Add new SentenceTransformer model

ed48493 verified 26 days ago

33.1 kB

	---
	language:
	- en
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:557850
	- loss:MatryoshkaLoss
	- loss:MultipleNegativesRankingLoss
	base_model: microsoft/mpnet-base
	widget:
	- source_sentence: A man is jumping unto his filthy bed.
	sentences:
	- A young male is looking at a newspaper while 2 females walks past him.
	- The bed is dirty.
	- The man is on the moon.
	- source_sentence: A carefully balanced male stands on one foot near a clean ocean
	beach area.
	sentences:
	- A man is ouside near the beach.
	- Three policemen patrol the streets on bikes
	- A man is sitting on his couch.
	- source_sentence: The man is wearing a blue shirt.
	sentences:
	- Near the trashcan the man stood and smoked
	- A man in a blue shirt leans on a wall beside a road with a blue van and red car
	with water in the background.
	- A man in a black shirt is playing a guitar.
	- source_sentence: The girls are outdoors.
	sentences:
	- Two girls riding on an amusement part ride.
	- a guy laughs while doing laundry
	- Three girls are standing together in a room, one is listening, one is writing
	on a wall and the third is talking to them.
	- source_sentence: A construction worker peeking out of a manhole while his coworker
	sits on the sidewalk smiling.
	sentences:
	- A worker is looking out of a manhole.
	- A man is giving a presentation.
	- The workers are both inside the manhole.
	datasets:
	- sentence-transformers/all-nli
	pipeline_tag: sentence-similarity
	library_name: sentence-transformers
	metrics:
	- pearson_cosine
	- spearman_cosine
	co2_eq_emissions:
	emissions: 205.739032893975
	energy_consumed: 0.5292975927419334
	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: 2.452
	hardware_used: 1 x NVIDIA GeForce RTX 3090
	model-index:
	- name: SentenceTransformer based on microsoft/mpnet-base
	results:
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts dev 768
	type: sts-dev-768
	metrics:
	- type: pearson_cosine
	value: 0.8427806843466507
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8508672705970183
	name: Spearman Cosine
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts dev 512
	type: sts-dev-512
	metrics:
	- type: pearson_cosine
	value: 0.8402650019702758
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8492501196021981
	name: Spearman Cosine
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts dev 256
	type: sts-dev-256
	metrics:
	- type: pearson_cosine
	value: 0.8346871892249894
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8462852114011874
	name: Spearman Cosine
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts dev 128
	type: sts-dev-128
	metrics:
	- type: pearson_cosine
	value: 0.8258126981506843
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8396442287070809
	name: Spearman Cosine
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts dev 64
	type: sts-dev-64
	metrics:
	- type: pearson_cosine
	value: 0.8133510090549183
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8314093123007742
	name: Spearman Cosine
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 768
	type: sts-test-768
	metrics:
	- type: pearson_cosine
	value: 0.8189065344720828
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8358553875433253
	name: Spearman Cosine
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 512
	type: sts-test-512
	metrics:
	- type: pearson_cosine
	value: 0.8185683063331012
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8361687236813662
	name: Spearman Cosine
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 256
	type: sts-test-256
	metrics:
	- type: pearson_cosine
	value: 0.8129602938883278
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8332021961323041
	name: Spearman Cosine
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 128
	type: sts-test-128
	metrics:
	- type: pearson_cosine
	value: 0.8030325360463209
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.826154869627039
	name: Spearman Cosine
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 64
	type: sts-test-64
	metrics:
	- type: pearson_cosine
	value: 0.7903762214352186
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8193971659006509
	name: Spearman Cosine
	---

	# SentenceTransformer based on microsoft/mpnet-base

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) on the [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) 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: [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) <!-- at revision 6996ce1e91bd2a9c7d7f61daec37463394f73f09 -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 768 dimensions
	- Similarity Function: Cosine Similarity
	- Training Dataset:
	- [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli)
	- Language: en
	<!-- - 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': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
	(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/mpnet-base-nli-matryoshka-reproduced")
	# Run inference
	sentences = [
	'A construction worker peeking out of a manhole while his coworker sits on the sidewalk smiling.',
	'A worker is looking out of a manhole.',
	'The workers are both inside the manhole.',
	]
	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

