Add new SentenceTransformer model.

e4bf7b2 verified 5 months ago

30.5 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: how to change date format in ms project 2007?
	sentences:
	- '[''Choose File > Options.'', ''Select General.'', ''Under Project view, pick
	an option from the Date format list.'']'
	- Cats can be very affectionate and bonded with each other and still bond well and
	show affection to their human. Getting two kittens from the same litter, regardless
	of gender, can make it easier for them to befriend each other and play—but any
	two kittens generally tend to get on well after introductions.
	- 'Treat your permed hair like silk or another delicate fabric: washing it once
	a week is enough to keep it clean and help maintain its beauty. Wash your hair
	with warm water. Hot water can strip your hair of oils that help keep it moisturized
	and looking lustrous. Hot water can also ruin the curls.'
	- source_sentence: is the mother in vinegar good for you?
	sentences:
	- Some people say the “mother,” the cloud of yeast and bacteria you might see in
	a bottle of apple cider vinegar, is what makes it healthy. These things are probiotic,
	meaning they might give your digestive system a boost, but there isn't enough
	research to back up the other claims.
	- It is normal for vaginal discharge to increase in amount and become “stringy”
	(like egg whites) during the middle of your menstrual cycle when you're ovulating.
	If you find that your normal discharge is annoying, you can wear panty liners/shields
	on your underwear.
	- State law protects cypress trees along Florida's waterways, but it has been up
	to the courts to enforce the regulations. ... Landowners can cut down cypress
	trees on their land, but trees below the high-water mark are considered state
	property and are protected.
	- source_sentence: if you're blocked on whatsapp can you see last seen?
	sentences:
	- Jaguars aren't going to London this year, releases new plan for season tickets.
	The Jaguars will no longer be playing two games in London, and will instead play
	both games at TIAA Bank Field.
	- Typically, most drugs are absorbed within 20-30 minutes after given by mouth.
	Vomiting after this amount of time is not related to the drug in the stomach as
	the vast majority, if not all, has already been absorbed.
	- You can no longer see a contact's last seen or online in the chat window. Learn
	more here. You do not see updates to a contact's profile photo. Any messages sent
	to a contact who has blocked you will always show one check mark (message sent),
	and never show a second check mark (message delivered).
	- source_sentence: how many enchantments can you put on armor?
	sentences:
	- 4 Answers. You can in theory add every enchantment that is compatible with a tool/weapon/armor
	onto the same item. The bow can have these 7 enchantments, though mending and
	infinity are mutually exclusive.
	- The sleeve length will make or break a jacket. If too long, it will make the jacket
	look too big, and if too short, like you have outgrown your jacket. ... This is
	when you need an experienced tailor, who will be able to shorten the sleeves from
	the shoulders, so the details on the cuffs are not disturbed.
	- Grace period of 60 days granted after the expiration of license for purpose of
	renewal, and license is valid during this period. Renewal of license may occur
	from 60 days (effective August 1, 2016, 180 days) prior to expiration to 3 years
	after date; afterwards, applicant required to take and pass examination.
	- source_sentence: what is the best drugstore shampoo for volume?
	sentences:
	- '[''#8. ... '', ''#7. ... '', ''#6. Hask Biotin Boost Shampoo. ... '', ''#5. Pantene
	Pro-V Sheer Volume Shampoo. ... '', ''#4. John Frieda Luxurious Volume Touchably
	Full Shampoo. ... '', ''#3. Acure Vivacious Volume Peppermint Shampoo. ... '',
	''#2. OGX Thick & Full Biotin & Collagen Shampoo. ... '', "#1. L''Oréal Paris
	EverPure Sulfate Free Volume Shampoo."]'
	- Genes can't control an organism on their own; rather, they must interact with
	and respond to the organism's environment. Some genes are constitutive, or always
	"on," regardless of environmental conditions.
	- In electricity, the phase refers to the distribution of a load. What is the difference
	between single-phase and three-phase power supplies? Single-phase power is a two-wire
	alternating current (ac) power circuit. ... Three-phase power is a three-wire
	ac power circuit with each phase ac signal 120 electrical degrees apart.
