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1_Pooling/config.json

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+{
+  "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
+}

README.md

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+---
+base_model: klue/roberta-base
+datasets: []
+language: []
+library_name: sentence-transformers
+metrics:
+- pearson_cosine
+- spearman_cosine
+- pearson_manhattan
+- spearman_manhattan
+- pearson_euclidean
+- spearman_euclidean
+- pearson_dot
+- spearman_dot
+- pearson_max
+- spearman_max
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:10501
+- loss:CosineSimilarityLoss
+widget:
+- source_sentence: 기업은 생존 문제에 직면하고, 자영업자와 소상공인의 고통은 이루 말할 수 없을 정도입니다.
+  sentences:
+  - 자유무역은 기업이 서로를 신뢰하고, 미래의 불확실성을 낮추는 안전장치입니다.
+  - 국가 임상연구 승인, 시행기관 지정, 장기 추적조사 등 안전관리체계를 구축하고 치료 개발 및 임상연구 수행을 위한 RD 투자를 확대합니다.
+  - 중심가와 거리가 조금 먼 점 빼고는 정말 모든게 너무 좋았던 숙소입니다!
+- source_sentence: 타이페이를 다시 간다면 여기 또 올거예요.
+  sentences:
+  - 사진으로 봤던것보다 훨씬 더 좋았습니다
+  - 겨울에 난방 온도 이십오도 이상으로 올리지마라고 경고했어
+  - 만약 내가 다시 타이페이에 간다면, 나는 여기에 다시 올 것입니다.
+- source_sentence: 호주의 좋은 가정집에서 묵는 느낌이었어요.
+  sentences:
+  - 어린이 교통사고 위험지역에 CCTV 2087대, 신호등 2146개를 올해 상반기 중으로 설치하고 옐로카펫과 노란발자국 등을 올해 하반기에 초등학교
+    100곳에 시범 설치한다.
+  - 호주에 있는 좋은 집에서 지내는 것 같았어요.
+  - 그러나 호텔업계 노사가 가장 어려운 시기에, 가장 모범적으로 함께 마음을 모았습니다.
+- source_sentence: 그들덕분에 우리는 4일간 편안히 쉴 수 있었습니다.
+  sentences:
+  - 그들 덕분에, 우리는 4일 동안 쉴 수 있었어요.
+  - 주변에 두 개의 지하철역이 있습니다. 큰 공원, 큰 슈퍼마켓, 그리고 편의점이 있습니다.
+  - 방은 쾌적하고 에어컨도 아주 잘 나와요.
+- source_sentence: 테라스에서 봤던 뷰와 그곳에서 먹었던 식사가 그리울 것 같아요.
+  sentences:
+  - 테라스에서 본 풍경과 거기서 먹었던 음식이 그리울 것 같아요.
+  - 이쪽 주변에서 여행할 계획이라면 추천합니다!
+  - 저희 할아버지는 매우 친절하고 친절하십니다.
+co2_eq_emissions:
+  emissions: 7.379414346751554
+  energy_consumed: 0.016863301234344347
+  source: codecarbon
+  training_type: fine-tuning
+  on_cloud: false
+  cpu_model: 13th Gen Intel(R) Core(TM) i7-13700
+  ram_total_size: 62.56697463989258
+  hours_used: 0.057
+  hardware_used: 1 x NVIDIA GeForce RTX 4090
+model-index:
+- name: SentenceTransformer based on klue/roberta-base
+  results:
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: Unknown
+      type: unknown
+    metrics:
+    - type: pearson_cosine
+      value: 0.34770704341988723
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.35560473197486514
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.3673846313946801
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.36460670798564826
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.3607451203867209
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.35482778401649034
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.21251167982120983
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.20063256899469895
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.3673846313946801
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.36460670798564826
+      name: Spearman Max
+    - type: pearson_cosine
+      value: 0.961968864970919
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.9196100863981246
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.9530332430579778
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.9186168431687389
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.9532923011007042
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.9190754386835427
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.9493179101338206
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.8999468521869318
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.961968864970919
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.9196100863981246
+      name: Spearman Max
+---
+
+# 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:** 512 tokens
+- **Output Dimensionality:** 768 tokens
+- **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': 512, 'do_lower_case': False}) with Transformer model: 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': True})
+)
+```
+
+## 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("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    '테라스에서 봤던 뷰와 그곳에서 먹었던 식사가 그리울 것 같아요.',
+    '테라스에서 본 풍경과 거기서 먹었던 음식이 그리울 것 같아요.',
+    '이쪽 주변에서 여행할 계획이라면 추천합니다!',
+]
+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
+
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric             | Value      |
+|:-------------------|:-----------|
+| pearson_cosine     | 0.3477     |
+| spearman_cosine    | 0.3556     |
+| pearson_manhattan  | 0.3674     |
+| spearman_manhattan | 0.3646     |
+| pearson_euclidean  | 0.3607     |
+| spearman_euclidean | 0.3548     |
+| pearson_dot        | 0.2125     |
+| spearman_dot       | 0.2006     |
+| pearson_max        | 0.3674     |
+| **spearman_max**   | **0.3646** |
+
+#### Semantic Similarity
+
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric             | Value      |
+|:-------------------|:-----------|
