AdaptiveLayerLoss(model=model,

f0c76d4 verified 5 months ago

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	---
	language:
	- en
	library_name: sentence-transformers
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:67190
	- loss:AdaptiveLayerLoss
	- loss:MultipleNegativesRankingLoss
	base_model: microsoft/deberta-v3-small
	datasets:
	- stanfordnlp/snli
	metrics:
	- cosine_accuracy
	- cosine_accuracy_threshold
	- cosine_f1
	- cosine_f1_threshold
	- cosine_precision
	- cosine_recall
	- cosine_ap
	- dot_accuracy
	- dot_accuracy_threshold
	- dot_f1
	- dot_f1_threshold
	- dot_precision
	- dot_recall
	- dot_ap
	- manhattan_accuracy
	- manhattan_accuracy_threshold
	- manhattan_f1
	- manhattan_f1_threshold
	- manhattan_precision
	- manhattan_recall
	- manhattan_ap
	- euclidean_accuracy
	- euclidean_accuracy_threshold
	- euclidean_f1
	- euclidean_f1_threshold
	- euclidean_precision
	- euclidean_recall
	- euclidean_ap
	- max_accuracy
	- max_accuracy_threshold
	- max_f1
	- max_f1_threshold
	- max_precision
	- max_recall
	- max_ap
	- pearson_cosine
	- spearman_cosine
	- pearson_manhattan
	- spearman_manhattan
	- pearson_euclidean
	- spearman_euclidean
	- pearson_dot
	- spearman_dot
	- pearson_max
	- spearman_max
	widget:
	- source_sentence: A worker peers out from atop a building under construction.
	sentences:
	- The man pleads for mercy.
	- People and a baby crossing the street.
	- A person is atop of a building.
	- source_sentence: An aisle at Best Buy with an employee standing at the computer
	and a Geek Squad sign in the background.
	sentences:
	- the man is watching the stars
	- The employee is wearing a blue shirt.
	- A person balancing.
	- source_sentence: A man with a long white beard is examining a camera and another
	man with a black shirt is in the background.
	sentences:
	- a man is with another man
	- Children in uniforms climb a tower.
	- There are five children.
	- source_sentence: A black dog with a blue collar is jumping into the water.
	sentences:
	- The dog is playing tug of war with a stick.
	- There is a woman painting.
	- A black dog wearing a blue collar is chasing something into the water.
	- source_sentence: A wet child stands in chest deep ocean water.
	sentences:
	- A woman paints a portrait of her best friend.
	- A person in red is cutting the grass on a riding mower
	- The child s playing on the beach.
	pipeline_tag: sentence-similarity
	model-index:
	- name: SentenceTransformer based on microsoft/deberta-v3-small
	results:
	- task:
	type: binary-classification
	name: Binary Classification
	dataset:
	name: Unknown
	type: unknown
	metrics:
	- type: cosine_accuracy
	value: 0.6583157259281618
	name: Cosine Accuracy
	- type: cosine_accuracy_threshold
	value: 0.6766541004180908
	name: Cosine Accuracy Threshold
	- type: cosine_f1
	value: 0.7049362860324137
	name: Cosine F1
	- type: cosine_f1_threshold
	value: 0.6017583012580872
	name: Cosine F1 Threshold
	- type: cosine_precision
	value: 0.6115046147241897
	name: Cosine Precision
	- type: cosine_recall
	value: 0.8320677570093458
	name: Cosine Recall
	- type: cosine_ap
	value: 0.6995030811464378
	name: Cosine Ap
	- type: dot_accuracy
	value: 0.6272260790824027
	name: Dot Accuracy
	- type: dot_accuracy_threshold
	value: 163.25054931640625
	name: Dot Accuracy Threshold
	- type: dot_f1
	value: 0.6976381461675579
	name: Dot F1
	- type: dot_f1_threshold
	value: 119.20779418945312
	name: Dot F1 Threshold
	- type: dot_precision
	value: 0.5639409221902018
	name: Dot Precision
	- type: dot_recall
	value: 0.914427570093458
	name: Dot Recall
	- type: dot_ap
	value: 0.643747511442345
	name: Dot Ap
	- type: manhattan_accuracy
	value: 0.6571083610021129
	name: Manhattan Accuracy
	- type: manhattan_accuracy_threshold
	value: 243.75453186035156
	name: Manhattan Accuracy Threshold
	- type: manhattan_f1
	value: 0.7055783910745744
	name: Manhattan F1
	- type: manhattan_f1_threshold
	value: 295.95947265625
	name: Manhattan F1 Threshold
	- type: manhattan_precision
	value: 0.5900608917697898
	name: Manhattan Precision
	- type: manhattan_recall
	value: 0.8773364485981309
	name: Manhattan Recall
	- type: manhattan_ap
	value: 0.7072033306346501
	name: Manhattan Ap
	- type: euclidean_accuracy
	value: 0.6590703290069424
	name: Euclidean Accuracy
	- type: euclidean_accuracy_threshold
	value: 12.141830444335938
	name: Euclidean Accuracy Threshold
	- type: euclidean_f1
	value: 0.7036813518406759
	name: Euclidean F1
	- type: euclidean_f1_threshold
	value: 14.197540283203125
	name: Euclidean F1 Threshold
	- type: euclidean_precision
	value: 0.5996708496194199
	name: Euclidean Precision
	- type: euclidean_recall
	value: 0.8513434579439252
	name: Euclidean Recall
	- type: euclidean_ap
	value: 0.7035256676322055
	name: Euclidean Ap
	- type: max_accuracy
	value: 0.6590703290069424
	name: Max Accuracy
	- type: max_accuracy_threshold
	value: 243.75453186035156
	name: Max Accuracy Threshold
	- type: max_f1
	value: 0.7055783910745744
	name: Max F1
	- type: max_f1_threshold
	value: 295.95947265625
	name: Max F1 Threshold
	- type: max_precision
	value: 0.6115046147241897
	name: Max Precision
	- type: max_recall
	value: 0.914427570093458
	name: Max Recall
	- type: max_ap
	value: 0.7072033306346501
	name: Max Ap
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: Unknown
	type: unknown
	metrics:
	- type: pearson_cosine
	value: 0.732169941341086
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.7344587206087978
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.7537099624360986
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.7550555196955944
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.7468210439584286
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.74849026008206
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.6142835401925993
	name: Pearson Dot
	- type: spearman_dot
	value: 0.6100201108417316
	name: Spearman Dot
	- type: pearson_max
	value: 0.7537099624360986
	name: Pearson Max
	- type: spearman_max
	value: 0.7550555196955944
	name: Spearman Max
	---

