CocoRoF/ModernBERT-SimCSE-multitask_v03-retry

8ea8efa verified 15 days ago

23.5 kB

	---
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:5749
	- loss:CosineSimilarityLoss
	base_model: CocoRoF/mobert_retry_SimCSE_test
	widget:
	- source_sentence: 우리는 움직이는 동행 우주 정지 좌표계에 비례하여 이동하고 있습니다 ... 약 371km / s에서 별자리 leo
	쪽으로. "
	sentences:
	- 두 마리의 독수리가 가지에 앉는다.
	- 다른 물체와는 관련이 없는 '정지'는 없다.
	- 소녀는 버스의 열린 문 앞에 서 있다.
	- source_sentence: 숲에는 개들이 있다.
	sentences:
	- 양을 보는 아이들.
	- 여왕의 배우자를 "왕"이라고 부르지 않는 것은 아주 좋은 이유가 있다. 왜냐하면 그들은 왕이 아니기 때문이다.
	- 개들은 숲속에 혼자 있다.
	- source_sentence: '첫째, 두 가지 다른 종류의 대시가 있다는 것을 알아야 합니다 : en 대시와 em 대시.'
	sentences:
	- 그들은 그 물건들을 집 주변에 두고 가거나 집의 정리를 해칠 의도가 없다.
	- 세미콜론은 혼자 있을 수 있는 문장에 참여하는데 사용되지만, 그들의 관계를 강조하기 위해 결합됩니다.
	- 그의 남동생이 지켜보는 동안 집 앞에서 트럼펫을 연주하는 금발의 아이.
	- source_sentence: 한 여성이 생선 껍질을 벗기고 있다.
	sentences:
	- 한 남자가 수영장으로 뛰어들었다.
	- 한 여성이 프라이팬에 노란 혼합물을 부어 넣고 있다.
	- 두 마리의 갈색 개가 눈 속에서 서로 놀고 있다.
	- source_sentence: 버스가 바쁜 길을 따라 운전한다.
	sentences:
	- 우리와 같은 태양계가 은하계 밖에서 존재할 수도 있을 것입니다.
	- 그 여자는 데이트하러 가는 중이다.
	- 녹색 버스가 도로를 따라 내려간다.
	pipeline_tag: sentence-similarity
	library_name: sentence-transformers
	metrics:
	- pearson_cosine
	- spearman_cosine
	- pearson_euclidean
	- spearman_euclidean
	- pearson_manhattan
	- spearman_manhattan
	- pearson_dot
	- spearman_dot
	- pearson_max
	- spearman_max
	model-index:
	- name: SentenceTransformer based on CocoRoF/mobert_retry_SimCSE_test
	results:
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts dev
	type: sts_dev
	metrics:
	- type: pearson_cosine
	value: 0.7885728442437165
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.7890106880187878
	name: Spearman Cosine
	- type: pearson_euclidean
	value: 0.7209624590910948
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.7132906703480484
	name: Spearman Euclidean
	- type: pearson_manhattan
	value: 0.7228003273015342
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.7161151111265872
	name: Spearman Manhattan
	- type: pearson_dot
	value: 0.7119673656141701
	name: Pearson Dot
	- type: spearman_dot
	value: 0.7059066541365785
	name: Spearman Dot
	- type: pearson_max
	value: 0.7885728442437165
	name: Pearson Max
	- type: spearman_max
	value: 0.7890106880187878
	name: Spearman Max
	---

	# SentenceTransformer based on CocoRoF/mobert_retry_SimCSE_test

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [CocoRoF/mobert_retry_SimCSE_test](https://huggingface.co/CocoRoF/mobert_retry_SimCSE_test). It maps sentences & paragraphs to a 1024-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: [CocoRoF/mobert_retry_SimCSE_test](https://huggingface.co/CocoRoF/mobert_retry_SimCSE_test) <!-- at revision 94f4e00947539b6741c4a31b977a66220298317d -->
	- Maximum Sequence Length: 2048 tokens
	- Output Dimensionality: 1024 dimensions
	- Similarity Function: Cosine Similarity
	<!-- - Training Dataset: Unknown -->
	<!-- - Language: Unknown -->
	<!-- - 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': 2048, 'do_lower_case': False}) with Transformer model: ModernBertModel
