61000 \| 0.0356 \|
	\| 3.9441 \| 61500 \| 0.0323 \|
	\| 3.9761 \| 62000 \| 0.0213 \|
	\| 4.0 \| 62372 \| - \|
	\| 4.0082 \| 62500 \| 0.0275 \|
	\| 4.0403 \| 63000 \| 0.0125 \|
	\| 4.0723 \| 63500 \| 0.0183 \|
	\| 4.1044 \| 64000 \| 0.0138 \|
	\| 4.1365 \| 64500 \| 0.0174 \|
	\| 4.1685 \| 65000 \| 0.0088 \|
	\| 4.2006 \| 65500 \| 0.0126 \|
	\| 4.2327 \| 66000 \| 0.0134 \|
	\| 4.2647 \| 66500 \| 0.0099 \|
	\| 4.2968 \| 67000 \| 0.0188 \|
	\| 4.3289 \| 67500 \| 0.0112 \|
	\| 4.3609 \| 68000 \| 0.0156 \|
	\| 4.3930 \| 68500 \| 0.0175 \|
	\| 4.4251 \| 69000 \| 0.0128 \|
	\| 4.4571 \| 69500 \| 0.0154 \|
	\| 4.4892 \| 70000 \| 0.0127 \|
	\| 4.5213 \| 70500 \| 0.0131 \|
	\| 4.5533 \| 71000 \| 0.017 \|
	\| 4.5854 \| 71500 \| 0.0116 \|
	\| 4.6175 \| 72000 \| 0.0137 \|
	\| 4.6495 \| 72500 \| 0.0156 \|
	\| 4.6816 \| 73000 \| 0.0155 \|
	\| 4.7137 \| 73500 \| 0.0078 \|
	\| 4.7457 \| 74000 \| 0.0152 \|
	\| 4.7778 \| 74500 \| 0.0089 \|
	\| 4.8099 \| 75000 \| 0.0116 \|
	\| 4.8419 \| 75500 \| 0.0144 \|
	\| 4.8740 \| 76000 \| 0.0112 \|
	\| 4.9060 \| 76500 \| 0.0108 \|
	\| 4.9381 \| 77000 \| 0.0188 \|
	\| 4.9702 \| 77500 \| 0.0109 \|
	\| 5.0 \| 77965 \| - \|

	</details>

	### Framework Versions
	- Python: 3.11.11
	- Sentence Transformers: 3.4.1
	- Transformers: 4.49.0
	- PyTorch: 2.5.1+cu124
	- Accelerate: 1.3.0
	- Datasets: 3.3.2
	- Tokenizers: 0.21.0

	## Citation

	### BibTeX

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

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