x2bee/KoModernBERT_SBERT_compare_mlmlv5

1_Pooling/config.json

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+{
+  "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/config.json

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+{"in_features": 768, "out_features": 768, "bias": true, "activation_function": "torch.nn.modules.activation.Tanh"}

2_Dense/model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:47f06bfc7c718988a4e54cdadfc958038eeb065cfc638e5b8d12a8b67c9bc989
+size 2362528

README.md

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+---
+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]
+```
+
+<!--
+### 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     | 0.6374     |
+| 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** |
+
+<!--
+## 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
+
+#### 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",
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.3.1",
+    "transformers": "4.48.0.dev0",
+    "pytorch": "2.5.1+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Dense",
+    "type": "sentence_transformers.models.Dense"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}