Update README.md

README.md

@@ -1,199 +1,543 @@
 ---
-library_name: transformers
-tags: []
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:392702
+- loss:CosineSimilarityLoss
+base_model: x2bee/KoModernBERT-base-mlm-v03-ckp00
+widget:
+- source_sentence: 우리는 움직이는 동행 우주 정지 좌표계에 비례하여 이동하고 있습니다 ... 약 371km / s에서 별자리 leo
+    쪽으로. "
+  sentences:
+  - 두 마리의 독수리가 가지에 앉는다.
+  - 다른 물체와는 관련이 없는 '정지'는 없다.
+  - 소녀는 버스의 열린 문 앞에 서 있다.
+- source_sentence: 숲에는 개들이 있다.
+  sentences:
+  - 양을 보는 아이들.
+  - 여왕의 배우자를 "왕"이라고 부르지 않는 것은 아주 좋은 이유가 있다. 왜냐하면 그들은 왕이 아니기 때문이다.
+  - 개들은 숲속에 혼자 있다.
+- source_sentence: '첫째, 두 가지 다른 종류의 대시가 있다는 것을 알아야 합니다 : en 대시와 em 대시.'
+  sentences:
+  - 그들은 그 물건들을 집 주변에 두고 가거나 집의 정리를 해칠 의도가 없다.
+  - 세미콜론은 혼자 있을 수 있는 문장에 참여하는데 사용되지만, 그들의 관계를 강조하기 위해 결합됩니다.
+  - 그의 남동생이 지켜보는 동안 집 앞에서 트럼펫을 연주하는 금발의 아이.
+- source_sentence: 한 여성이 생선 껍질을 벗기고 있다.
+  sentences:
+  - 한 남자가 수영장으로 뛰어들었다.
+  - 한 여성이 프라이팬에 노란 혼합물을 부어 넣고 있다.
+  - 두 마리의 갈색 개가 눈 속에서 서로 놀고 있다.
+- source_sentence: 버스가 바쁜 길을 따라 운전한다.
+  sentences:
+  - 우리와 같은 태양계가 은하계 밖에서 존재할 수도 있을 것입니다.
+  - 그 여자는 데이트하러 가는 중이다.
+  - 녹색 버스가 도로를 따라 내려간다.
+datasets:
+- x2bee/Korean_NLI_dataset
+- CocoRoF/sts_dev
+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-v03-ckp00
+  results:
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts dev
+      type: sts_dev
+    metrics:
+    - type: pearson_cosine
+      value: 0.6463764324668821
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.668749120795344
+      name: Spearman Cosine
+    - type: pearson_euclidean
+      value: 0.6434649881382908
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.6535107003038169
+      name: Spearman Euclidean
+    - type: pearson_manhattan
+      value: 0.6516759845194007
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.6679435004022668
+      name: Spearman Manhattan
+    - type: pearson_dot
+      value: 0.6306152465572834
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.6496717700503837
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.6516759845194007
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.668749120795344
+      name: Spearman Max
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# SentenceTransformer based on x2bee/KoModernBERT-base-mlm-v03-ckp00
 
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [x2bee/KoModernBERT-base-mlm-v03-ckp00](https://huggingface.co/x2bee/KoModernBERT-base-mlm-v03-ckp00) 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-v03-ckp00](https://huggingface.co/x2bee/KoModernBERT-base-mlm-v03-ckp00) <!-- at revision addb15798678d7f76904915cf8045628d402b3ce -->
+- **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 -->
 
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
+### Model Sources
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+- **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)
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+### Full Model Architecture
 
-## How to Get Started with the Model
+```
+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': True, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
+)
+```
 
-Use the code below to get started with the model.
+## Usage
 
-[More Information Needed]
+### Direct Usage (Sentence Transformers)
 
-## Training Details
+First install the Sentence Transformers library:
 
-### Training Data
+```bash
+pip install -U sentence-transformers
+```
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
 
