Add new SentenceTransformer model.

02da0dc verified 5 months ago

29.8 kB

	---
	base_model: google-bert/bert-base-uncased
	datasets:
	- sentence-transformers/gooaq
	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
	- dot_accuracy@1
	- dot_accuracy@3
	- dot_accuracy@5
	- dot_accuracy@10
	- dot_precision@1
	- dot_precision@3
	- dot_precision@5
	- dot_precision@10
	- dot_recall@1
	- dot_recall@3
	- dot_recall@5
	- dot_recall@10
	- dot_ndcg@10
	- dot_mrr@10
	- dot_map@100
	pipeline_tag: sentence-similarity
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:3002496
	- loss:MultipleNegativesRankingLoss
	widget:
	- source_sentence: extreme old age is called?
	sentences:
	- The organic process of ageing is called senescence, the medical study of the aging
	process is called gerontology, and the study of diseases that afflict the elderly
	is called geriatrics. ... Old age is not a definite biological stage, as the chronological
	age denoted as "old age" varies culturally and historically.
	- The syllabus is described as the summary of the topics covered or units to be
	taught in the particular subject. Curriculum refers to the overall content, taught
	in an educational system or a course. ... Syllabus is descriptive in nature, but
	the curriculum is prescriptive. Syllabus is set for a particular subject.
	- Keep records for 3 years from the date you filed your original return or 2 years
	from the date you paid the tax, whichever is later, if you file a claim for credit
	or refund after you file your return. Keep records for 7 years if you file a claim
	for a loss from worthless securities or bad debt deduction.
	- source_sentence: has or as when to use?
	sentences:
	- 'Re: Has or as As is an adverb used in comparisons to refer to the extent or degree
	of something; a conjunction 1 used to indicate simultaneous occurrence. 2 used
	to indicate by comparison the way that something happens.'
	- Go through their posts, likes, comments, and followers to see if the suspect's
	username appears. If the user's name appears, click on it. If you click on the
	user's profile and are unable to see their content, even though it says they have
	a number of posts at the top of their profile, then they have blocked you.
	- There's just a 2.6% + $0.30 fee on any portion funded by your credit or debit
	card.
	- source_sentence: how many inches of snow is good for snowboarding?
	sentences:
	- All kinds of tomato paste come with a best-by date. Like other condiments, such
	as bbq sauce, the unopened paste will easily last months past the date on the
	label.
	- Data Storage Data in an SD card is stored on a series of electronic components
	called NAND chips. These chips allow data to be written and stored on the SD card.
	As the chips have no moving parts, data can be transferred from the cards quickly,
	far exceeding the speeds available to CD or hard-drive media.
	- In these areas, as little as 2-4 inches of snow may be sufficient. Other pistes,
	however, may traverse uneven, rocky terrain. In these areas, several inches to
	several feet may be necessary to cover the rocky surface. Even more important
	than the amount of snowfall is the amount of snow that is retained on the slopes.
	- source_sentence: is it normal to have a period after not having one for 8 months?
	sentences:
	- It is not normal to bleed or spot 12 months or more after your last period. Bleeding
	after menopause is usually a sign of a minor health problem but can sometimes
	be an early sign of more serious disease.
	- '[''What are your recruiting needs for my class? ... '', ''What are the next steps
	in the recruiting process with your program? ... '', ''What is your recruiting
	timeline? ... '', ''What does a typical day or week look like for a player during
	the season? ... '', ''What are the off-season expectations for a player? ... '',
	''What are the values of your program?'']'
	- Registered retirement savings plans (RRSP) and registered pension plans (RPP)
	are both retirement savings plans that are registered with the Canada Revenue
	Agency (CRA). RRSPs are individual retirement plans, while RPPs are plans established
	by companies to provide pensions to their employees.
	- source_sentence: what health services are covered by medicare?
	sentences:
	- Medicare Part A hospital insurance covers inpatient hospital care, skilled nursing
	facility, hospice, lab tests, surgery, home health care.
	- Meiocytes are the diploid cells which undergo meiosis to produce gametes. They
	are also known as gamete mother cells. The chromosome number in diploid cells
	of onion is 16. So meiocytes have 16 chromosomes.