	#### Semantic Similarity

	* Datasets: `sts-dev-768` and `sts-test-768`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) with these parameters:
	```json
	{
	"truncate_dim": 768
	}
	```

	\| Metric \| sts-dev-768 \| sts-test-768 \|
	\|:--------------------\|:------------\|:-------------\|
	\| pearson_cosine \| 0.8428 \| 0.8189 \|
	\| spearman_cosine \| 0.8509 \| 0.8359 \|

	#### Semantic Similarity

	* Datasets: `sts-dev-512` and `sts-test-512`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) with these parameters:
	```json
	{
	"truncate_dim": 512
	}
	```

	\| Metric \| sts-dev-512 \| sts-test-512 \|
	\|:--------------------\|:------------\|:-------------\|
	\| pearson_cosine \| 0.8403 \| 0.8186 \|
	\| spearman_cosine \| 0.8493 \| 0.8362 \|

	#### Semantic Similarity

	* Datasets: `sts-dev-256` and `sts-test-256`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) with these parameters:
	```json
	{
	"truncate_dim": 256
	}
	```

	\| Metric \| sts-dev-256 \| sts-test-256 \|
	\|:--------------------\|:------------\|:-------------\|
	\| pearson_cosine \| 0.8347 \| 0.813 \|
	\| spearman_cosine \| 0.8463 \| 0.8332 \|

	#### Semantic Similarity

	* Datasets: `sts-dev-128` and `sts-test-128`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) with these parameters:
	```json
	{
	"truncate_dim": 128
	}
	```

	\| Metric \| sts-dev-128 \| sts-test-128 \|
	\|:--------------------\|:------------\|:-------------\|
	\| pearson_cosine \| 0.8258 \| 0.803 \|
	\| spearman_cosine \| 0.8396 \| 0.8262 \|

	#### Semantic Similarity

	* Datasets: `sts-dev-64` and `sts-test-64`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) with these parameters:
	```json
	{
	"truncate_dim": 64
	}
	```

	\| Metric \| sts-dev-64 \| sts-test-64 \|
	\|:--------------------\|:-----------\|:------------\|
	\| pearson_cosine \| 0.8134 \| 0.7904 \|
	\| spearman_cosine \| 0.8314 \| 0.8194 \|

	<!--
	## 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

	#### all-nli

	* Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
	* Size: 557,850 training samples
	* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| anchor \| positive \| negative \|
	\|:--------\|:----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\|
	\| type \| string \| string \| string \|
	\| details \| <ul><li>min: 7 tokens</li><li>mean: 10.46 tokens</li><li>max: 46 tokens</li></ul> \| <ul><li>min: 6 tokens</li><li>mean: 12.81 tokens</li><li>max: 40 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 13.4 tokens</li><li>max: 50 tokens</li></ul> \|
	* Samples:
	\| anchor \| positive \| negative \|
	\|:---------------------------------------------------------------------------\|:-------------------------------------------------\|:-----------------------------------------------------------\|
	\| <code>A person on a horse jumps over a broken down airplane.</code> \| <code>A person is outdoors, on a horse.</code> \| <code>A person is at a diner, ordering an omelette.</code> \|
	\| <code>Children smiling and waving at camera</code> \| <code>There are children present</code> \| <code>The kids are frowning</code> \|
	\| <code>A boy is jumping on skateboard in the middle of a red bridge.</code> \| <code>The boy does a skateboarding trick.</code> \| <code>The boy skates down the sidewalk.</code> \|
	* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
	```json
	{
	"loss": "MultipleNegativesRankingLoss",
	"matryoshka_dims": [
	768,
	512,
	256,
	128,
	64
	],
	"matryoshka_weights": [
	1,
	1,
	1,
	1,
	1
	],
	"n_dims_per_step": -1
	}
	```