-[More Information Needed]
+# Download from the 🤗 Hub
+model = SentenceTransformer("x2bee/sts_nli_tune_test")
+# Run inference
+sentences = [
+    '버스가 바쁜 길을 따라 운전한다.',
+    '녹색 버스가 도로를 따라 내려간다.',
+    '그 여자는 데이트하러 가는 중이다.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
 
-### Training Procedure
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+<!--
+### Direct Usage (Transformers)
 
-#### Preprocessing [optional]
+<details><summary>Click to see the direct usage in Transformers</summary>
 
-[More Information Needed]
+</details>
+-->
 
+<!--
+### Downstream Usage (Sentence Transformers)
 
-#### Training Hyperparameters
+You can finetune this model on your own dataset.
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+<details><summary>Click to expand</summary>
 
-#### Speeds, Sizes, Times [optional]
+</details>
+-->
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+<!--
+### Out-of-Scope Use
 
-[More Information Needed]
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+### Metrics
 
-[More Information Needed]
+#### Semantic Similarity
 
-#### Metrics
+* Dataset: `sts_dev`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+| Metric             | Value      |
+|:-------------------|:-----------|
+| pearson_cosine     | 0.6464     |
+| spearman_cosine    | 0.6687     |
+| pearson_euclidean  | 0.6435     |
+| spearman_euclidean | 0.6535     |
+| pearson_manhattan  | 0.6517     |
+| spearman_manhattan | 0.6679     |
+| pearson_dot        | 0.6306     |
+| spearman_dot       | 0.6497     |
+| pearson_max        | 0.6517     |
+| **spearman_max**   | **0.6687** |
 
-[More Information Needed]
+<!--
+## Bias, Risks and Limitations
 
-### Results
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
 
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
+<!--
+### Recommendations
 
-## Model Card Authors [optional]
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
 
