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- ---
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- tags:
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- - sentence-transformers
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- - sentence-similarity
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- - feature-extraction
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- - generated_from_trainer
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- - dataset_size:11664
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- - loss:CosineSimilarityLoss
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- base_model: klue/roberta-base
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- widget:
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- - source_sentence: Multi-Class, Multi-Label ์ค‘ BCE ๊ฐ€ ์ข‹์€ task -> ์ด๊ฑด ๋ถ„๋ช… ๋ฉ€ํ‹ฐ๋ผ๋ฒจ์ด์ง€.
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- sentences:
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- - ๊ธฐ๋ณธ ๊ฒฝํ—˜
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- - ๋ฉด์ ‘ ์‹œ์ž‘ ์ธ์‚ฌ
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- - ์ข‹์•„ํ•˜๋Š” ์•„์ด๋Œ
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- - source_sentence: Loss Function ๊ด€๋ จ ์‹ค๋ฌด ๊ฒฝํ—˜ -> [๊ธฐ๋ณธ ๊ฒฝํ—˜] ํ™•๋ฅ  ์˜ˆ์ธก์—์„œ MSE Loss, MAE Loss ์จ
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- ๋ดค์–ด! ์—„์ฒญ ํ˜ผ๋‚ฌ๋‹ค ใ… ใ… 
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- sentences:
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- - Loss Function ์˜ˆ์‹œ
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- - Multi-Label ์—์„œ CE + Softmax ์ ์šฉ ๋ฌธ์ œ์ 
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- - ์šฉ์–ด ์งˆ๋ฌธ
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- - source_sentence: Loss Function ๊ด€๋ จ ์‹ค๋ฌด ๊ฒฝํ—˜ -> [์ƒ์„ธ ๊ฒฝํ—˜] ํ•„์ˆ˜์ ์ธ Loss Term ์ธ Cross-Entropy
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- Loss ๊ฐ€ ๋น ์กŒ๋”๋ผ! ๊ทธ๋ž˜์„œ ๊ทธ๊ฑฐ ํ•ด๊ฒฐํ•ด์„œ ์„ฑ๋Šฅ 20% ๊ฐœ์„ ํ–ˆ์ง€!
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- sentences:
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- - LLM Fine-Tuning ์˜ PEFT
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- - Loss Function ์˜ˆ์‹œ
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- - ๋งˆ์ง€๋ง‰ ํ•  ๋ง
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- - source_sentence: ๊ฑฐ๋Œ€ ์–ธ์–ด ๋ชจ๋ธ ์ •์˜ -> ์ˆ˜๋ฐฑ์–ต ํŒŒ๋ผ๋ฏธํ„ฐ๋กœ ๊ตฌ์„ฑ๋œ ์–ธ์–ด ๋ชจ๋ธ!
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- sentences:
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- - BCE ๊ฐ€ ์ข‹์€ task
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- - LoRA ์™€ QLoRA ์˜ ์ฐจ์ด
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- - ๊ธฐ๋ณธ ๊ฒฝํ—˜
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- - source_sentence: PEFT ๋ฐฉ๋ฒ• 5๊ฐ€์ง€ -> Adapter Layer ์ถ”๊ฐ€ํ•˜๋Š” ๊ฑฐ๋ž‘ ์Œ ๊ทธ๋ฆฌ๊ณ  PEFT! ๊ทธ๊ฑฐ ์•Œ์ง€?
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- sentences:
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- - ๋จธ์‹ ๋Ÿฌ๋‹
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- - LoRA ์™€ QLoRA ์˜ ์ฐจ์ด
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- - ์šฉ์–ด ์งˆ๋ฌธ
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- pipeline_tag: sentence-similarity
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- library_name: sentence-transformers
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- metrics:
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- - pearson_cosine
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- - spearman_cosine
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- model-index:
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- - name: SentenceTransformer based on klue/roberta-base
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- results:
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- - task:
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- type: semantic-similarity
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- name: Semantic Similarity
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- dataset:
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- name: valid evaluator
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- type: valid_evaluator
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- metrics:
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- - type: pearson_cosine
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- value: 0.9999519237820663
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- name: Pearson Cosine
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- - type: spearman_cosine
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- value: 0.3303596809565949
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- name: Spearman Cosine
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- ---
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-
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- # SentenceTransformer based on klue/roberta-base
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-
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- 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.
