Add new SentenceTransformer model.

ffad4be verified 5 months ago

29.2 kB

	---
	language:
	- en
	library_name: sentence-transformers
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- dataset_size:100K<n<1M
	- loss:MatryoshkaLoss
	- loss:MultipleNegativesRankingLoss
	base_model: distilbert/distilroberta-base
	metrics:
	- pearson_cosine
	- spearman_cosine
	- pearson_manhattan
	- spearman_manhattan
	- pearson_euclidean
	- spearman_euclidean
	- pearson_dot
	- spearman_dot
	- pearson_max
	- spearman_max
	widget:
	- source_sentence: He shrugged.
	sentences:
	- Then he shrugged.
	- Two people are dancing.
	- The people are Indian.
	- source_sentence: a young girl
	sentences:
	- A girl is playing.
	- A dog playing outside.
	- The men are moving.
	- source_sentence: girl sleeps
	sentences:
	- A little girl is sleep.
	- Two women are walking.
	- three men are pictured
	- source_sentence: He walked.
	sentences:
	- A man is moving around.
	- A young man is running.
	- What idiots girls are!
	- source_sentence: '''Go now.'''
	sentences:
	- Now go.
	- The door did not budge.
	- I never knew the man.
	pipeline_tag: sentence-similarity
	model-index:
	- name: SentenceTransformer based on distilbert/distilroberta-base
	results:
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts dev 768
	type: sts-dev-768
	metrics:
	- type: pearson_cosine
	value: 0.8418367310465795
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8485984004433933
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8356556933767024
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8341402433895243
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8378021883964464
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8364904078404392
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.7476524989991268
	name: Pearson Dot
	- type: spearman_dot
	value: 0.744450587024694
	name: Spearman Dot
	- type: pearson_max
	value: 0.8418367310465795
	name: Pearson Max
	- type: spearman_max
	value: 0.8485984004433933
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts dev 512
	type: sts-dev-512
	metrics:
	- type: pearson_cosine
	value: 0.8416891989714739
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8490082509626217
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8348187780435371
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8332638443518806
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.837008948364763
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8356608810942396
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.7426437744526075
	name: Pearson Dot
	- type: spearman_dot
	value: 0.7393063147821313
	name: Spearman Dot
	- type: pearson_max
	value: 0.8416891989714739
	name: Pearson Max
	- type: spearman_max
	value: 0.8490082509626217
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts dev 256
	type: sts-dev-256
	metrics:
	- type: pearson_cosine
	value: 0.8368212220308662
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8458532859579723
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8282949195581827
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8279757292284411
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8304309516656533
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8301347336633305
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.7158283880571648
	name: Pearson Dot
	- type: spearman_dot
	value: 0.7114038350641958
	name: Spearman Dot
	- type: pearson_max
	value: 0.8368212220308662
	name: Pearson Max
	- type: spearman_max
	value: 0.8458532859579723
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts dev 128
	type: sts-dev-128
	metrics:
	- type: pearson_cosine
	value: 0.8291552182220155
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8410315378567165
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8205197124842151
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8211956528048456
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8218377581296912
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8223376697977559
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.6736747525126793
	name: Pearson Dot
	- type: spearman_dot
	value: 0.6704632728499174
	name: Spearman Dot
	- type: pearson_max
	value: 0.8291552182220155
	name: Pearson Max
	- type: spearman_max
	value: 0.8410315378567165
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts dev 64
	type: sts-dev-64
	metrics:
	- type: pearson_cosine
	value: 0.8201110050860942
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.835036509147006
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8028297556674707
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8048509047037822
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8046682420071583
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8063788129340022
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.6171580093307325
	name: Pearson Dot
	- type: spearman_dot
	value: 0.6176751811391049
	name: Spearman Dot
	- type: pearson_max
	value: 0.8201110050860942
	name: Pearson Max
	- type: spearman_max
	value: 0.835036509147006
	name: Spearman Max
	---