+| pearson_cosine     | 0.962      |
+| spearman_cosine    | 0.9196     |
+| pearson_manhattan  | 0.953      |
+| spearman_manhattan | 0.9186     |
+| pearson_euclidean  | 0.9533     |
+| spearman_euclidean | 0.9191     |
+| pearson_dot        | 0.9493     |
+| spearman_dot       | 0.8999     |
+| pearson_max        | 0.962      |
+| **spearman_max**   | **0.9196** |
+
+<!--
+## 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: 10,501 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: 7 tokens</li><li>mean: 20.23 tokens</li><li>max: 64 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 19.94 tokens</li><li>max: 63 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.44</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence_0                                                                                                      | sentence_1                                                                                                        | label                            |
+  |:----------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------|:---------------------------------|
+  | <code>지하철 역 내려서 1분정도의 아주 가까운 거리입니다.</code>                                                                      | <code>지하철역에서 1분 정도 아주 가까운 거리입니다.</code>                                                                           | <code>0.86</code>                |
+  | <code>그것빼곤 2인여행자들에게는 좋은숙소에요!</code>                                                                             | <code>계단이 많다는거 빼곤 완벽한 숙소에요!</code>                                                                                | <code>0.27999999999999997</code> |
+  | <code>이어 현금이 286만 가구(13.2%) 1조3007억원, 선불카드가 75만 가구(3.5%) 4990억원, 지역사랑상품권은 63만 가구(2.9%) 4171억원으로 각각 집계됐다.</code> | <code>이어 현금 286만 가구(13.2%), 현금 1조337억 원, 선불카드 75만 가구(3.5%), 4990억 원, 지역사랑상품권 63만 가구(2.9%), 4171억 원 순이었습니다.</code> | <code>0.86</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
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `num_train_epochs`: 4
+- `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`: 16
+- `per_device_eval_batch_size`: 16
+- `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`: 4
+- `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`: False
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `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
+- `eval_use_gather_object`: False
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | spearman_max |
+|:------:|:----:|:-------------:|:------------:|
+| 0      | 0    | -             | 0.3646       |
+| 0.7610 | 500  | 0.0283        | -            |
+| 1.0    | 657  | -             | 0.9075       |
+| 1.5221 | 1000 | 0.0082        | 0.9148       |
+| 2.0    | 1314 | -             | 0.9148       |
+| 2.2831 | 1500 | 0.0047        | -            |
+| 3.0    | 1971 | -             | 0.9180       |
+| 3.0441 | 2000 | 0.0034        | 0.9168       |
+| 3.8052 | 2500 | 0.0027        | -            |
+| 4.0    | 2628 | -             | 0.9196       |
+
+
+### Environmental Impact
+Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
+- **Energy Consumed**: 0.017 kWh
+- **Carbon Emitted**: 0.007 kg of CO2
+- **Hours Used**: 0.057 hours
+
+### Training Hardware
+- **On Cloud**: No
+- **GPU Model**: 1 x NVIDIA GeForce RTX 4090
+- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700
+- **RAM Size**: 62.57 GB
+
+### Framework Versions
+- Python: 3.9.0
+- Sentence Transformers: 3.0.1
+- Transformers: 4.44.1
+- PyTorch: 2.3.1+cu121
+- Accelerate: 0.33.0
+- Datasets: 2.19.1
+- Tokenizers: 0.19.1
+
+## 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.json

@@ -0,0 +1,29 @@
+{
+  "_name_or_path": "klue/roberta-base",
+  "architectures": [
+    "RobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "tokenizer_class": "BertTokenizer",
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.1",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 32000
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.44.1",
+    "pytorch": "2.3.1+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ef6cb4617ea78f430fc88514db380aefc4ae196203ac391928e751f9e610fa7f
+size 442494816

modules.json

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+[
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+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
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+  {
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+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

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+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+    "single_word": false
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+  "bos_token": "[CLS]",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": false,
+  "eos_token": "[SEP]",
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}