	### Evaluation Dataset

	#### all-nli

	* Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
	* Size: 6,584 evaluation samples
	* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| anchor \| positive \| negative \|
	\|:--------\|:----------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|
	\| type \| string \| string \| string \|
	\| details \| <ul><li>min: 6 tokens</li><li>mean: 17.95 tokens</li><li>max: 63 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 9.78 tokens</li><li>max: 29 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 10.35 tokens</li><li>max: 29 tokens</li></ul> \|
	* Samples:
	\| anchor \| positive \| negative \|
	\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:------------------------------------------------------------\|:--------------------------------------------------------\|
	\| <code>Two women are embracing while holding to go packages.</code> \| <code>Two woman are holding packages.</code> \| <code>The men are fighting outside a deli.</code> \|
	\| <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> \| <code>Two kids in numbered jerseys wash their hands.</code> \| <code>Two kids in jackets walk to school.</code> \|
	\| <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code> \| <code>A man selling donuts to a customer.</code> \| <code>A woman drinks her coffee in a small cafe.</code> \|
	* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
	```json
	{
	"loss": "MultipleNegativesRankingLoss",
	"matryoshka_dims": [
	768,
	512,
	256,
	128,
	64
	],
	"matryoshka_weights": [
	1,
	1,
	1,
	1,
	1
	],
	"n_dims_per_step": -1
	}
	```

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

	- `eval_strategy`: steps
	- `per_device_train_batch_size`: 16
	- `per_device_eval_batch_size`: 16
	- `num_train_epochs`: 1
	- `warmup_ratio`: 0.1
	- `fp16`: 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`: 16
	- `per_device_eval_batch_size`: 16
	- `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`: 1.0
	- `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`: 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}
	- `tp_size`: 0
	- `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
	- `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`: no_duplicates
	- `multi_dataset_batch_sampler`: proportional