	co2_eq_emissions:
	emissions: 523.8395173647017
	energy_consumed: 1.3476635503925931
	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: 3.544
	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: 0.7001
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.8712
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.9219
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.9629
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.7001
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.2904
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.18438000000000002
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.09629000000000001
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.7001
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.8712
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.9219
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.9629
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.8358567622290791
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.7945682142857085
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.796615366916047
	name: Cosine Map@100
	- type: dot_accuracy@1
	value: 0.6709
	name: Dot Accuracy@1
	- type: dot_accuracy@3
	value: 0.8558
	name: Dot Accuracy@3
	- type: dot_accuracy@5
	value: 0.9096
	name: Dot Accuracy@5
	- type: dot_accuracy@10
	value: 0.9567
	name: Dot Accuracy@10
	- type: dot_precision@1
	value: 0.6709
	name: Dot Precision@1
	- type: dot_precision@3
	value: 0.28526666666666667
	name: Dot Precision@3
	- type: dot_precision@5
	value: 0.18192000000000003
	name: Dot Precision@5
	- type: dot_precision@10
	value: 0.09567
	name: Dot Precision@10
	- type: dot_recall@1
	value: 0.6709
	name: Dot Recall@1
	- type: dot_recall@3
	value: 0.8558
	name: Dot Recall@3
	- type: dot_recall@5
	value: 0.9096
	name: Dot Recall@5
	- type: dot_recall@10
	value: 0.9567
	name: Dot Recall@10
	- type: dot_ndcg@10
	value: 0.8177950307933399
	name: Dot Ndcg@10
	- type: dot_mrr@10
	value: 0.772776468253962
	name: Dot Mrr@10
	- type: dot_map@100
	value: 0.7751231358698718
	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: BertModel
	(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")
	# Run inference
	sentences = [
	'what is the best drugstore shampoo for volume?',
	'[\'#8. ... \', \'#7. ... \', \'#6. Hask Biotin Boost Shampoo. ... \', \'#5. Pantene Pro-V Sheer Volume Shampoo. ... \', \'#4. John Frieda Luxurious Volume Touchably Full Shampoo. ... \', \'#3. Acure Vivacious Volume Peppermint Shampoo. ... \', \'#2. OGX Thick & Full Biotin & Collagen Shampoo. ... \', "#1. L\'Oréal Paris EverPure Sulfate Free Volume Shampoo."]',
	'In electricity, the phase refers to the distribution of a load. What is the difference between single-phase and three-phase power supplies? Single-phase power is a two-wire alternating current (ac) power circuit. ... Three-phase power is a three-wire ac power circuit with each phase ac signal 120 electrical degrees apart.',
	]
	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 \| 0.7001 \|
	\| cosine_accuracy@3 \| 0.8712 \|
	\| cosine_accuracy@5 \| 0.9219 \|
	\| cosine_accuracy@10 \| 0.9629 \|
	\| cosine_precision@1 \| 0.7001 \|
	\| cosine_precision@3 \| 0.2904 \|
	\| cosine_precision@5 \| 0.1844 \|
	\| cosine_precision@10 \| 0.0963 \|
	\| cosine_recall@1 \| 0.7001 \|
	\| cosine_recall@3 \| 0.8712 \|
	\| cosine_recall@5 \| 0.9219 \|
	\| cosine_recall@10 \| 0.9629 \|
	\| cosine_ndcg@10 \| 0.8359 \|
	\| cosine_mrr@10 \| 0.7946 \|
	\| cosine_map@100 \| 0.7966 \|
	\| dot_accuracy@1 \| 0.6709 \|
	\| dot_accuracy@3 \| 0.8558 \|
	\| dot_accuracy@5 \| 0.9096 \|
	\| dot_accuracy@10 \| 0.9567 \|
	\| dot_precision@1 \| 0.6709 \|
	\| dot_precision@3 \| 0.2853 \|
	\| dot_precision@5 \| 0.1819 \|
	\| dot_precision@10 \| 0.0957 \|
	\| dot_recall@1 \| 0.6709 \|
	\| dot_recall@3 \| 0.8558 \|
	\| dot_recall@5 \| 0.9096 \|
	\| dot_recall@10 \| 0.9567 \|
	\| dot_ndcg@10 \| 0.8178 \|
	\| dot_mrr@10 \| 0.7728 \|
	\| dot_map@100 \| 0.7751 \|

	<!--
	## 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: 11.95 tokens</li><li>max: 24 tokens</li></ul> \| <ul><li>min: 17 tokens</li><li>mean: 60.83 tokens</li><li>max: 130 tokens</li></ul> \|
	* Samples:
	\| question \| answer \|
	\|:------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>what are the differences between internet and web?</code> \| <code>The Internet is a global network of networks while the Web, also referred formally as World Wide Web (www) is collection of information which is accessed via the Internet. Another way to look at this difference is; the Internet is infrastructure while the Web is service on top of that infrastructure.</code> \|
