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: 흰 개와 갈색 개가 풀밭을 뛰어다니고 있다.
-  sentences:
-  - 갈색과 흰색의 개가 잔디밭을 가로질러 달리고 있다.
-  - 자전거를 타고 강을 배경으로 뛰어넘는 남자
-  - 방글라데시 공장에서 발견된 생존자 50명
-- source_sentence: 깔개 위에 누워 있는 고양이의 흑백 이미지.
-  sentences:
-  - 깔개 위에 누워 있는 고양이의 흑백 사진.
-  - 캘리포니아에서의 반응은 무엇인가?
-  - 한 남자가 두 개의 키보드를 연주하고 있다.
-- source_sentence: 르네상스 페어 의상을 입은 한 소년이 잔디 위에 주차된 차 옆에 서 있다.
-  sentences:
-  - 오른손에 금띠를 매고 있는 갈색 머리의 남자가 메뉴를 보면서 아이스티 한 잔을 홀짝인다.
-  - 근처에 비상근무자들이 있는 도랑에서 차가 뒤집힌다.
-  - 밖에 차가 주차되어 있다.
-- source_sentence: 선수들은 페널티 샷을 할 준비가 되어 있다.
-  sentences:
-  - 남자들이 테니스를 치고 있다.
-  - 마카오는 1622년 네덜란드로부터 성공적으로 방어했다.
-  - 빨간 옷을 입은 축구 선수들은 골을 보호하기 위해 페널티 슛을 준비한다.
-- source_sentence: 한 할머니와 한 아이가 아프리카 사람에 의해 음식을 제공받는 동안 화려한 녹색 테이블에 앉아 있다.
-  sentences:
-  - 두 사람이 술집에 앉아 의사소통을 하고 있다.
-  - 소년과 소녀가 나무 오솔길을 걷고 있다.
-  - 두 사람이 함께 앉아 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: 0.8629638547144741
-      name: Pearson Cosine
-    - type: spearman_cosine
-      value: 0.8626862905871633
-      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-128-01")
-# Run inference
-sentences = [
-    '한 할머니와 한 아이가 아프리카 사람에 의해 음식을 제공받는 동안 화려한 녹색 테이블에 앉아 있다.',
-    '두 사람이 함께 앉아 있다.',
-    '소년과 소녀가 나무 오솔길을 걷고 있다.',
-]
-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, 0.4191, 0.0954],
-#         [0.4191, 1.0000, 0.0444],
-#         [0.0954, 0.0444, 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      | 0.863      |
-| **spearman_cosine** | **0.8627** |
-
-<!--
-## 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: 4 tokens</li><li>mean: 19.19 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 18.32 tokens</li><li>max: 93 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.66 tokens</li><li>max: 57 tokens</li></ul> |
-* Samples:
-  | sentence_0                              | sentence_1                                       | sentence_2                            |
-  |:----------------------------------------|:-------------------------------------------------|:--------------------------------------|
-  | <code>발생 부하가 함께 5% 적습니다.</code>         | <code>발생 부하의 5% 감소와 함께 11.</code>                | <code>발생 부하가 5% 증가합니다.</code>         |
-  | <code>어떤 행사를 위해 음식과 옷을 배급하는 여성들.</code> | <code>여성들은 음식과 옷을 나눠줌으로써 난민들을 돕고 있다.</code>      | <code>여자들이 사막에서 오토바이를 운전하고 있다.</code> |
-  | <code>어린 아이들은 그 지식을 얻을 필요가 있다.</code>   | <code>응, 우리 젊은이들 중 많은 사람들이 그걸 배워야 할 것 같아.</code> | <code>젊은 사람들은 배울 필요가 없다.</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: 5 tokens</li><li>mean: 17.15 tokens</li><li>max: 71 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 16.86 tokens</li><li>max: 76 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.54</li><li>max: 1.0</li></ul> |
-* Samples:
-  | sentence_0                               | sentence_1                         | label             |
-  |:-----------------------------------------|:-----------------------------------|:------------------|
-  | <code>남자가 기타를 치고 있다.</code>              | <code>소뇌는 기타를 치고 있다.</code>        | <code>0.72</code> |
-  | <code>고양이가 빨판을 핥고 있다.</code>             | <code>한 여성이 오이를 자르고 있다.</code>     | <code>0.0</code>  |
-  | <code>누군가가 파워 드릴로 나무 조각에 구멍을 뚫는다.</code> | <code>한 남자가 나무 조각에 구멍을 뚫는다.</code> | <code>0.64</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  | 0.3801        | -                       |
-| 0.6954 | 1000 | 0.282         | 0.8489                  |
-| 1.0    | 1438 | -             | 0.8560                  |
-| 1.0431 | 1500 | 0.2629        | -                       |
-| 1.3908 | 2000 | 0.109         | 0.8627                  |
-
-
-### 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.*
--->
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-## Model Card Contact
-
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