	- Elephants have the longest gestation period of all mammals. These gentle giants'
	pregnancies last for more than a year and a half. The average gestation period
	of an elephant is about 640 to 660 days, or roughly 95 weeks.
	co2_eq_emissions:
	emissions: 408.66249919578786
	energy_consumed: 1.0513516760803594
	source: codecarbon
	training_type: fine-tuning
	on_cloud: false
	cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
	ram_total_size: 31.777088165283203
	hours_used: 2.832
	hardware_used: 1 x NVIDIA GeForce RTX 3090
	model-index:
	- name: BERT base uncased trained on GooAQ triplets
	results:
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: gooaq dev
	type: gooaq-dev
	metrics:
	- type: cosine_accuracy@1
	value: 0.576
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.7295
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.7824
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.8462
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.576
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.24316666666666664
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.15648
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.08462
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.576
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.7295
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.7824
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.8462
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.7089171465159466
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.6652589285714262
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.6708962490161547
	name: Cosine Map@100
	- type: dot_accuracy@1
	value: 0.5263
	name: Dot Accuracy@1
	- type: dot_accuracy@3
	value: 0.6922
	name: Dot Accuracy@3
	- type: dot_accuracy@5
	value: 0.7494
	name: Dot Accuracy@5
	- type: dot_accuracy@10
	value: 0.8175
	name: Dot Accuracy@10
	- type: dot_precision@1
	value: 0.5263
	name: Dot Precision@1
	- type: dot_precision@3
	value: 0.23073333333333335
	name: Dot Precision@3
	- type: dot_precision@5
	value: 0.14987999999999999
	name: Dot Precision@5
	- type: dot_precision@10
	value: 0.08175
	name: Dot Precision@10
	- type: dot_recall@1
	value: 0.5263
	name: Dot Recall@1
	- type: dot_recall@3
	value: 0.6922
	name: Dot Recall@3
	- type: dot_recall@5
	value: 0.7494
	name: Dot Recall@5
	- type: dot_recall@10
	value: 0.8175
	name: Dot Recall@10
	- type: dot_ndcg@10
	value: 0.6696727448603579
	name: Dot Ndcg@10
	- type: dot_mrr@10
	value: 0.622603690476188
	name: Dot Mrr@10
	- type: dot_map@100
	value: 0.6291100061102131
	name: Dot Map@100
	---

	# BERT base uncased trained on GooAQ triplets

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the [sentence-transformers/gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) dataset. 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: [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) <!-- at revision 86b5e0934494bd15c9632b12f734a8a67f723594 -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 768 tokens
	- Similarity Function: Cosine Similarity
	- Training Dataset:
	- [sentence-transformers/gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq)
	- 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': False}) with Transformer model: PeftModelForFeatureExtraction
	(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
	)
	```

	## Usage

	### Direct Usage (Sentence Transformers)

	First install the Sentence Transformers library:

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

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

	# Download from the 🤗 Hub
	model = SentenceTransformer("tomaarsen/bert-base-uncased-gooaq-peft")
	# Run inference
	sentences = [
	'what health services are covered by medicare?',
	'Medicare Part A hospital insurance covers inpatient hospital care, skilled nursing facility, hospice, lab tests, surgery, home health care.',
	"Elephants have the longest gestation period of all mammals. These gentle giants' pregnancies last for more than a year and a half. The average gestation period of an elephant is about 640 to 660 days, or roughly 95 weeks.",
	]
	embeddings = model.encode(sentences)
	print(embeddings.shape)
	# [3, 768]

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

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

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

	</details>
	-->

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

	You can finetune this model on your own dataset.