	# SentenceTransformer based on microsoft/deberta-v3-small

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the [stanfordnlp/snli](https://huggingface.co/datasets/stanfordnlp/snli) 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/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) <!-- at revision a36c739020e01763fe789b4b85e2df55d6180012 -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 768 tokens
	- Similarity Function: Cosine Similarity
	- Training Dataset:
	- [stanfordnlp/snli](https://huggingface.co/datasets/stanfordnlp/snli)
	- 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: DebertaV2Model
	(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("bobox/DeBERTaV3-small-ST-AdaptiveLayers-ep2")
	# Run inference
	sentences = [
	'A wet child stands in chest deep ocean water.',
	'The child s playing on the beach.',
	'A woman paints a portrait of her best friend.',
	]
	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

	#### Binary Classification

	* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)

	\| Metric \| Value \|
	\|:-----------------------------\|:-----------\|
	\| cosine_accuracy \| 0.6583 \|
	\| cosine_accuracy_threshold \| 0.6767 \|
	\| cosine_f1 \| 0.7049 \|
	\| cosine_f1_threshold \| 0.6018 \|
	\| cosine_precision \| 0.6115 \|
	\| cosine_recall \| 0.8321 \|
	\| cosine_ap \| 0.6995 \|
	\| dot_accuracy \| 0.6272 \|
	\| dot_accuracy_threshold \| 163.2505 \|
	\| dot_f1 \| 0.6976 \|
	\| dot_f1_threshold \| 119.2078 \|
	\| dot_precision \| 0.5639 \|
	\| dot_recall \| 0.9144 \|
	\| dot_ap \| 0.6437 \|
	\| manhattan_accuracy \| 0.6571 \|
	\| manhattan_accuracy_threshold \| 243.7545 \|
	\| manhattan_f1 \| 0.7056 \|
	\| manhattan_f1_threshold \| 295.9595 \|
	\| manhattan_precision \| 0.5901 \|
	\| manhattan_recall \| 0.8773 \|
	\| manhattan_ap \| 0.7072 \|
	\| euclidean_accuracy \| 0.6591 \|
	\| euclidean_accuracy_threshold \| 12.1418 \|
	\| euclidean_f1 \| 0.7037 \|
	\| euclidean_f1_threshold \| 14.1975 \|
	\| euclidean_precision \| 0.5997 \|
	\| euclidean_recall \| 0.8513 \|
	\| euclidean_ap \| 0.7035 \|
	\| max_accuracy \| 0.6591 \|
	\| max_accuracy_threshold \| 243.7545 \|
	\| max_f1 \| 0.7056 \|
	\| max_f1_threshold \| 295.9595 \|
	\| max_precision \| 0.6115 \|
	\| max_recall \| 0.9144 \|
	\| max_ap \| 0.7072 \|