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-
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- ## Model Details
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-
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- ### Model Description
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- - **Model Type:** Sentence Transformer
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- - **Base model:** [klue/roberta-base](https://huggingface.co/klue/roberta-base) <!-- at revision 02f94ba5e3fcb7e2a58a390b8639b0fac974a8da -->
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- - **Maximum Sequence Length:** 64 tokens
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- - **Output Dimensionality:** 768 dimensions
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- - **Similarity Function:** Cosine Similarity
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- <!-- - **Training Dataset:** Unknown -->
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- <!-- - **Language:** Unknown -->
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- <!-- - **License:** Unknown -->
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-
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- ### Model Sources
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-
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- - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
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- - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
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- - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
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-
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- ### Full Model Architecture
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-
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- ```
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- SentenceTransformer(
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- (0): Transformer({'max_seq_length': 64, 'do_lower_case': True}) with Transformer model: RobertaModel
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- (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})
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- )
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- ```
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-
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- ## Usage
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-
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- ### Direct Usage (Sentence Transformers)
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-
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- First install the Sentence Transformers library:
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-
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- ```bash
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- pip install -U sentence-transformers
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- ```
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-
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- Then you can load this model and run inference.
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- ```python
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- from sentence_transformers import SentenceTransformer
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-
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- # Download from the ๐Ÿค— Hub
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- model = SentenceTransformer("sentence_transformers_model_id")
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- # Run inference
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- sentences = [
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- 'PEFT ๋ฐฉ๋ฒ• 5๊ฐ€์ง€ -> Adapter Layer ์ถ”๊ฐ€ํ•˜๋Š” ๊ฑฐ๋ž‘ ์Œ ๊ทธ๋ฆฌ๊ณ  PEFT! ๊ทธ๊ฑฐ ์•Œ์ง€?',
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- 'LoRA ์™€ QLoRA ์˜ ์ฐจ์ด',
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- '์šฉ์–ด ์งˆ๋ฌธ',
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- ]
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- embeddings = model.encode(sentences)
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- print(embeddings.shape)
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- # [3, 768]
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-
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- # Get the similarity scores for the embeddings
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- similarities = model.similarity(embeddings, embeddings)
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- print(similarities.shape)
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- # [3, 3]
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- ```
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-
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- <!--
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- ### Direct Usage (Transformers)
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-
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- <details><summary>Click to see the direct usage in Transformers</summary>
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-
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- </details>
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- -->
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-
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- <!--
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- ### Downstream Usage (Sentence Transformers)
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-
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- You can finetune this model on your own dataset.
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-
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- <details><summary>Click to expand</summary>
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-
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- </details>
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- -->
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-
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- <!--
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- ### Out-of-Scope Use
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-
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- *List how the model may foreseeably be misused and address what users ought not to do with the model.*
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- -->
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-
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- ## Evaluation
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-
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- ### Metrics
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-
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- #### Semantic Similarity
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-
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- * Dataset: `valid_evaluator`
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- * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
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-