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \| Validation Loss \| sts-dev-768_spearman_cosine \| sts-dev-512_spearman_cosine \| sts-dev-256_spearman_cosine \| sts-dev-128_spearman_cosine \| sts-dev-64_spearman_cosine \| sts-test-768_spearman_cosine \| sts-test-512_spearman_cosine \| sts-test-256_spearman_cosine \| sts-test-128_spearman_cosine \| sts-test-64_spearman_cosine \|
	\|:------:\|:-----:\|:-------------:\|:---------------:\|:---------------------------:\|:---------------------------:\|:---------------------------:\|:---------------------------:\|:--------------------------:\|:----------------------------:\|:----------------------------:\|:----------------------------:\|:----------------------------:\|:---------------------------:\|
	\| 0.0459 \| 1600 \| 4.3243 \| 1.5267 \| 0.8525 \| 0.8475 \| 0.8438 \| 0.8356 \| 0.8155 \| - \| - \| - \| - \| - \|
	\| 0.0918 \| 3200 \| 2.4538 \| 1.4448 \| 0.8479 \| 0.8439 \| 0.8403 \| 0.8346 \| 0.8249 \| - \| - \| - \| - \| - \|
	\| 0.1377 \| 4800 \| 2.2829 \| 1.5117 \| 0.8507 \| 0.8481 \| 0.8429 \| 0.8348 \| 0.8203 \| - \| - \| - \| - \| - \|
	\| 0.1836 \| 6400 \| 2.0446 \| 1.2684 \| 0.8574 \| 0.8541 \| 0.8498 \| 0.8413 \| 0.8302 \| - \| - \| - \| - \| - \|
	\| 0.2294 \| 8000 \| 1.8867 \| 1.3107 \| 0.8452 \| 0.8423 \| 0.8400 \| 0.8352 \| 0.8255 \| - \| - \| - \| - \| - \|
	\| 0.2753 \| 9600 \| 1.747 \| 1.1663 \| 0.8456 \| 0.8420 \| 0.8384 \| 0.8292 \| 0.8229 \| - \| - \| - \| - \| - \|
	\| 0.3212 \| 11200 \| 1.6297 \| 1.0809 \| 0.8420 \| 0.8388 \| 0.8360 \| 0.8294 \| 0.8205 \| - \| - \| - \| - \| - \|
	\| 0.3671 \| 12800 \| 1.5974 \| 1.0853 \| 0.8374 \| 0.8352 \| 0.8310 \| 0.8264 \| 0.8184 \| - \| - \| - \| - \| - \|
	\| 0.4130 \| 14400 \| 1.5227 \| 1.0440 \| 0.8479 \| 0.8457 \| 0.8434 \| 0.8380 \| 0.8266 \| - \| - \| - \| - \| - \|
	\| 0.4589 \| 16000 \| 1.3835 \| 1.0718 \| 0.8365 \| 0.8341 \| 0.8310 \| 0.8258 \| 0.8172 \| - \| - \| - \| - \| - \|
	\| 0.5048 \| 17600 \| 1.3893 \| 1.0140 \| 0.8384 \| 0.8363 \| 0.8339 \| 0.8275 \| 0.8178 \| - \| - \| - \| - \| - \|
	\| 0.5507 \| 19200 \| 1.3203 \| 1.0048 \| 0.8418 \| 0.8400 \| 0.8364 \| 0.8292 \| 0.8204 \| - \| - \| - \| - \| - \|
	\| 0.5966 \| 20800 \| 1.2396 \| 0.9407 \| 0.8458 \| 0.8439 \| 0.8404 \| 0.8353 \| 0.8274 \| - \| - \| - \| - \| - \|
	\| 0.6425 \| 22400 \| 1.1842 \| 0.9541 \| 0.8435 \| 0.8404 \| 0.8384 \| 0.8335 \| 0.8257 \| - \| - \| - \| - \| - \|
	\| 0.6883 \| 24000 \| 1.1217 \| 0.9000 \| 0.8534 \| 0.8512 \| 0.8478 \| 0.8408 \| 0.8297 \| - \| - \| - \| - \| - \|
	\| 0.7342 \| 25600 \| 1.093 \| 0.8731 \| 0.8525 \| 0.8503 \| 0.8467 \| 0.8406 \| 0.8313 \| - \| - \| - \| - \| - \|
	\| 0.7801 \| 27200 \| 1.0609 \| 0.8238 \| 0.8528 \| 0.8510 \| 0.8469 \| 0.8399 \| 0.8312 \| - \| - \| - \| - \| - \|
	\| 0.8260 \| 28800 \| 0.9807 \| 0.8264 \| 0.8497 \| 0.8478 \| 0.8448 \| 0.8384 \| 0.8295 \| - \| - \| - \| - \| - \|
	\| 0.8719 \| 30400 \| 1.0061 \| 0.8135 \| 0.8455 \| 0.8439 \| 0.8405 \| 0.8338 \| 0.8256 \| - \| - \| - \| - \| - \|
	\| 0.9178 \| 32000 \| 0.9724 \| 0.7965 \| 0.8517 \| 0.8499 \| 0.8465 \| 0.8401 \| 0.8319 \| - \| - \| - \| - \| - \|
	\| 0.9637 \| 33600 \| 0.9057 \| 0.7841 \| 0.8509 \| 0.8493 \| 0.8463 \| 0.8396 \| 0.8314 \| - \| - \| - \| - \| - \|
	\| -1 \| -1 \| - \| - \| - \| - \| - \| - \| - \| 0.8359 \| 0.8362 \| 0.8332 \| 0.8262 \| 0.8194 \|


	### Environmental Impact
	Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
	- Energy Consumed: 0.529 kWh
	- Carbon Emitted: 0.206 kg of CO2
	- Hours Used: 2.452 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: 4.1.0.dev0
	- Transformers: 4.51.1
	- PyTorch: 2.6.0+cu124
	- Accelerate: 1.5.1
	- Datasets: 3.3.2
	- Tokenizers: 0.21.1

	## Citation

	### BibTeX

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

	#### MatryoshkaLoss
	```bibtex
	@misc{kusupati2024matryoshka,
	title={Matryoshka Representation Learning},
	author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
	year={2024},
	eprint={2205.13147},
	archivePrefix={arXiv},
	primaryClass={cs.LG}
	}
	```

	#### MultipleNegativesRankingLoss
	```bibtex
	@misc{henderson2017efficient,
	title={Efficient Natural Language Response Suggestion for Smart Reply},
	author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
	year={2017},
	eprint={1705.00652},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
	}
	```

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