	# SentenceTransformer based on distilbert/distilroberta-base

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) on the [sentence-transformers/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: [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) <!-- at revision fb53ab8802853c8e4fbdbcd0529f21fc6f459b2b -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 768 tokens
	- Similarity Function: Cosine Similarity
	- Training Dataset:
	- [sentence-transformers/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: RobertaModel
	(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("sentence_transformers_model_id")
	# Run inference
	sentences = [
	"'Go now.'",
	'Now go.',
	'The door did not budge.',
	]
	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]
	```

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	### Downstream Usage (Sentence Transformers)

	You can finetune this model on your own dataset.

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

	</details>
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	## Evaluation

	### Metrics

	#### Semantic Similarity
	* Dataset: `sts-dev-768`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.8418 \|
	\| spearman_cosine \| 0.8486 \|
	\| pearson_manhattan \| 0.8357 \|
	\| spearman_manhattan \| 0.8341 \|
	\| pearson_euclidean \| 0.8378 \|
	\| spearman_euclidean \| 0.8365 \|
	\| pearson_dot \| 0.7477 \|
	\| spearman_dot \| 0.7445 \|
	\| pearson_max \| 0.8418 \|
	\| spearman_max \| 0.8486 \|

	#### Semantic Similarity
	* Dataset: `sts-dev-512`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:----------\|
	\| pearson_cosine \| 0.8417 \|
	\| spearman_cosine \| 0.849 \|
	\| pearson_manhattan \| 0.8348 \|
	\| spearman_manhattan \| 0.8333 \|
	\| pearson_euclidean \| 0.837 \|
	\| spearman_euclidean \| 0.8357 \|
	\| pearson_dot \| 0.7426 \|
	\| spearman_dot \| 0.7393 \|
	\| pearson_max \| 0.8417 \|
	\| spearman_max \| 0.849 \|

	#### Semantic Similarity
	* Dataset: `sts-dev-256`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.8368 \|
	\| spearman_cosine \| 0.8459 \|
	\| pearson_manhattan \| 0.8283 \|
	\| spearman_manhattan \| 0.828 \|
	\| pearson_euclidean \| 0.8304 \|
	\| spearman_euclidean \| 0.8301 \|
	\| pearson_dot \| 0.7158 \|
	\| spearman_dot \| 0.7114 \|
	\| pearson_max \| 0.8368 \|
	\| spearman_max \| 0.8459 \|

	#### Semantic Similarity
	* Dataset: `sts-dev-128`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:----------\|
	\| pearson_cosine \| 0.8292 \|
	\| spearman_cosine \| 0.841 \|
	\| pearson_manhattan \| 0.8205 \|
	\| spearman_manhattan \| 0.8212 \|
	\| pearson_euclidean \| 0.8218 \|
	\| spearman_euclidean \| 0.8223 \|
	\| pearson_dot \| 0.6737 \|
	\| spearman_dot \| 0.6705 \|
	\| pearson_max \| 0.8292 \|
	\| spearman_max \| 0.841 \|

	#### Semantic Similarity
	* Dataset: `sts-dev-64`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:----------\|
	\| pearson_cosine \| 0.8201 \|
	\| spearman_cosine \| 0.835 \|
	\| pearson_manhattan \| 0.8028 \|
	\| spearman_manhattan \| 0.8049 \|
	\| pearson_euclidean \| 0.8047 \|
	\| spearman_euclidean \| 0.8064 \|
	\| pearson_dot \| 0.6172 \|
	\| spearman_dot \| 0.6177 \|
	\| pearson_max \| 0.8201 \|
	\| spearman_max \| 0.835 \|

	<!--
	## Bias, Risks and Limitations

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	## Training Details

	### Training Dataset

	#### sentence-transformers/all-nli