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- | Metric | Value |
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- |:--------------------|:-----------|
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- | pearson_cosine | 1.0 |
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- | **spearman_cosine** | **0.3304** |
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-
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- <!--
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- ## Bias, Risks and Limitations
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-
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- *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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- -->
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-
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- <!--
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- ### Recommendations
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-
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- *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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- -->
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-
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- ## Training Details
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-
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- ### Training Dataset
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-
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- #### Unnamed Dataset
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-
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- * Size: 11,664 training samples
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- * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
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- * Approximate statistics based on the first 1000 samples:
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- | | sentence_0 | sentence_1 | label |
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- |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------|
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- | type | string | string | float |
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- | details | <ul><li>min: 7 tokens</li><li>mean: 29.04 tokens</li><li>max: 64 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 6.85 tokens</li><li>max: 18 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.03</li><li>max: 1.0</li></ul> |
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- * Samples:
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- | sentence_0 | sentence_1 | label |
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- |:---------------------------------------------------------------------------|:-------------------------|:-----------------|
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- | <code>Loss Function ์ •์˜ -> ๋ชจ๋ธ์ด ์ž˜๋ชป ์˜ˆ์ธกํ•œ ๊ฒƒ์— ๋Œ€ํ•œ ํŒจ๋„ํ‹ฐ๋ฅผ ์ˆ˜์‹์œผ๋กœ ์ •์˜ํ•œ ๊ฑฐ ์•„๋‹ˆ์•ผ? ๋งž์ง€?</code> | <code>MSE Loss ์„ค๋ช…</code> | <code>0.0</code> |
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- | <code>์ธ๊ณต์ง€๋Šฅ, ๋จธ์‹ ๋Ÿฌ๋‹, ๋”ฅ๋Ÿฌ๋‹ ์ฐจ์ด -> ๋”ฅ๋Ÿฌ๋‹์€ ์‹ ๊ฒฝ๋ง์ด๋ผ๋Š” ๊ฑธ ์ด์šฉํ•ด์„œ ๋จธ์‹ ๋Ÿฌ๋‹์„ ํ•˜๋Š” ๊ฑฐ์ง€!</code> | <code>์ข‹์•„ํ•˜๋Š” ์•„์ด๋Œ</code> | <code>0.0</code> |
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- | <code>MSE Loss ์„ค๋ช… -> ๊ฐ ๋ฐ์ดํ„ฐ๋ณ„๋กœ ์˜ค์ฐจ๋ฅผ ๊ตฌํ•˜๊ณ  ๊ทธ ์ œ๊ณฑ์„ ํ‰๊ท ํ•œ ๊ฑฐ์•ผ!</code> | <code>๊ฑฐ๋Œ€ ์–ธ์–ด ๋ชจ๋ธ ์ •์˜</code> | <code>0.0</code> |
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- * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
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- ```json
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- {
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- "loss_fct": "torch.nn.modules.loss.MSELoss"
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- }
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- ```
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-
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- ### Training Hyperparameters
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- #### Non-Default Hyperparameters
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-
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- - `eval_strategy`: steps
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- - `per_device_train_batch_size`: 16
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- - `per_device_eval_batch_size`: 16
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- - `num_train_epochs`: 40
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- - `multi_dataset_batch_sampler`: round_robin
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-
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- #### All Hyperparameters
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- <details><summary>Click to expand</summary>
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-
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- - `overwrite_output_dir`: False
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- - `do_predict`: False
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- - `eval_strategy`: steps
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- - `prediction_loss_only`: True
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- - `per_device_train_batch_size`: 16
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- - `per_device_eval_batch_size`: 16
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- - `per_gpu_train_batch_size`: None
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- - `per_gpu_eval_batch_size`: None
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- - `gradient_accumulation_steps`: 1
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- - `eval_accumulation_steps`: None
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- - `torch_empty_cache_steps`: None
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- - `learning_rate`: 5e-05
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- - `weight_decay`: 0.0
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- - `adam_beta1`: 0.9
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- - `adam_beta2`: 0.999
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- - `adam_epsilon`: 1e-08
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- - `max_grad_norm`: 1
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- - `num_train_epochs`: 40
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- - `max_steps`: -1
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- - `lr_scheduler_type`: linear
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- - `lr_scheduler_kwargs`: {}
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- - `warmup_ratio`: 0.0
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- - `warmup_steps`: 0
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- - `log_level`: passive
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- - `log_level_replica`: warning
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- - `log_on_each_node`: True
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- - `logging_nan_inf_filter`: True
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- - `save_safetensors`: True