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

	</details>
	-->

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

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

	## Evaluation

	### Metrics

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

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| cosine_accuracy@1 \| 0.576 \|
	\| cosine_accuracy@3 \| 0.7295 \|
	\| cosine_accuracy@5 \| 0.7824 \|
	\| cosine_accuracy@10 \| 0.8462 \|
	\| cosine_precision@1 \| 0.576 \|
	\| cosine_precision@3 \| 0.2432 \|
	\| cosine_precision@5 \| 0.1565 \|
	\| cosine_precision@10 \| 0.0846 \|
	\| cosine_recall@1 \| 0.576 \|
	\| cosine_recall@3 \| 0.7295 \|
	\| cosine_recall@5 \| 0.7824 \|
	\| cosine_recall@10 \| 0.8462 \|
	\| cosine_ndcg@10 \| 0.7089 \|
	\| cosine_mrr@10 \| 0.6653 \|
	\| cosine_map@100 \| 0.6709 \|
	\| dot_accuracy@1 \| 0.5263 \|
	\| dot_accuracy@3 \| 0.6922 \|
	\| dot_accuracy@5 \| 0.7494 \|
	\| dot_accuracy@10 \| 0.8175 \|
	\| dot_precision@1 \| 0.5263 \|
	\| dot_precision@3 \| 0.2307 \|
	\| dot_precision@5 \| 0.1499 \|
	\| dot_precision@10 \| 0.0818 \|
	\| dot_recall@1 \| 0.5263 \|
	\| dot_recall@3 \| 0.6922 \|
	\| dot_recall@5 \| 0.7494 \|
	\| dot_recall@10 \| 0.8175 \|
	\| dot_ndcg@10 \| 0.6697 \|
	\| dot_mrr@10 \| 0.6226 \|
	\| dot_map@100 \| 0.6291 \|

	<!--
	## 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

	#### sentence-transformers/gooaq

	* Dataset: [sentence-transformers/gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c)
	* Size: 3,002,496 training samples
	* Columns: <code>question</code> and <code>answer</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| question \| answer \|
	\|:--------\|:----------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------\|
	\| type \| string \| string \|
	\| details \| <ul><li>min: 8 tokens</li><li>mean: 11.84 tokens</li><li>max: 31 tokens</li></ul> \| <ul><li>min: 13 tokens</li><li>mean: 60.69 tokens</li><li>max: 149 tokens</li></ul> \|
	* Samples:
	\| question \| answer \|
	\|:-------------------------------------------------------------------------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>can dogs get pregnant when on their period?</code> \| <code>2. Female dogs can only get pregnant when they're in heat. Some females will show physical signs of readiness – their discharge will lighten in color, and they will “flag,” or lift their tail up and to the side.</code> \|
	\| <code>are there different forms of als?</code> \| <code>['Sporadic ALS is the most common form. It affects up to 95% of people with the disease. Sporadic means it happens sometimes without a clear cause.', 'Familial ALS (FALS) runs in families. About 5% to 10% of people with ALS have this type. FALS is caused by changes to a gene.']</code> \|
	\| <code>what is the difference between stayman and jacoby transfer?</code> \| <code>1. The Stayman Convention is used only with a 4-Card Major suit looking for a 4-Card Major suit fit. Jacoby Transfer bids are used with a 5-Card suit looking for a 3-Card fit.</code> \|
	* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
	```json
	{
	"scale": 20.0,
	"similarity_fct": "cos_sim"
	}
	```

	### Evaluation Dataset

	#### sentence-transformers/gooaq

	* Dataset: [sentence-transformers/gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c)
	* Size: 10,000 evaluation samples
	* Columns: <code>question</code> and <code>answer</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| question \| answer \|
	\|:--------\|:----------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------\|
	\| type \| string \| string \|
	\| details \| <ul><li>min: 8 tokens</li><li>mean: 12.01 tokens</li><li>max: 28 tokens</li></ul> \| <ul><li>min: 19 tokens</li><li>mean: 61.37 tokens</li><li>max: 138 tokens</li></ul> \|
	* Samples:
	\| question \| answer \|
