Update README.md

README.md

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----
-tags:
-- sentence-transformers
-- sentence-similarity
-- feature-extraction
-- dense
-- generated_from_trainer
-- dataset_size:574389
-- loss:MultipleNegativesRankingLoss
-- loss:CosineSimilarityLoss
-base_model: klue/roberta-base
-widget:
-- source_sentence: 이슬람 국가, 인질 참수 비디오
-  sentences:
-  - 이슬람 국가, 영국 인질 2차 선전 비디오 게시
-  - 보라섹을 골로 삼아라.
-  - 우리가 모두 많은 불가사의로 가득 찬 그릇이라고 말하세요.
-- source_sentence: 사람이 양파를 썰고 있다.
-  sentences:
-  - 햄스터가 노래하고 있다.
-  - 이스라엘, 새 정착민 주택 계획 공개
-  - 소킨은 음모 혐의로, 대배심에게 거짓말을 한 혐의로 별도로 재판을 받아야 했다.
-- source_sentence: 빨간 셔츠를 입은 남자가 뒷주머니를 들여다보고 있다
-  sentences:
-  - 헤드폰을 쓴 여성은 메모지에 글을 쓰면서 커피를 즐긴다.
-  - 녹색 셔츠를 입은 남자가 앞주머니를 보고 있다.
-  - 남자가 뒷주머니를 보고 있다.
-- source_sentence: 그들은 많은 사람들이다.
-  sentences:
-  - 사람들이 질식하고 있다.
-  - 파란 드레스를 입은 여자가 손에 고블렛을 들고 카메라 밖에서 손을 뻗는다.
-  - 군대를 귀국시키기 위해 항의하는 군중의 중간 사격.
-- source_sentence: 한 젊은 치어리더가 경기 중에 그녀의 팀과 함께 공연을 하고 있다.
-  sentences:
-  - 문서 감축법에서 요구하는 정보의 필요성은 서문에서 찾을 수 있다.
-  - 치어리더들이 하고 있는 경기는 끝났다.
-  - 치어리더 그룹이 공연을 하고 있다.
-pipeline_tag: sentence-similarity
-library_name: sentence-transformers
-metrics:
-- pearson_cosine
-- spearman_cosine
-model-index:
-- name: SentenceTransformer based on klue/roberta-base
-  results:
-  - task:
-      type: semantic-similarity
-      name: Semantic Similarity
-    dataset:
-      name: sts dev
-      type: sts-dev
-    metrics:
-    - type: pearson_cosine
-      value: 0.8574470760699765
-      name: Pearson Cosine
-    - type: spearman_cosine
-      value: 0.8573610558316641
-      name: Spearman Cosine
----
-
-# SentenceTransformer based on klue/roberta-base
-
-This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [klue/roberta-base](https://huggingface.co/klue/roberta-base). 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:** [klue/roberta-base](https://huggingface.co/klue/roberta-base) <!-- at revision 02f94ba5e3fcb7e2a58a390b8639b0fac974a8da -->
-- **Maximum Sequence Length:** 128 tokens
-- **Output Dimensionality:** 768 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': 128, 'do_lower_case': False, 'architecture': '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': False})
-)
-```
-
-## 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("twodigit/rt-128-02")
-# Run inference
-sentences = [
-    '한 젊은 치어리더가 경기 중에 그녀의 팀과 함께 공연을 하고 있다.',
-    '치어리더 그룹이 공연을 하고 있다.',
-    '치어리더들이 하고 있는 경기는 끝났다.',
-]
-embeddings = model.encode(sentences)
-print(embeddings.shape)
-# [3, 768]
-
-# Get the similarity scores for the embeddings
-similarities = model.similarity(embeddings, embeddings)
-print(similarities)
-# tensor([[1.0000, 0.8456, 0.6418],
-#         [0.8456, 1.0000, 0.6590],
-#         [0.6418, 0.6590, 1.0000]])
-```
-
-<!--
-### 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      | 0.8574     |
-| **spearman_cosine** | **0.8574** |
-
-<!--
-## 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 Datasets
-
-#### Unnamed Dataset
-
-* Size: 568,640 training samples
-* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code>
-* Approximate statistics based on the first 1000 samples:
-  |         | sentence_0                                                                         | sentence_1                                                                         | sentence_2                                                                        |
-  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
-  | type    | string                                                                             | string                                                                             | string                                                                            |
-  | details | <ul><li>min: 3 tokens</li><li>mean: 18.69 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 19.26 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.62 tokens</li><li>max: 49 tokens</li></ul> |
-* Samples:
-  | sentence_0                            | sentence_1                                             | sentence_2                          |
