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README.md

@@ -1,199 +1,306 @@
 ---
-library_name: transformers
-tags: []
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:392702
+- loss:CosineSimilarityLoss
+base_model: answerdotai/ModernBERT-base
+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.
+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 answerdotai/ModernBERT-base
+  results:
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts dev
+      type: sts_dev
+    metrics:
+    - type: pearson_cosine
+      value: 0.8273878707711191
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.8298080691919564
+      name: Spearman Cosine
+    - type: pearson_euclidean
+      value: 0.8112987734110177
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.8214596205940881
+      name: Spearman Euclidean
+    - type: pearson_manhattan
+      value: 0.8125188338482303
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.8226861322419045
+      name: Spearman Manhattan
+    - type: pearson_dot
+      value: 0.7646820898603437
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.7648333772102188
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.8273878707711191
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.8298080691919564
+      name: Spearman Max
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# SentenceTransformer based on answerdotai/ModernBERT-base
 
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) 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:** [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) <!-- at revision addb15798678d7f76904915cf8045628d402b3ce -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **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     | 0.8273     |
+| spearman_cosine    | 0.8298     |
+| pearson_euclidean  | 0.8112     |
+| spearman_euclidean | 0.8214     |
+| pearson_manhattan  | 0.8125     |
+| spearman_manhattan | 0.8226     |
+| pearson_dot        | 0.7648     |
+| spearman_dot       | 0.7648     |
+| pearson_max        | 0.8273     |
+| **spearman_max**   | **0.8298** |
 
-[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 떠올리기로 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"
+  }
+  ```
+
+### 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",
+}
+```
+
+<!--
+## 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
 
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