x2bee/KoModernBERT_SBERT_compare_mlmlv5

badfcff verified 29 days ago

19.6 kB

	---
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:550152
	- loss:CosineSimilarityLoss
	base_model: x2bee/KoModernBERT-base-mlm_v02
	widget:
	- source_sentence: 한 남자가 다리가 허벅지에 있고 자전거 헬멧이 두 개 뒤에 있는 여자 옆에 앉아 있다.
	sentences:
	- 그 어린 소년은 야외에서 장난감 비행기를 날리고 있었다.
	- 사람들은 더 잘 보기 위해 서 있다.
	- 남자가 여자의 허벅지에 다리를 얹고 있다.
	- source_sentence: 도끼로 구조물을 무너뜨리는 남자.
	sentences:
	- 소년이 당나귀를 타고 있다.
	- 남자는 새들의 사진을 찍을 준비를 한다.
	- 한 남자가 수갑을 찬 채 감옥을 통과하고 있다.
	- source_sentence: 오토바이를 탄 스폰서를 입은 남자가 손을 들고 오토바이에 앉아 있다.
	sentences:
	- 남자는 오토바이 경주를 준비한다.
	- 한 여성이 라켓을 허공에 대고 라켓 볼 코트 모퉁이를 가로질러 걸어간다.
	- 어떤 남자들은 발레리나 옷을 입고 있다.
	- source_sentence: 경기를 볼 수 있는 스포츠 바.
	sentences:
	- 럭비를 하는 사람
	- 스포츠 바는 게임을 보기에 인기 있는 곳이다.
	- 두 여자 모두 들고 있는 안경으로 술을 마시고 있다.
	- source_sentence: 한 여자와 소년이 경찰 오토바이에 앉아 있다.
	sentences:
	- 여자와 소년이 밖에 있다.
	- 한 남자가 총으로 아기를 쐈다.
	- 한 남자가 물 위에 밧줄을 매고 있다.
	datasets:
	- x2bee/Korean_NLI_dataset
	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 x2bee/KoModernBERT-base-mlm_v02
	results:
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts dev
	type: sts_dev
	metrics:
	- type: pearson_cosine
	value: 0.6374494482799764
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.6328250180270107
	name: Spearman Cosine
	- type: pearson_euclidean
	value: 0.6326629869012427
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.612232056020112
	name: Spearman Euclidean
	- type: pearson_manhattan
	value: 0.6346199347508898
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.615448809374675
	name: Spearman Manhattan
	- type: pearson_dot
	value: 0.5941390124399774
	name: Pearson Dot
	- type: spearman_dot
	value: 0.5741507526998049
	name: Spearman Dot
	- type: pearson_max
	value: 0.6374494482799764
	name: Pearson Max
	- type: spearman_max
	value: 0.6328250180270107
	name: Spearman Max
	---

	# SentenceTransformer based on x2bee/KoModernBERT-base-mlm_v02

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [x2bee/KoModernBERT-base-mlm_v02](https://huggingface.co/x2bee/KoModernBERT-base-mlm_v02) on the [korean_nli_dataset](https://huggingface.co/datasets/x2bee/Korean_NLI_dataset) 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: [x2bee/KoModernBERT-base-mlm_v02](https://huggingface.co/x2bee/KoModernBERT-base-mlm_v02) <!-- at revision e70a0396ecbe3f187762e0cb9ee5952fa42e6bb9 -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 768 dimensions
	- Similarity Function: Cosine Similarity
	- Training Dataset:
	- [korean_nli_dataset](https://huggingface.co/datasets/x2bee/Korean_NLI_dataset)
	<!-- - 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': 512, 'do_lower_case': False}) with Transformer model: ModernBertModel
	(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': True, 'pooling_mode_lasttoken': False, 'include_prompt': True})
	(2): Dense({'in_features': 768, 'out_features': 768, '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("x2bee/KoModernBERT_SBERT_compare_mlmlv5")
	# Run inference
	sentences = [
	'한 여자와 소년이 경찰 오토바이에 앉아 있다.',
	'여자와 소년이 밖에 있다.',
	'한 남자가 물 위에 밧줄을 매고 있다.',
	]
	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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	You can finetune this model on your own dataset.

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	## 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 \| 0.6374 \|
	\| spearman_cosine \| 0.6328 \|
	\| pearson_euclidean \| 0.6327 \|
	\| spearman_euclidean \| 0.6122 \|
	\| pearson_manhattan \| 0.6346 \|
	\| spearman_manhattan \| 0.6154 \|
	\| pearson_dot \| 0.5941 \|
	\| spearman_dot \| 0.5742 \|
	\| pearson_max \| 0.6374 \|
	\| spearman_max \| 0.6328 \|