	#### Semantic Similarity

	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.7322 \|
	\| spearman_cosine \| 0.7345 \|
	\| pearson_manhattan \| 0.7537 \|
	\| spearman_manhattan \| 0.7551 \|
	\| pearson_euclidean \| 0.7468 \|
	\| spearman_euclidean \| 0.7485 \|
	\| pearson_dot \| 0.6143 \|
	\| spearman_dot \| 0.61 \|
	\| pearson_max \| 0.7537 \|
	\| spearman_max \| 0.7551 \|

	<!--
	## 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

	#### stanfordnlp/snli

	* Dataset: [stanfordnlp/snli](https://huggingface.co/datasets/stanfordnlp/snli) at [cdb5c3d](https://huggingface.co/datasets/stanfordnlp/snli/tree/cdb5c3d5eed6ead6e5a341c8e56e669bb666725b)
	* Size: 67,190 training samples
	* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence1 \| sentence2 \| label \|
	\|:--------\|:-----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:-----------------------------\|
	\| type \| string \| string \| int \|
	\| details \| <ul><li>min: 4 tokens</li><li>mean: 21.19 tokens</li><li>max: 133 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 11.77 tokens</li><li>max: 49 tokens</li></ul> \| <ul><li>0: 100.00%</li></ul> \|
	* Samples:
	\| sentence1 \| sentence2 \| label \|
	\|:---------------------------------------------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------\|:---------------\|
	\| <code>Without a placebo group, we still won't know if any of the treatments are better than nothing and therefore worth giving.</code> \| <code>It is necessary to use a controlled method to ensure the treatments are worthwhile.</code> \| <code>0</code> \|
	\| <code>It was conducted in silence.</code> \| <code>It was done silently.</code> \| <code>0</code> \|
	\| <code>oh Lewisville any decent food in your cafeteria up there</code> \| <code>Is there any decent food in your cafeteria up there in Lewisville?</code> \| <code>0</code> \|
	* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters:
	```json
	{
	"loss": "MultipleNegativesRankingLoss",
	"n_layers_per_step": 1,
	"last_layer_weight": 1,
	"prior_layers_weight": 1,
	"kl_div_weight": 1,
	"kl_temperature": 1
	}
	```

	### Evaluation Dataset

	#### stanfordnlp/snli

	* Dataset: [stanfordnlp/snli](https://huggingface.co/datasets/stanfordnlp/snli) at [cdb5c3d](https://huggingface.co/datasets/stanfordnlp/snli/tree/cdb5c3d5eed6ead6e5a341c8e56e669bb666725b)
	* Size: 1,500 evaluation samples
	* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence1 \| sentence2 \| score \|
	\|:--------\|:----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:---------------------------------------------------------------\|
	\| type \| string \| string \| float \|
	\| details \| <ul><li>min: 5 tokens</li><li>mean: 14.77 tokens</li><li>max: 45 tokens</li></ul> \| <ul><li>min: 6 tokens</li><li>mean: 14.74 tokens</li><li>max: 49 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 0.47</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| sentence1 \| sentence2 \| score \|
	\|:--------------------------------------------------\|:------------------------------------------------------\|:------------------\|
	\| <code>A man with a hard hat is dancing.</code> \| <code>A man wearing a hard hat is dancing.</code> \| <code>1.0</code> \|
	\| <code>A young child is riding a horse.</code> \| <code>A child is riding a horse.</code> \| <code>0.95</code> \|
	\| <code>A man is feeding a mouse to a snake.</code> \| <code>The man is feeding a mouse to the snake.</code> \| <code>1.0</code> \|
	* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters:
	```json
	{
	"loss": "MultipleNegativesRankingLoss",
	"n_layers_per_step": 1,
	"last_layer_weight": 1,
	"prior_layers_weight": 1,
	"kl_div_weight": 1,
	"kl_temperature": 1
	}
	```