	\| <code>who is the most important person in a first aid situation?</code> \| <code>Subscribe to New First Aid For Free The main principle of incident management is that you are the most important person and your safety comes first! Your first actions when coming across the scene of an incident should be: Check for any dangers to yourself or bystanders. Manage any dangers found (if safe to do so)</code> \|
	\| <code>why is jibjab not working?</code> \| <code>Usually disabling your ad blockers for JibJab will resolve this issue. If you're still having issues loading the card after your ad blockers are disabled, you can try clearing your cache/cookies or updating and restarting your browser. As a last resort, you can try opening JibJab from a different browser.</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: 34 tokens</li></ul> \| <ul><li>min: 13 tokens</li><li>mean: 59.81 tokens</li><li>max: 145 tokens</li></ul> \|
	* Samples:
	\| question \| answer \|
	\|:---------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>what are some common attributes/characteristics between animal and human?</code> \| <code>['Culture.', 'Emotions.', 'Language.', 'Humour.', 'Tool Use.', 'Memory.', 'Self-Awareness.', 'Intelligence.']</code> \|
	\| <code>is folic acid the same as vitamin b?</code> \| <code>Vitamin B9, also called folate or folic acid, is one of 8 B vitamins. All B vitamins help the body convert food (carbohydrates) into fuel (glucose), which is used to produce energy. These B vitamins, often referred to as B-complex vitamins, also help the body use fats and protein.</code> \|
	\| <code>are bendy buses still in london?</code> \| <code>Bendy bus makes final journey for Transport for London. The last of London's bendy buses was taken off the roads on Friday night. ... The final route to be operated with bendy buses has been the 207 between Hayes and White City, and the last of the long vehicles was to run late on Friday.</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 \| - \| - \| 0.2018 \|
	\| 0.0000 \| 1 \| 2.6207 \| - \| - \|
	\| 0.0213 \| 500 \| 0.9092 \| - \| - \|
	\| 0.0426 \| 1000 \| 0.2051 \| - \| - \|
	\| 0.0639 \| 1500 \| 0.1354 \| - \| - \|
	\| 0.0853 \| 2000 \| 0.1089 \| 0.0719 \| 0.7124 \|
	\| 0.1066 \| 2500 \| 0.0916 \| - \| - \|
	\| 0.1279 \| 3000 \| 0.0812 \| - \| - \|
	\| 0.1492 \| 3500 \| 0.0716 \| - \| - \|
	\| 0.1705 \| 4000 \| 0.0658 \| 0.0517 \| 0.7432 \|
	\| 0.1918 \| 4500 \| 0.0623 \| - \| - \|
	\| 0.2132 \| 5000 \| 0.0596 \| - \| - \|
	\| 0.2345 \| 5500 \| 0.0554 \| - \| - \|
	\| 0.2558 \| 6000 \| 0.0504 \| 0.0401 \| 0.7580 \|
	\| 0.2771 \| 6500 \| 0.0498 \| - \| - \|
	\| 0.2984 \| 7000 \| 0.0483 \| - \| - \|
	\| 0.3197 \| 7500 \| 0.0487 \| - \| - \|
	\| 0.3410 \| 8000 \| 0.0458 \| 0.0359 \| 0.7652 \|
	\| 0.3624 \| 8500 \| 0.0435 \| - \| - \|
	\| 0.3837 \| 9000 \| 0.0421 \| - \| - \|
	\| 0.4050 \| 9500 \| 0.0421 \| - \| - \|
	\| 0.4263 \| 10000 \| 0.0405 \| 0.0329 \| 0.7738 \|
	\| 0.4476 \| 10500 \| 0.0392 \| - \| - \|
	\| 0.4689 \| 11000 \| 0.0388 \| - \| - \|
	\| 0.4903 \| 11500 \| 0.0388 \| - \| - \|
	\| 0.5116 \| 12000 \| 0.0361 \| 0.0290 \| 0.7810 \|
	\| 0.5329 \| 12500 \| 0.0362 \| - \| - \|
	\| 0.5542 \| 13000 \| 0.0356 \| - \| - \|
	\| 0.5755 \| 13500 \| 0.0352 \| - \| - \|
	\| 0.5968 \| 14000 \| 0.0349 \| 0.0267 \| 0.7866 \|
	\| 0.6182 \| 14500 \| 0.0334 \| - \| - \|
	\| 0.6395 \| 15000 \| 0.0323 \| - \| - \|
	\| 0.6608 \| 15500 \| 0.0325 \| - \| - \|
	\| 0.6821 \| 16000 \| 0.0316 \| 0.0256 \| 0.7879 \|
	\| 0.7034 \| 16500 \| 0.0313 \| - \| - \|
	\| 0.7247 \| 17000 \| 0.0306 \| - \| - \|
	\| 0.7460 \| 17500 \| 0.0328 \| - \| - \|
	\| 0.7674 \| 18000 \| 0.0303 \| 0.0238 \| 0.7928 \|
	\| 0.7887 \| 18500 \| 0.0301 \| - \| - \|
	\| 0.8100 \| 19000 \| 0.0291 \| - \| - \|
	\| 0.8313 \| 19500 \| 0.0286 \| - \| - \|
	\| 0.8526 \| 20000 \| 0.0295 \| 0.0218 \| 0.7952 \|
	\| 0.8739 \| 20500 \| 0.0288 \| - \| - \|
	\| 0.8953 \| 21000 \| 0.0277 \| - \| - \|
	\| 0.9166 \| 21500 \| 0.0266 \| - \| - \|
	\| 0.9379 \| 22000 \| 0.0289 \| 0.0218 \| 0.7971 \|
	\| 0.9592 \| 22500 \| 0.0286 \| - \| - \|
	\| 0.9805 \| 23000 \| 0.0275 \| - \| - \|
	\| 1.0 \| 23457 \| - \| - \| 0.7966 \|


	### Environmental Impact
	Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
	- Energy Consumed: 1.348 kWh
	- Carbon Emitted: 0.524 kg of CO2
	- Hours Used: 3.544 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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