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	## Training Details

	### Training Dataset

	#### korean_nli_dataset

	* Dataset: [korean_nli_dataset](https://huggingface.co/datasets/x2bee/Korean_NLI_dataset) at [51cc968](https://huggingface.co/datasets/x2bee/Korean_NLI_dataset/tree/51cc968560f9600460d8af859c5b9a94849790a4)
	* Size: 550,152 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: 8 tokens</li><li>mean: 21.76 tokens</li><li>max: 76 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 14.36 tokens</li><li>max: 44 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 0.49</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| sentence1 \| sentence2 \| score \|
	\|:-----------------------------------------------------------------------------\|:-----------------------------------------------------------------------------\|:-----------------\|
	\| <code>몸에 맞지 않는 노란색 셔츠와 파란색 플래드 스커트를 입은 나이든 여성이 두 개의 통 옆에 앉아 있다.</code> \| <code>여자가 역기를 들어올리고 있다.</code> \| <code>0.0</code> \|
	\| <code>갈색 코트를 입은 선글라스를 쓴 한 남성이 담배를 피우며 손님들이 길거리 스탠드에서 물건을 구입하자 코를 긁는다.</code> \| <code>갈색 코트를 입은 선글라스를 쓴 청년이 담배를 피우며 손님들이 스테이트 스탠드에서 구매하고 있을 때 코를 긁는다.</code> \| <code>0.5</code> \|
	\| <code>소녀들은 물을 뿌리며 놀면서 킥킥 웃는다.</code> \| <code>수도 본관이 고장나서 큰길이 범람했다.</code> \| <code>0.0</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"
	}
	```

	### Evaluation Dataset

	#### korean_nli_dataset

	* Dataset: [korean_nli_dataset](https://huggingface.co/datasets/x2bee/Korean_NLI_dataset) at [51cc968](https://huggingface.co/datasets/x2bee/Korean_NLI_dataset/tree/51cc968560f9600460d8af859c5b9a94849790a4)
	* Size: 550,152 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: 4 tokens</li><li>mean: 21.88 tokens</li><li>max: 76 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 14.14 tokens</li><li>max: 38 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 0.52</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| sentence1 \| sentence2 \| score \|
	\|:-------------------------------------------------------------\|:------------------------------------------\|:-----------------\|
	\| <code>한 역사학자와 그의 친구는 연구를 위해 더 많은 화석을 찾기 위해 광산을 파고 있다.</code> \| <code>역사가는 공부를 위해 친구와 함께 땅을 파고 있다.</code> \| <code>0.5</code> \|
	\| <code>소년은 회전목마에 도움을 받는다.</code> \| <code>소년이 당나귀를 타고 있다.</code> \| <code>0.0</code> \|
	\| <code>세탁실에서 사색적인 포즈를 취하고 있는 남자.</code> \| <code>한 남자가 파티오 밖에 있다.</code> \| <code>0.0</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

	- `eval_strategy`: epoch
	- `per_device_train_batch_size`: 32
	- `per_device_eval_batch_size`: 32
	- `gradient_accumulation_steps`: 2
	- `learning_rate`: 1e-05
	- `num_train_epochs`: 2
	- `warmup_ratio`: 0.3
	- `push_to_hub`: True
	- `hub_model_id`: x2bee/KoModernBERT_SBERT_compare_mlmlv5
	- `batch_sampler`: no_duplicates

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

	- `overwrite_output_dir`: False
	- `do_predict`: False
	- `eval_strategy`: epoch
	- `prediction_loss_only`: True
	- `per_device_train_batch_size`: 32
	- `per_device_eval_batch_size`: 32
	- `per_gpu_train_batch_size`: None
	- `per_gpu_eval_batch_size`: None
	- `gradient_accumulation_steps`: 2
	- `eval_accumulation_steps`: None
	- `torch_empty_cache_steps`: None
	- `learning_rate`: 1e-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`: 2
	- `max_steps`: -1
	- `lr_scheduler_type`: linear
	- `lr_scheduler_kwargs`: {}
	- `warmup_ratio`: 0.3
	- `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`: x2bee/KoModernBERT_SBERT_compare_mlmlv5
	- `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
	- `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 \| 0 \| - \| - \| 0.3994 \|
	\| 0.0980 \| 100 \| 0.3216 \| - \| - \|
	\| 0.1960 \| 200 \| 0.2019 \| - \| - \|
	\| 0.2940 \| 300 \| 0.1451 \| - \| - \|
	\| 0.3920 \| 400 \| 0.1327 \| - \| - \|
	\| 0.4900 \| 500 \| 0.1231 \| - \| - \|
	\| 0.5879 \| 600 \| 0.1138 \| - \| - \|
	\| 0.6859 \| 700 \| 0.1091 \| - \| - \|
	\| 0.7839 \| 800 \| 0.106 \| - \| - \|
	\| 0.8819 \| 900 \| 0.1047 \| - \| - \|
	\| 0.9799 \| 1000 \| 0.1029 \| - \| - \|
	\| 1.0 \| 1021 \| - \| 0.1003 \| 0.6352 \|
	\| 1.0774 \| 1100 \| 0.0999 \| - \| - \|
	\| 1.1754 \| 1200 \| 0.0994 \| - \| - \|
	\| 1.2734 \| 1300 \| 0.0989 \| - \| - \|
	\| 1.3714 \| 1400 \| 0.0974 \| - \| - \|
	\| 1.4694 \| 1500 \| 0.0975 \| - \| - \|
	\| 1.5674 \| 1600 \| 0.0945 \| - \| - \|
	\| 1.6654 \| 1700 \| 0.0933 \| - \| - \|
	\| 1.7634 \| 1800 \| 0.0922 \| - \| - \|
	\| 1.8613 \| 1900 \| 0.0928 \| - \| - \|
	\| 1.9593 \| 2000 \| 0.0928 \| - \| - \|
	\| 1.9985 \| 2040 \| - \| 0.0955 \| 0.6328 \|


	### Framework Versions
	- Python: 3.11.10
	- Sentence Transformers: 3.3.1
	- Transformers: 4.48.0.dev0
	- PyTorch: 2.5.1+cu124
	- Accelerate: 1.2.1
	- Datasets: 3.2.0
	- 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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