-  |:--------------------------------------|:-------------------------------------------------------|:------------------------------------|
-  | <code>산 마테오 교회는 회색과 흰색이 섞여 있다.</code> | <code>산 마테오의 로마네스크-고딕 교회는 같은 회색과 흰색의 면을 가지고 있다.</code> | <code>산마테오 교회는 노랗고 푸른 면이 같다.</code> |
-  | <code>한 병사가 걸으면서 큰 총을 들고 있다.</code>   | <code>뭔가를 들고 있는 사람</code>                              | <code>아무도 걷지 않는다.</code>            |
-  | <code>동물들은 들판에서 논다.</code>            | <code>두 마리의 강아지가 들판에서 빨간 씹는 장난감을 가지고 논다.</code>        | <code>강아지들은 들판에서 뼈를 가지고 논다.</code>  |
-* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
-  ```json
-  {
-      "scale": 20.0,
-      "similarity_fct": "cos_sim"
-  }
-  ```
-
-#### Unnamed Dataset
-
-* Size: 5,749 training samples
-* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
-* Approximate statistics based on the first 1000 samples:
-  |         | sentence_0                                                                        | sentence_1                                                                        | label                                                          |
-  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
-  | type    | string                                                                            | string                                                                            | float                                                          |
-  | details | <ul><li>min: 3 tokens</li><li>mean: 17.58 tokens</li><li>max: 66 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 17.41 tokens</li><li>max: 59 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.52</li><li>max: 1.0</li></ul> |
-* Samples:
-  | sentence_0                             | sentence_1                                        | label                           |
-  |:---------------------------------------|:--------------------------------------------------|:--------------------------------|
-  | <code>이스라엘, 팔레스타인 죄수 석방</code>         | <code>Gov 'T ���원회는 26명의 팔레스타인 수감자 석방을 승인한다.</code> | <code>0.6799999999999999</code> |
-  | <code>중국 남부 도로 사고로 7명 사망, 3명 부상</code> | <code>필리핀 도로 사고로 20명 사망, 44명 부상</code>            | <code>0.12</code>               |
-  | <code>이란의 새로운 제재 없이 마감 기한이 지났다</code>  | <code>EU는 새로운 이란 제재에 더 가까이 다가간다.</code>           | <code>0.32</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`: steps
-- `batch_sampler`: no_duplicates
-- `multi_dataset_batch_sampler`: round_robin
-
-#### 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`: 8
-- `per_device_eval_batch_size`: 8
-- `per_gpu_train_batch_size`: None
-- `per_gpu_eval_batch_size`: None
-- `gradient_accumulation_steps`: 1
-- `eval_accumulation_steps`: None
-- `torch_empty_cache_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
-- `num_train_epochs`: 3
-- `max_steps`: -1
-- `lr_scheduler_type`: linear
-- `lr_scheduler_kwargs`: {}
-- `warmup_ratio`: 0.0
-- `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`: 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`: None
-- `hub_always_push`: False
-- `hub_revision`: None
-- `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
-- `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
-- `liger_kernel_config`: None
-- `eval_use_gather_object`: False
-- `average_tokens_across_devices`: False
-- `prompts`: None
-- `batch_sampler`: no_duplicates
-- `multi_dataset_batch_sampler`: round_robin
-- `router_mapping`: {}
-- `learning_rate_mapping`: {}
-
-</details>
-
-### Training Logs
-| Epoch  | Step | Training Loss | sts-dev_spearman_cosine |
-|:------:|:----:|:-------------:|:-----------------------:|
-| 0.3477 | 500  | 0.4175        | -                       |
-| 0.6954 | 1000 | 0.3015        | 0.8491                  |
-| 1.0    | 1438 | -             | 0.8574                  |
-
-
-### Framework Versions
-- Python: 3.11.13
-- Sentence Transformers: 5.0.0
-- Transformers: 4.54.1
-- PyTorch: 2.7.1+cu126
-- Accelerate: 1.9.0
-- Datasets: 3.6.0
-- Tokenizers: 0.21.4
-
-## 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",
-}
-```
-
-#### 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}
-}
-```
-
-<!--
-## 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.*
--->