	\|:-----------------------------------------------------\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>is there a season 5 animal kingdom?</code> \| <code>the good news for the fans is that the season five was confirmed by TNT in July, 2019. The season five of Animal Kingdom was expected to release in May, 2020.</code> \|
	\| <code>what are cmos voltage levels?</code> \| <code>CMOS gate circuits have input and output signal specifications that are quite different from TTL. For a CMOS gate operating at a power supply voltage of 5 volts, the acceptable input signal voltages range from 0 volts to 1.5 volts for a “low” logic state, and 3.5 volts to 5 volts for a “high” logic state.</code> \|
	\| <code>dangers of drinking coke when pregnant?</code> \| <code>Drinking it during pregnancy was linked to poorer fine motor, visual, spatial and visual motor abilities in early childhood (around age 3). By mid-childhood (age 7), kids whose moms drank diet sodas while pregnant had poorer verbal abilities, the study findings reported.</code> \|
	* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
	```json
	{
	"scale": 20.0,
	"similarity_fct": "cos_sim"
	}
	```

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

	- `eval_strategy`: steps
	- `per_device_train_batch_size`: 128
	- `per_device_eval_batch_size`: 128
	- `learning_rate`: 2e-05
	- `num_train_epochs`: 1
	- `warmup_ratio`: 0.1
	- `bf16`: True
	- `batch_sampler`: no_duplicates

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

	- `overwrite_output_dir`: False
	- `do_predict`: False
	- `eval_strategy`: steps
	- `prediction_loss_only`: True
	- `per_device_train_batch_size`: 128
	- `per_device_eval_batch_size`: 128
	- `per_gpu_train_batch_size`: None
	- `per_gpu_eval_batch_size`: None
	- `gradient_accumulation_steps`: 1
	- `eval_accumulation_steps`: None
	- `learning_rate`: 2e-05
	- `weight_decay`: 0.0
	- `adam_beta1`: 0.9
	- `adam_beta2`: 0.999
	- `adam_epsilon`: 1e-08
	- `max_grad_norm`: 1.0
	- `num_train_epochs`: 1
	- `max_steps`: -1
	- `lr_scheduler_type`: linear
	- `lr_scheduler_kwargs`: {}
	- `warmup_ratio`: 0.1
	- `warmup_steps`: 0
	- `log_level`: passive
	- `log_level_replica`: warning
	- `log_on_each_node`: True
	- `logging_nan_inf_filter`: True
	- `save_safetensors`: True
	- `save_on_each_node`: False
	- `save_only_model`: False
	- `restore_callback_states_from_checkpoint`: False
	- `no_cuda`: False
	- `use_cpu`: False
	- `use_mps_device`: False
	- `seed`: 42
	- `data_seed`: None
	- `jit_mode_eval`: False
	- `use_ipex`: False
	- `bf16`: True
	- `fp16`: False
	- `fp16_opt_level`: O1
	- `half_precision_backend`: auto
	- `bf16_full_eval`: False
	- `fp16_full_eval`: False
	- `tf32`: None
	- `local_rank`: 0
	- `ddp_backend`: None
	- `tpu_num_cores`: None
	- `tpu_metrics_debug`: False
	- `debug`: []
	- `dataloader_drop_last`: False
	- `dataloader_num_workers`: 0
	- `dataloader_prefetch_factor`: None
	- `past_index`: -1
	- `disable_tqdm`: False
	- `remove_unused_columns`: True
	- `label_names`: None
	- `load_best_model_at_end`: False
	- `ignore_data_skip`: False
	- `fsdp`: []
	- `fsdp_min_num_params`: 0
	- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
	- `fsdp_transformer_layer_cls_to_wrap`: None
	- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
	- `deepspeed`: None
	- `label_smoothing_factor`: 0.0
	- `optim`: adamw_torch
	- `optim_args`: None
	- `adafactor`: False
	- `group_by_length`: False
	- `length_column_name`: length
	- `ddp_find_unused_parameters`: None
	- `ddp_bucket_cap_mb`: None
	- `ddp_broadcast_buffers`: False
	- `dataloader_pin_memory`: True
	- `dataloader_persistent_workers`: False
	- `skip_memory_metrics`: True
	- `use_legacy_prediction_loop`: False
	- `push_to_hub`: False
	- `resume_from_checkpoint`: None
	- `hub_model_id`: None
	- `hub_strategy`: every_save
	- `hub_private_repo`: False
	- `hub_always_push`: False