	* Dataset: [sentence-transformers/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: 10.38 tokens</li><li>max: 45 tokens</li></ul> \| <ul><li>min: 6 tokens</li><li>mean: 12.8 tokens</li><li>max: 39 tokens</li></ul> \| <ul><li>min: 6 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

	#### sentence-transformers/all-nli

	* Dataset: [sentence-transformers/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: 18.02 tokens</li><li>max: 66 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 9.81 tokens</li><li>max: 29 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 10.37 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`: 256
	- `per_device_eval_batch_size`: 256
	- `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`: 256
	- `per_device_eval_batch_size`: 256
	- `per_gpu_train_batch_size`: None
	- `per_gpu_eval_batch_size`: None
	- `gradient_accumulation_steps`: 1
	- `eval_accumulation_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`: 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 \| sts-dev-128_spearman_cosine \| sts-dev-256_spearman_cosine \| sts-dev-512_spearman_cosine \| sts-dev-64_spearman_cosine \| sts-dev-768_spearman_cosine \|
	\|:------:\|:----:\|:-------------:\|:------:\|:---------------------------:\|:---------------------------:\|:---------------------------:\|:--------------------------:\|:---------------------------:\|
	\| 0.0459 \| 100 \| 19.459 \| 8.2665 \| 0.7796 \| 0.8046 \| 0.8114 \| 0.8082 \| 0.7996 \|
	\| 0.0917 \| 200 \| 11.0035 \| 7.6606 \| 0.7696 \| 0.7971 \| 0.8083 \| 0.7987 \| 0.7933 \|
	\| 0.1376 \| 300 \| 9.7634 \| 6.4912 \| 0.7992 \| 0.8126 \| 0.8190 \| 0.8062 \| 0.8127 \|
	\| 0.1835 \| 400 \| 9.1103 \| 5.9960 \| 0.8081 \| 0.8229 \| 0.8263 \| 0.8136 \| 0.8224 \|
	\| 0.2294 \| 500 \| 8.7099 \| 5.9388 \| 0.7984 \| 0.8138 \| 0.8189 \| 0.8021 \| 0.8166 \|
	\| 0.2752 \| 600 \| 8.1215 \| 5.6457 \| 0.7963 \| 0.8104 \| 0.8149 \| 0.8057 \| 0.8121 \|
	\| 0.3211 \| 700 \| 7.7441 \| 5.4632 \| 0.7937 \| 0.8153 \| 0.8199 \| 0.8119 \| 0.8150 \|
	\| 0.3670 \| 800 \| 7.4849 \| 5.1815 \| 0.8076 \| 0.8208 \| 0.8238 \| 0.8152 \| 0.8172 \|
	\| 0.4128 \| 900 \| 7.1386 \| 5.1419 \| 0.8035 \| 0.8181 \| 0.8235 \| 0.8139 \| 0.8189 \|
	\| 0.4587 \| 1000 \| 6.839 \| 5.1548 \| 0.7943 \| 0.8118 \| 0.8172 \| 0.8054 \| 0.8153 \|
	\| 0.5046 \| 1100 \| 6.6597 \| 5.1015 \| 0.7895 \| 0.8066 \| 0.8119 \| 0.8059 \| 0.8063 \|
	\| 0.5505 \| 1200 \| 6.7172 \| 5.3707 \| 0.7753 \| 0.7987 \| 0.8068 \| 0.7989 \| 0.8014 \|
	\| 0.5963 \| 1300 \| 6.6514 \| 4.9368 \| 0.7904 \| 0.8086 \| 0.8139 \| 0.8051 \| 0.8083 \|
	\| 0.6422 \| 1400 \| 6.5573 \| 5.0196 \| 0.7882 \| 0.8066 \| 0.8128 \| 0.8035 \| 0.8091 \|
	\| 0.6881 \| 1500 \| 6.7596 \| 4.9381 \| 0.7960 \| 0.8120 \| 0.8169 \| 0.8058 \| 0.8140 \|
	\| 0.7339 \| 1600 \| 6.2686 \| 4.4018 \| 0.8136 \| 0.8245 \| 0.8268 \| 0.8160 \| 0.8244 \|
	\| 0.7798 \| 1700 \| 3.4607 \| 3.8397 \| 0.8415 \| 0.8466 \| 0.8502 \| 0.8345 \| 0.8503 \|
	\| 0.8257 \| 1800 \| 2.6912 \| 3.7914 \| 0.8415 \| 0.8459 \| 0.8493 \| 0.8350 \| 0.8488 \|
	\| 0.8716 \| 1900 \| 2.4958 \| 3.7752 \| 0.8402 \| 0.8450 \| 0.8484 \| 0.8340 \| 0.8478 \|
	\| 0.9174 \| 2000 \| 2.3413 \| 3.7997 \| 0.8410 \| 0.8459 \| 0.8490 \| 0.8350 \| 0.8486 \|


	### Framework Versions
	- Python: 3.10.12
	- Sentence Transformers: 3.0.0
	- Transformers: 4.41.1
	- PyTorch: 2.3.0+cu121
	- 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",
	}
	```

	#### 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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