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- - `save_on_each_node`: False
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- - `save_only_model`: False
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- - `restore_callback_states_from_checkpoint`: False
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- - `no_cuda`: False
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- - `use_cpu`: False
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- - `use_mps_device`: False
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- - `seed`: 42
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- - `data_seed`: None
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- - `jit_mode_eval`: False
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- - `use_ipex`: False
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- - `bf16`: False
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- - `fp16`: False
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- - `fp16_opt_level`: O1
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- - `half_precision_backend`: auto
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- - `bf16_full_eval`: False
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- - `fp16_full_eval`: False
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- - `tf32`: None
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- - `local_rank`: 0
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- - `ddp_backend`: None
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- - `tpu_num_cores`: None
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- - `tpu_metrics_debug`: False
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- - `debug`: []
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- - `dataloader_drop_last`: False
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- - `dataloader_num_workers`: 0
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- - `dataloader_prefetch_factor`: None
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- - `past_index`: -1
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- - `disable_tqdm`: False
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- - `remove_unused_columns`: True
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- - `label_names`: None
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- - `load_best_model_at_end`: False
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- - `ignore_data_skip`: False
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- - `fsdp`: []
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- - `fsdp_min_num_params`: 0
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- - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
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- - `tp_size`: 0
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- - `fsdp_transformer_layer_cls_to_wrap`: None
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- - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
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- - `deepspeed`: None
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- - `label_smoothing_factor`: 0.0
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- - `optim`: adamw_torch
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- - `optim_args`: None
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- - `adafactor`: False
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- - `group_by_length`: False
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- - `length_column_name`: length
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- - `ddp_find_unused_parameters`: None
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- - `ddp_bucket_cap_mb`: None
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- - `ddp_broadcast_buffers`: False
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- - `dataloader_pin_memory`: True
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- - `dataloader_persistent_workers`: False
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- - `skip_memory_metrics`: True
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- - `use_legacy_prediction_loop`: False
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- - `push_to_hub`: False
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- - `resume_from_checkpoint`: None
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- - `hub_model_id`: None
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- - `hub_strategy`: every_save
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- - `hub_private_repo`: None
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- - `hub_always_push`: False
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- - `gradient_checkpointing`: False
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- - `gradient_checkpointing_kwargs`: None
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- - `include_inputs_for_metrics`: False
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- - `include_for_metrics`: []
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- - `eval_do_concat_batches`: True
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- - `fp16_backend`: auto
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- - `push_to_hub_model_id`: None
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- - `push_to_hub_organization`: None
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- - `mp_parameters`:
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- - `auto_find_batch_size`: False
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- - `full_determinism`: False
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- - `torchdynamo`: None
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- - `ray_scope`: last
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- - `ddp_timeout`: 1800
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- - `torch_compile`: False
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- - `torch_compile_backend`: None
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- - `torch_compile_mode`: None
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- - `include_tokens_per_second`: False
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- - `include_num_input_tokens_seen`: False
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- - `neftune_noise_alpha`: None
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- - `optim_target_modules`: None
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- - `batch_eval_metrics`: False
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- - `eval_on_start`: False
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- - `use_liger_kernel`: False
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- - `eval_use_gather_object`: False
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- - `average_tokens_across_devices`: False
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- - `prompts`: None
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- - `batch_sampler`: batch_sampler
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- - `multi_dataset_batch_sampler`: round_robin
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-
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- </details>