	- `gradient_checkpointing`: False
	- `gradient_checkpointing_kwargs`: None
	- `include_inputs_for_metrics`: False
	- `eval_do_concat_batches`: True
	- `fp16_backend`: auto
	- `push_to_hub_model_id`: None
	- `push_to_hub_organization`: None
	- `mp_parameters`:
	- `auto_find_batch_size`: False
	- `full_determinism`: False
	- `torchdynamo`: None
	- `ray_scope`: last
	- `ddp_timeout`: 1800
	- `torch_compile`: False
	- `torch_compile_backend`: None
	- `torch_compile_mode`: None
	- `dispatch_batches`: None
	- `split_batches`: None
	- `include_tokens_per_second`: False
	- `include_num_input_tokens_seen`: False
	- `neftune_noise_alpha`: None
	- `optim_target_modules`: None
	- `batch_eval_metrics`: False
	- `batch_sampler`: no_duplicates
	- `multi_dataset_batch_sampler`: proportional

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \| loss \| gooaq-dev_cosine_map@100 \|
	\|:------:\|:-----:\|:-------------:\|:------:\|:------------------------:\|
	\| 0 \| 0 \| - \| - \| 0.2017 \|
	\| 0.0000 \| 1 \| 2.584 \| - \| - \|
	\| 0.0213 \| 500 \| 2.4164 \| - \| - \|
	\| 0.0426 \| 1000 \| 1.1421 \| - \| - \|
	\| 0.0639 \| 1500 \| 0.5215 \| - \| - \|
	\| 0.0853 \| 2000 \| 0.3645 \| 0.2763 \| 0.6087 \|
	\| 0.1066 \| 2500 \| 0.3046 \| - \| - \|
	\| 0.1279 \| 3000 \| 0.2782 \| - \| - \|
	\| 0.1492 \| 3500 \| 0.2601 \| - \| - \|
	\| 0.1705 \| 4000 \| 0.2457 \| 0.2013 \| 0.6396 \|
	\| 0.1918 \| 4500 \| 0.2363 \| - \| - \|
	\| 0.2132 \| 5000 \| 0.2291 \| - \| - \|
	\| 0.2345 \| 5500 \| 0.2217 \| - \| - \|
	\| 0.2558 \| 6000 \| 0.2137 \| 0.1770 \| 0.6521 \|
	\| 0.2771 \| 6500 \| 0.215 \| - \| - \|
	\| 0.2984 \| 7000 \| 0.2057 \| - \| - \|
	\| 0.3197 \| 7500 \| 0.198 \| - \| - \|
	\| 0.3410 \| 8000 \| 0.196 \| 0.1626 \| 0.6594 \|
	\| 0.3624 \| 8500 \| 0.1938 \| - \| - \|
	\| 0.3837 \| 9000 \| 0.195 \| - \| - \|
	\| 0.4050 \| 9500 \| 0.1895 \| - \| - \|
	\| 0.4263 \| 10000 \| 0.186 \| 0.1542 \| 0.6628 \|
	\| 0.4476 \| 10500 \| 0.1886 \| - \| - \|
	\| 0.4689 \| 11000 \| 0.1835 \| - \| - \|
	\| 0.4903 \| 11500 \| 0.1825 \| - \| - \|
	\| 0.5116 \| 12000 \| 0.1804 \| 0.1484 \| 0.6638 \|
	\| 0.5329 \| 12500 \| 0.176 \| - \| - \|
	\| 0.5542 \| 13000 \| 0.1825 \| - \| - \|
	\| 0.5755 \| 13500 \| 0.1785 \| - \| - \|
	\| 0.5968 \| 14000 \| 0.1766 \| 0.1436 \| 0.6672 \|
	\| 0.6182 \| 14500 \| 0.1718 \| - \| - \|
	\| 0.6395 \| 15000 \| 0.1717 \| - \| - \|
	\| 0.6608 \| 15500 \| 0.1674 \| - \| - \|
	\| 0.6821 \| 16000 \| 0.1691 \| 0.1406 \| 0.6704 \|
	\| 0.7034 \| 16500 \| 0.1705 \| - \| - \|
	\| 0.7247 \| 17000 \| 0.1693 \| - \| - \|
	\| 0.7460 \| 17500 \| 0.166 \| - \| - \|
	\| 0.7674 \| 18000 \| 0.1676 \| 0.1385 \| 0.6721 \|
	\| 0.7887 \| 18500 \| 0.1666 \| - \| - \|
	\| 0.8100 \| 19000 \| 0.1658 \| - \| - \|
	\| 0.8313 \| 19500 \| 0.1682 \| - \| - \|
	\| 0.8526 \| 20000 \| 0.1639 \| 0.1370 \| 0.6705 \|
	\| 0.8739 \| 20500 \| 0.1711 \| - \| - \|
	\| 0.8953 \| 21000 \| 0.1667 \| - \| - \|
	\| 0.9166 \| 21500 \| 0.165 \| - \| - \|
	\| 0.9379 \| 22000 \| 0.1658 \| 0.1356 \| 0.6711 \|
	\| 0.9592 \| 22500 \| 0.1665 \| - \| - \|
	\| 0.9805 \| 23000 \| 0.1636 \| - \| - \|
	\| 1.0 \| 23457 \| - \| - \| 0.6709 \|


	### Environmental Impact
	Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
	- Energy Consumed: 1.051 kWh
	- Carbon Emitted: 0.409 kg of CO2
	- Hours Used: 2.832 hours

	### Training Hardware
	- On Cloud: No
	- GPU Model: 1 x NVIDIA GeForce RTX 3090
	- CPU Model: 13th Gen Intel(R) Core(TM) i7-13700K
	- RAM Size: 31.78 GB

	### Framework Versions
	- Python: 3.11.6
	- Sentence Transformers: 3.1.0.dev0
	- Transformers: 4.41.2
	- PyTorch: 2.3.0+cu121
	- Accelerate: 0.31.0
	- Datasets: 2.20.0
	- Tokenizers: 0.19.1

	## Citation

	### BibTeX

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

	#### 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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