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

	- `eval_strategy`: steps
	- `per_device_train_batch_size`: 42
	- `per_device_eval_batch_size`: 22
	- `learning_rate`: 3e-06
	- `weight_decay`: 1e-08
	- `num_train_epochs`: 2
	- `lr_scheduler_type`: cosine
	- `warmup_ratio`: 0.5
	- `save_safetensors`: False
	- `fp16`: True
	- `hub_model_id`: bobox/DeBERTaV3-small-ST-AdaptiveLayers-ep2-tmp
	- `hub_strategy`: checkpoint
	- `hub_private_repo`: 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`: 42
	- `per_device_eval_batch_size`: 22
	- `per_gpu_train_batch_size`: None
	- `per_gpu_eval_batch_size`: None
	- `gradient_accumulation_steps`: 1
	- `eval_accumulation_steps`: None
	- `learning_rate`: 3e-06
	- `weight_decay`: 1e-08
	- `adam_beta1`: 0.9
	- `adam_beta2`: 0.999
	- `adam_epsilon`: 1e-08
	- `max_grad_norm`: 1.0
	- `num_train_epochs`: 2
	- `max_steps`: -1
	- `lr_scheduler_type`: cosine
	- `lr_scheduler_kwargs`: {}
	- `warmup_ratio`: 0.5
	- `warmup_steps`: 0
	- `log_level`: passive
	- `log_level_replica`: warning
	- `log_on_each_node`: True
	- `logging_nan_inf_filter`: True
	- `save_safetensors`: False
	- `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`: bobox/DeBERTaV3-small-ST-AdaptiveLayers-ep2-tmp
	- `hub_strategy`: checkpoint
	- `hub_private_repo`: True
	- `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 \| max_ap \| spearman_cosine \|
	\|:-----:\|:----:\|:-------------:\|:------:\|:------:\|:---------------:\|
	\| 0.1 \| 160 \| 4.6003 \| 4.8299 \| 0.6017 \| - \|
	\| 0.2 \| 320 \| 4.0659 \| 4.3436 \| 0.6168 \| - \|
	\| 0.3 \| 480 \| 3.4886 \| 4.0840 \| 0.6339 \| - \|
	\| 0.4 \| 640 \| 3.0592 \| 3.6422 \| 0.6611 \| - \|
	\| 0.5 \| 800 \| 2.5728 \| 3.1927 \| 0.6773 \| - \|
	\| 0.6 \| 960 \| 2.184 \| 2.8322 \| 0.6893 \| - \|
	\| 0.7 \| 1120 \| 1.8744 \| 2.4892 \| 0.6954 \| - \|
	\| 0.8 \| 1280 \| 1.757 \| 2.4453 \| 0.7002 \| - \|
	\| 0.9 \| 1440 \| 1.5872 \| 2.2565 \| 0.7010 \| - \|
	\| 1.0 \| 1600 \| 1.446 \| 2.1391 \| 0.7046 \| - \|
	\| 1.1 \| 1760 \| 1.3892 \| 2.1236 \| 0.7058 \| - \|
	\| 1.2 \| 1920 \| 1.2567 \| 1.9738 \| 0.7053 \| - \|
	\| 1.3 \| 2080 \| 1.2233 \| 1.8925 \| 0.7063 \| - \|
	\| 1.4 \| 2240 \| 1.1954 \| 1.8392 \| 0.7075 \| - \|
	\| 1.5 \| 2400 \| 1.1395 \| 1.9081 \| 0.7065 \| - \|
	\| 1.6 \| 2560 \| 1.1211 \| 1.8080 \| 0.7074 \| - \|
	\| 1.7 \| 2720 \| 1.0825 \| 1.8408 \| 0.7073 \| - \|
	\| 1.8 \| 2880 \| 1.1358 \| 1.7363 \| 0.7073 \| - \|
	\| 1.9 \| 3040 \| 1.0628 \| 1.8936 \| 0.7072 \| - \|
	\| 2.0 \| 3200 \| 1.1412 \| 1.7846 \| 0.7072 \| - \|
	\| None \| 0 \| - \| 3.0121 \| 0.7072 \| 0.7345 \|


	### Framework Versions
	- Python: 3.10.13
	- Sentence Transformers: 3.0.1
	- Transformers: 4.41.2
	- PyTorch: 2.1.2
	- Accelerate: 0.30.1
	- Datasets: 2.19.2
	- 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",
	}
	```

	#### AdaptiveLayerLoss
	```bibtex
	@misc{li20242d,
	title={2D Matryoshka Sentence Embeddings},
	author={Xianming Li and Zongxi Li and Jing Li and Haoran Xie and Qing Li},
	year={2024},
	eprint={2402.14776},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
	}
	```

	#### 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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