	(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})
	(2): Dense({'in_features': 768, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
	)
	```

	## 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("CocoRoF/ModernBERT-SimCSE-multitask_v03-retry")
	# Run inference
	sentences = [
	'버스가 바쁜 길을 따라 운전한다.',
	'녹색 버스가 도로를 따라 내려간다.',
	'그 여자는 데이트하러 가는 중이다.',
	]
	embeddings = model.encode(sentences)
	print(embeddings.shape)
	# [3, 1024]

	# 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

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

	\| Metric \| Value \|
	\|:-------------------\|:----------\|
	\| pearson_cosine \| 0.7886 \|
	\| spearman_cosine \| 0.789 \|
	\| pearson_euclidean \| 0.721 \|
	\| spearman_euclidean \| 0.7133 \|
	\| pearson_manhattan \| 0.7228 \|
	\| spearman_manhattan \| 0.7161 \|
	\| pearson_dot \| 0.712 \|
	\| spearman_dot \| 0.7059 \|
	\| pearson_max \| 0.7886 \|
	\| spearman_max \| 0.789 \|

	<!--
	## 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

	#### Unnamed Dataset

	* Size: 5,749 training 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: 7 tokens</li><li>mean: 13.52 tokens</li><li>max: 36 tokens</li></ul> \| <ul><li>min: 7 tokens</li><li>mean: 13.41 tokens</li><li>max: 32 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 0.45</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| sentence1 \| sentence2 \| score \|
	\|:------------------------------------\|:------------------------------------------\|:------------------\|
	\| <code>비행기가 이륙하고 있다.</code> \| <code>비행기가 이륙하고 있다.</code> \| <code>1.0</code> \|
	\| <code>한 남자가 큰 플루트를 연주하고 있다.</code> \| <code>남자가 플루트를 연주하고 있다.</code> \| <code>0.76</code> \|
	\| <code>한 남자가 피자에 치즈를 뿌려놓고 있다.</code> \| <code>한 남자가 구운 피자에 치즈 조각을 뿌려놓고 있다.</code> \| <code>0.76</code> \|
	* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
	```json
	{
	"loss_fct": "torch.nn.modules.loss.MSELoss"
	}
	```

	### Evaluation Dataset

	#### Unnamed Dataset

	* 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: 7 tokens</li><li>mean: 20.38 tokens</li><li>max: 52 tokens</li></ul> \| <ul><li>min: 6 tokens</li><li>mean: 20.52 tokens</li><li>max: 54 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| sentence1 \| sentence2 \| score \|
	\|:-------------------------------------\|:------------------------------------\|:------------------\|
	\| <code>안전모를 가진 한 남자가 춤을 추고 있다.</code> \| <code>안전모를 쓴 한 남자가 춤을 추고 있다.</code> \| <code>1.0</code> \|
	\| <code>어린아이가 말을 타고 있다.</code> \| <code>아이가 말을 타고 있다.</code> \| <code>0.95</code> \|
	\| <code>한 남자가 뱀에게 쥐를 먹이고 있다.</code> \| <code>남자가 뱀에게 쥐를 먹이고 있다.</code> \| <code>1.0</code> \|
	* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
	```json
	{
	"loss_fct": "torch.nn.modules.loss.MSELoss"
	}
	```

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

	- `overwrite_output_dir`: True
	- `eval_strategy`: steps
	- `per_device_train_batch_size`: 1
	- `per_device_eval_batch_size`: 1
	- `gradient_accumulation_steps`: 16