-[More Information Needed]
+## Training Details
 
+### Training Dataset
+
+#### korean_nli_dataset
+
+* Dataset: [korean_nli_dataset](https://huggingface.co/datasets/x2bee/Korean_NLI_dataset) at [ef305ef](https://huggingface.co/datasets/x2bee/Korean_NLI_dataset/tree/ef305ef8e2d83c6991f30f2322f321efb5a3b9d1)
+* Size: 392,702 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: 4 tokens</li><li>mean: 35.7 tokens</li><li>max: 194 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 19.92 tokens</li><li>max: 64 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.48</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence1                                                                                                                                           | sentence2                                         | score            |
+  |:----------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------|:-----------------|
+  | <code>개념적으로 크림 스키밍은 제품과 지리라는 두 가지 기본 차원을 가지고 있다.</code>                                                                                             | <code>제품과 지리학은 크림 스키밍을 작동시키는 것이다.</code>          | <code>0.5</code> |
+  | <code>시즌 중에 알고 있는 거 알아? 네 레벨에서 다음 레벨로 잃어버리는 거야 브레이브스가 모팀을 떠올���기로 결정하면 브레이브스가 트리플 A에서 한 남자를 떠올리기로 결정하면 더블 A가 그를 대신하러 올라가고 A 한 명이 그를 대신하러 올라간다.</code> | <code>사람들이 기억하면 다음 수준으로 물건을 잃는다.</code>           | <code>1.0</code> |
+  | <code>우리 번호 중 하나가 당신의 지시를 세밀하게 수행할 것이다.</code>                                                                                                      | <code>우리 팀의 일원이 당신의 명령을 엄청나게 정확하게 실행할 것이다.</code> | <code>1.0</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
+
+#### sts_dev
+
+* Dataset: [sts_dev](https://huggingface.co/datasets/CocoRoF/sts_dev) at [1de0cdf](https://huggingface.co/datasets/CocoRoF/sts_dev/tree/1de0cdfb2c238786ee61c5765aa60eed4a782371)
+* Size: 1,500 evaluation samples
+* Columns: <code>text</code>, <code>pair</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | text                                                                              | pair                                                                              | label                                                          |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                            | float                                                          |
+  | details | <ul><li>min: 7 tokens</li><li>mean: 20.38 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 20.52 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> |
+* Samples:
+  | text                                 | pair                                | label             |
+  |:-------------------------------------|:------------------------------------|:------------------|
+  | <code>안전모를 가진 한 남자가 춤을 추고 있다.</code> | <code>안전모를 쓴 한 남자가 춤을 추고 있다.</code> | <code>1.0</code>  |
+  | <code>어린아이가 말을 타고 있다.</code>         | <code>아이가 말을 타고 있다.</code>          | <code>0.95</code> |
+  | <code>한 남자가 뱀에게 쥐를 먹이고 있다.</code>    | <code>남자가 뱀에게 쥐를 먹이고 있다.</code>     | <code>1.0</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
+
+- `overwrite_output_dir`: True
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `gradient_accumulation_steps`: 8
+- `warmup_ratio`: 0.1
+- `push_to_hub`: True
+- `hub_model_id`: x2bee/sts_nli_tune_test
+- `hub_strategy`: checkpoint
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: True
+- `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`: 8
+- `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`: 1.0
+- `num_train_epochs`: 3.0
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `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/sts_nli_tune_test
+- `hub_strategy`: checkpoint
+- `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 |
+|:------:|:----:|:-------------:|:---------------:|:--------------------:|
+| 0.0326 | 25   | 0.3733        | -               | -                    |
+| 0.0652 | 50   | 0.362         | -               | -                    |
+| 0.0978 | 75   | 0.3543        | -               | -                    |
+| 0.1304 | 100  | 0.3431        | -               | -                    |
+| 0.1630 | 125  | 0.3273        | -               | -                    |
+| 0.1956 | 150  | 0.2745        | -               | -                    |
+| 0.2282 | 175  | 0.2061        | -               | -                    |
+| 0.2608 | 200  | 0.1814        | -               | -                    |
+| 0.2934 | 225  | 0.1658        | -               | -                    |
+| 0.3260 | 250  | 0.1637        | -               | -                    |
+| 0.3586 | 275  | 0.1542        | -               | -                    |
+| 0.3912 | 300  | 0.147         | -               | -                    |
+| 0.4238 | 325  | 0.1392        | -               | -                    |
+| 0.4564 | 350  | 0.1329        | -               | -                    |
+| 0.4890 | 375  | 0.131         | -               | -                    |
+| 0.5216 | 400  | 0.1294        | -               | -                    |
+| 0.5542 | 425  | 0.1245        | -               | -                    |
+| 0.5868 | 450  | 0.1243        | -               | -                    |
+| 0.6194 | 475  | 0.1237        | -               | -                    |
+| 0.6520 | 500  | 0.1236        | 0.0956          | 0.5284               |
+| 0.6846 | 525  | 0.1183        | -               | -                    |
+| 0.7172 | 550  | 0.1166        | -               | -                    |
+| 0.7498 | 575  | 0.1176        | -               | -                    |
+| 0.7824 | 600  | 0.1144        | -               | -                    |
+| 0.8150 | 625  | 0.1141        | -               | -                    |
+| 0.8476 | 650  | 0.1093        | -               | -                    |
+| 0.8802 | 675  | 0.1081        | -               | -                    |
+| 0.9128 | 700  | 0.1082        | -               | -                    |
+| 0.9454 | 725  | 0.1078        | -               | -                    |
+| 0.9780 | 750  | 0.1039        | -               | -                    |
+| 1.0117 | 775  | 0.1106        | -               | -                    |
+| 1.0443 | 800  | 0.1113        | -               | -                    |
+| 1.0769 | 825  | 0.1113        | -               | -                    |
+| 1.1095 | 850  | 0.1103        | -               | -                    |
+| 1.1421 | 875  | 0.1098        | -               | -                    |
+| 1.1747 | 900  | 0.1118        | -               | -                    |
+| 1.2073 | 925  | 0.1085        | -               | -                    |
+| 1.2399 | 950  | 0.1057        | -               | -                    |
+| 1.2725 | 975  | 0.1081        | -               | -                    |
+| 1.3051 | 1000 | 0.1052        | 0.0930          | 0.5830               |
+| 1.3377 | 1025 | 0.1087        | -               | -                    |
+| 1.3703 | 1050 | 0.1046        | -               | -                    |
+| 1.4029 | 1075 | 0.1032        | -               | -                    |
+| 1.4355 | 1100 | 0.1037        | -               | -                    |
+| 1.4681 | 1125 | 0.1026        | -               | -                    |
+| 1.5007 | 1150 | 0.1036        | -               | -                    |
+| 1.5333 | 1175 | 0.102         | -               | -                    |
+| 1.5659 | 1200 | 0.101         | -               | -                    |
+| 1.5985 | 1225 | 0.1014        | -               | -                    |
+| 1.6311 | 1250 | 0.1024        | -               | -                    |
+| 1.6637 | 1275 | 0.1005        | -               | -                    |
+| 1.6963 | 1300 | 0.0993        | -               | -                    |
+| 1.7289 | 1325 | 0.0982        | -               | -                    |
+| 1.7615 | 1350 | 0.0988        | -               | -                    |
+| 1.7941 | 1375 | 0.0965        | -               | -                    |
+| 1.8267 | 1400 | 0.0984        | -               | -                    |
+| 1.8593 | 1425 | 0.0936        | -               | -                    |
+| 1.8919 | 1450 | 0.0924        | -               | -                    |
+| 1.9245 | 1475 | 0.0956        | -               | -                    |
+| 1.9571 | 1500 | 0.0927        | 0.0732          | 0.6470               |
+| 1.9897 | 1525 | 0.0915        | -               | -                    |
+| 2.0235 | 1550 | 0.0991        | -               | -                    |
+| 2.0561 | 1575 | 0.097         | -               | -                    |
+| 2.0887 | 1600 | 0.0957        | -               | -                    |
+| 2.1213 | 1625 | 0.0968        | -               | -                    |
+| 2.1539 | 1650 | 0.0968        | -               | -                    |
+| 2.1865 | 1675 | 0.0973        | -               | -                    |
+| 2.2191 | 1700 | 0.0936        | -               | -                    |
+| 2.2517 | 1725 | 0.0955        | -               | -                    |
+| 2.2843 | 1750 | 0.0942        | -               | -                    |
+| 2.3169 | 1775 | 0.0939        | -               | -                    |
+| 2.3495 | 1800 | 0.0947        | -               | -                    |
+| 2.3821 | 1825 | 0.0934        | -               | -                    |
+| 2.4147 | 1850 | 0.0919        | -               | -                    |
+| 2.4473 | 1875 | 0.0919        | -               | -                    |
+| 2.4799 | 1900 | 0.0928        | -               | -                    |
+| 2.5125 | 1925 | 0.0927        | -               | -                    |
+| 2.5451 | 1950 | 0.0899        | -               | -                    |
+| 2.5777 | 1975 | 0.0911        | -               | -                    |
+| 2.6103 | 2000 | 0.0915        | 0.0671          | 0.6687               |
+| 2.6429 | 2025 | 0.0905        | -               | -                    |
+| 2.6755 | 2050 | 0.0894        | -               | -                    |
+| 2.7081 | 2075 | 0.0887        | -               | -                    |
+| 2.7407 | 2100 | 0.0903        | -               | -                    |
+| 2.7733 | 2125 | 0.0887        | -               | -                    |
+| 2.8059 | 2150 | 0.0869        | -               | -                    |
+| 2.8385 | 2175 | 0.0871        | -               | -                    |
+| 2.8711 | 2200 | 0.0843        | -               | -                    |
+| 2.9037 | 2225 | 0.0838        | -               | -                    |
+| 2.9363 | 2250 | 0.0864        | -               | -                    |
+| 2.9689 | 2275 | 0.0831        | -               | -                    |
+
+
+### Framework Versions
+- Python: 3.11.10
+- Sentence Transformers: 3.3.1
+- Transformers: 4.48.0
+- 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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+
+*Clearly define terms in order to be accessible across audiences.*
+-->
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