	- `learning_rate`: 8e-05
	- `num_train_epochs`: 10.0
	- `warmup_ratio`: 0.2
	- `push_to_hub`: True
	- `hub_model_id`: CocoRoF/ModernBERT-SimCSE-multitask_v03-retry
	- `hub_strategy`: checkpoint
	- `batch_sampler`: no_duplicates

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

	- `overwrite_output_dir`: True
	- `do_predict`: False
	- `eval_strategy`: steps
	- `prediction_loss_only`: True
	- `per_device_train_batch_size`: 1
	- `per_device_eval_batch_size`: 1
	- `per_gpu_train_batch_size`: None
	- `per_gpu_eval_batch_size`: None
	- `gradient_accumulation_steps`: 16
	- `eval_accumulation_steps`: None
	- `torch_empty_cache_steps`: None
	- `learning_rate`: 8e-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`: 10.0
	- `max_steps`: -1
	- `lr_scheduler_type`: linear
	- `lr_scheduler_kwargs`: {}
	- `warmup_ratio`: 0.2
	- `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`: 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`: True
	- `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`: True
	- `resume_from_checkpoint`: None
	- `hub_model_id`: CocoRoF/ModernBERT-SimCSE-multitask_v03-retry
	- `hub_strategy`: checkpoint
	- `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
	- `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
	- `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_spearman_max \|
	\|:------:\|:----:\|:-------------:\|:---------------:\|:--------------------:\|
	\| 0.1114 \| 5 \| - \| 0.0377 \| 0.7471 \|
	\| 0.2228 \| 10 \| 0.6923 \| 0.0377 \| 0.7471 \|
	\| 0.3343 \| 15 \| - \| 0.0376 \| 0.7473 \|
	\| 0.4457 \| 20 \| 0.6832 \| 0.0376 \| 0.7475 \|
	\| 0.5571 \| 25 \| - \| 0.0375 \| 0.7479 \|
	\| 0.6685 \| 30 \| 0.6787 \| 0.0375 \| 0.7484 \|
	\| 0.7799 \| 35 \| - \| 0.0374 \| 0.7488 \|
	\| 0.8914 \| 40 \| 0.6154 \| 0.0373 \| 0.7494 \|
	\| 1.0223 \| 45 \| - \| 0.0372 \| 0.7500 \|
	\| 1.1337 \| 50 \| 0.6231 \| 0.0371 \| 0.7506 \|
	\| 1.2451 \| 55 \| - \| 0.0370 \| 0.7512 \|
	\| 1.3565 \| 60 \| 0.6562 \| 0.0369 \| 0.7519 \|
	\| 1.4680 \| 65 \| - \| 0.0368 \| 0.7526 \|
	\| 1.5794 \| 70 \| 0.6578 \| 0.0366 \| 0.7534 \|
	\| 1.6908 \| 75 \| - \| 0.0365 \| 0.7541 \|
	\| 1.8022 \| 80 \| 0.6669 \| 0.0364 \| 0.7549 \|
	\| 1.9136 \| 85 \| - \| 0.0363 \| 0.7559 \|
	\| 2.0446 \| 90 \| 0.6428 \| 0.0361 \| 0.7568 \|
	\| 2.1560 \| 95 \| - \| 0.0360 \| 0.7577 \|
	\| 2.2674 \| 100 \| 0.5854 \| 0.0358 \| 0.7586 \|
	\| 2.3788 \| 105 \| - \| 0.0357 \| 0.7597 \|
	\| 2.4903 \| 110 \| 0.6027 \| 0.0356 \| 0.7607 \|
	\| 2.6017 \| 115 \| - \| 0.0354 \| 0.7618 \|
	\| 2.7131 \| 120 \| 0.6375 \| 0.0353 \| 0.7627 \|
	\| 2.8245 \| 125 \| - \| 0.0351 \| 0.7635 \|
	\| 2.9359 \| 130 \| 0.6204 \| 0.0350 \| 0.7643 \|
	\| 3.0669 \| 135 \| - \| 0.0348 \| 0.7653 \|
	\| 3.1783 \| 140 \| 0.6077 \| 0.0347 \| 0.7663 \|
	\| 3.2897 \| 145 \| - \| 0.0346 \| 0.7672 \|
	\| 3.4011 \| 150 \| 0.5772 \| 0.0344 \| 0.7681 \|
	\| 3.5125 \| 155 \| - \| 0.0343 \| 0.7690 \|
	\| 3.6240 \| 160 \| 0.5793 \| 0.0341 \| 0.7698 \|
	\| 3.7354 \| 165 \| - \| 0.0340 \| 0.7705 \|
	\| 3.8468 \| 170 \| 0.5807 \| 0.0338 \| 0.7712 \|
	\| 3.9582 \| 175 \| - \| 0.0337 \| 0.7721 \|
	\| 4.0891 \| 180 \| 0.5576 \| 0.0336 \| 0.7729 \|
	\| 4.2006 \| 185 \| - \| 0.0334 \| 0.7734 \|
	\| 4.3120 \| 190 \| 0.5244 \| 0.0333 \| 0.7740 \|
	\| 4.4234 \| 195 \| - \| 0.0332 \| 0.7748 \|
	\| 4.5348 \| 200 \| 0.539 \| 0.0331 \| 0.7754 \|
	\| 4.6462 \| 205 \| - \| 0.0330 \| 0.7760 \|
	\| 4.7577 \| 210 \| 0.5517 \| 0.0329 \| 0.7765 \|
	\| 4.8691 \| 215 \| - \| 0.0328 \| 0.7769 \|
	\| 4.9805 \| 220 \| 0.5265 \| 0.0327 \| 0.7776 \|
	\| 5.1114 \| 225 \| - \| 0.0326 \| 0.7780 \|
	\| 5.2228 \| 230 \| 0.5285 \| 0.0325 \| 0.7783 \|
	\| 5.3343 \| 235 \| - \| 0.0324 \| 0.7789 \|
	\| 5.4457 \| 240 \| 0.4697 \| 0.0323 \| 0.7793 \|
	\| 5.5571 \| 245 \| - \| 0.0323 \| 0.7798 \|
	\| 5.6685 \| 250 \| 0.4913 \| 0.0322 \| 0.7804 \|
	\| 5.7799 \| 255 \| - \| 0.0321 \| 0.7809 \|
	\| 5.8914 \| 260 \| 0.5253 \| 0.0320 \| 0.7813 \|
	\| 6.0223 \| 265 \| - \| 0.0320 \| 0.7817 \|
	\| 6.1337 \| 270 \| 0.4924 \| 0.0319 \| 0.7819 \|
	\| 6.2451 \| 275 \| - \| 0.0318 \| 0.7820 \|
	\| 6.3565 \| 280 \| 0.4844 \| 0.0317 \| 0.7822 \|
	\| 6.4680 \| 285 \| - \| 0.0317 \| 0.7825 \|
	\| 6.5794 \| 290 \| 0.442 \| 0.0316 \| 0.7827 \|
	\| 6.6908 \| 295 \| - \| 0.0315 \| 0.7830 \|
	\| 6.8022 \| 300 \| 0.4665 \| 0.0314 \| 0.7834 \|
	\| 6.9136 \| 305 \| - \| 0.0314 \| 0.7839 \|
	\| 7.0446 \| 310 \| 0.4672 \| 0.0314 \| 0.7843 \|
	\| 7.1560 \| 315 \| - \| 0.0314 \| 0.7851 \|
	\| 7.2674 \| 320 \| 0.4131 \| 0.0314 \| 0.7850 \|
	\| 7.3788 \| 325 \| - \| 0.0313 \| 0.7849 \|
	\| 7.4903 \| 330 \| 0.4221 \| 0.0312 \| 0.7848 \|
	\| 7.6017 \| 335 \| - \| 0.0311 \| 0.7854 \|
	\| 7.7131 \| 340 \| 0.4268 \| 0.0310 \| 0.7857 \|
	\| 7.8245 \| 345 \| - \| 0.0309 \| 0.7861 \|
	\| 7.9359 \| 350 \| 0.4316 \| 0.0309 \| 0.7866 \|
	\| 8.0669 \| 355 \| - \| 0.0309 \| 0.7872 \|
	\| 8.1783 \| 360 \| 0.4277 \| 0.0309 \| 0.7873 \|
	\| 8.2897 \| 365 \| - \| 0.0308 \| 0.7870 \|
	\| 8.4011 \| 370 \| 0.3925 \| 0.0308 \| 0.7868 \|
	\| 8.5125 \| 375 \| - \| 0.0308 \| 0.7866 \|
	\| 8.6240 \| 380 \| 0.4049 \| 0.0308 \| 0.7869 \|
	\| 8.7354 \| 385 \| - \| 0.0308 \| 0.7875 \|
	\| 8.8468 \| 390 \| 0.3742 \| 0.0308 \| 0.7883 \|
	\| 8.9582 \| 395 \| - \| 0.0307 \| 0.7885 \|
	\| 9.0891 \| 400 \| 0.3498 \| 0.0307 \| 0.7886 \|
	\| 9.2006 \| 405 \| - \| 0.0307 \| 0.7881 \|
	\| 9.3120 \| 410 \| 0.3569 \| 0.0307 \| 0.7878 \|
	\| 9.4234 \| 415 \| - \| 0.0307 \| 0.7876 \|
	\| 9.5348 \| 420 \| 0.3312 \| 0.0306 \| 0.7877 \|
	\| 9.6462 \| 425 \| - \| 0.0305 \| 0.7881 \|
	\| 9.7577 \| 430 \| 0.3848 \| 0.0304 \| 0.7885 \|
	\| 9.8691 \| 435 \| - \| 0.0304 \| 0.7889 \|
	\| 9.9805 \| 440 \| 0.332 \| 0.0305 \| 0.7890 \|


	### Framework Versions
	- Python: 3.11.10
	- Sentence Transformers: 3.4.1
	- Transformers: 4.48.3
	- PyTorch: 2.5.1+cu124
	- Accelerate: 1.3.0
	- Datasets: 3.3.0
	- Tokenizers: 0.21.0

	## 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",
	}
	```

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