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-
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- ### Training Logs
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- <details><summary>Click to expand</summary>
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-
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- | Epoch | Step | Training Loss | valid_evaluator_spearman_cosine |
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- |:-------:|:-----:|:-------------:|:-------------------------------:|
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- | 0.1001 | 73 | - | 0.0133 |
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- | 0.2003 | 146 | - | -0.0061 |
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- | 0.3004 | 219 | - | 0.0476 |
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- | 0.4005 | 292 | - | 0.1975 |
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- | 0.5007 | 365 | - | 0.2232 |
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- | 0.6008 | 438 | - | 0.2484 |
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- | 0.6859 | 500 | 0.0952 | - |
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- | 0.7010 | 511 | - | 0.2631 |
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- | 0.8011 | 584 | - | 0.2481 |
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- | 0.9012 | 657 | - | 0.2594 |
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- | 1.0 | 729 | - | 0.2798 |
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- | 1.0014 | 730 | - | 0.2792 |
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- | 1.1015 | 803 | - | 0.2875 |
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- | 1.2016 | 876 | - | 0.2941 |
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- | 1.3018 | 949 | - | 0.2897 |
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- | 1.3717 | 1000 | 0.0285 | - |
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- | 1.4019 | 1022 | - | 0.3089 |
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- | 1.5021 | 1095 | - | 0.3130 |
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- | 1.6022 | 1168 | - | 0.3121 |
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- | 1.7023 | 1241 | - | 0.3170 |
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- | 1.8025 | 1314 | - | 0.2639 |
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- | 1.9026 | 1387 | - | 0.3031 |
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- | 2.0 | 1458 | - | 0.3203 |
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- | 2.0027 | 1460 | - | 0.3200 |
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- | 2.0576 | 1500 | 0.0215 | - |
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- | 2.1029 | 1533 | - | 0.3205 |
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- | 2.2030 | 1606 | - | 0.3180 |
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- | 2.3032 | 1679 | - | 0.3009 |
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- | 2.4033 | 1752 | - | 0.2967 |
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- | 2.5034 | 1825 | - | 0.3215 |
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- | 2.6036 | 1898 | - | 0.3187 |
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- | 2.7037 | 1971 | - | 0.3230 |
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- | 2.7435 | 2000 | 0.0141 | - |
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- | 2.8038 | 2044 | - | 0.3216 |
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- | 2.9040 | 2117 | - | 0.3152 |
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- | 3.0 | 2187 | - | 0.3206 |
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- | 3.0041 | 2190 | - | 0.3202 |
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- | 3.1043 | 2263 | - | 0.3272 |
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- | 3.2044 | 2336 | - | 0.3270 |
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- | 3.3045 | 2409 | - | 0.3251 |
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- | 3.4047 | 2482 | - | 0.3291 |
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- | 3.4294 | 2500 | 0.0105 | - |
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- | 3.5048 | 2555 | - | 0.3267 |
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- | 3.6049 | 2628 | - | 0.3214 |
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- | 3.7051 | 2701 | - | 0.3275 |
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- | 3.8052 | 2774 | - | 0.3275 |
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- | 3.9053 | 2847 | - | 0.3295 |
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- | 4.0 | 2916 | - | 0.3288 |
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- | 4.0055 | 2920 | - | 0.3296 |
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- | 4.1056 | 2993 | - | 0.3293 |
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- | 4.1152 | 3000 | 0.0078 | - |
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- | 4.2058 | 3066 | - | 0.3280 |
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- | 4.3059 | 3139 | - | 0.3117 |
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- | 4.4060 | 3212 | - | 0.3250 |
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- | 4.5062 | 3285 | - | 0.3212 |
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- | 4.6063 | 3358 | - | 0.3277 |
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- | 4.7064 | 3431 | - | 0.3208 |
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- | 4.8011 | 3500 | 0.0033 | - |
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- | 4.8066 | 3504 | - | 0.3177 |
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- | 4.9067 | 3577 | - | 0.3260 |
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- | 5.0 | 3645 | - | 0.3246 |
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- | 5.0069 | 3650 | - | 0.3259 |
396
- | 5.1070 | 3723 | - | 0.3298 |
397
- | 5.2071 | 3796 | - | 0.3199 |
398
- | 5.3073 | 3869 | - | 0.3297 |
399
- | 5.4074 | 3942 | - | 0.3256 |
400
- | 5.4870 | 4000 | 0.0035 | - |
401
- | 5.5075 | 4015 | - | 0.3286 |
402
- | 5.6077 | 4088 | - | 0.3251 |
403
- | 5.7078 | 4161 | - | 0.3269 |
404
- | 5.8080 | 4234 | - | 0.3298 |
405
- | 5.9081 | 4307 | - | 0.3265 |
406
- | 6.0 | 4374 | - | 0.3047 |
407
- | 6.0082 | 4380 | - | 0.3181 |
408
- | 6.1084 | 4453 | - | 0.3301 |
409
- | 6.1728 | 4500 | 0.0023 | - |
410
- | 6.2085 | 4526 | - | 0.3301 |
411
- | 6.3086 | 4599 | - | 0.3296 |
412
- | 6.4088 | 4672 | - | 0.3251 |
413
- | 6.5089 | 4745 | - | 0.3291 |
414
- | 6.6091 | 4818 | - | 0.3295 |
415
- | 6.7092 | 4891 | - | 0.3289 |
416
- | 6.8093 | 4964 | - | 0.3254 |
417
- | 6.8587 | 5000 | 0.0011 | - |
418
- | 6.9095 | 5037 | - | 0.3271 |
419
- | 7.0 | 5103 | - | 0.3300 |
420
- | 7.0096 | 5110 | - | 0.3300 |
421
- | 7.1097 | 5183 | - | 0.3287 |
422
- | 7.2099 | 5256 | - | 0.3285 |
423
- | 7.3100 | 5329 | - | 0.3291 |
424
- | 7.4102 | 5402 | - | 0.3289 |
425
- | 7.5103 | 5475 | - | 0.3246 |
426
- | 7.5446 | 5500 | 0.0008 | - |
427
- | 7.6104 | 5548 | - | 0.3283 |
428
- | 7.7106 | 5621 | - | 0.3287 |
429
- | 7.8107 | 5694 | - | 0.3243 |
430
- | 7.9108 | 5767 | - | 0.3297 |
431
- | 8.0 | 5832 | - | 0.3278 |
432
- | 8.0110 | 5840 | - | 0.3280 |
433
- | 8.1111 | 5913 | - | 0.3289 |
434
- | 8.2112 | 5986 | - | 0.3250 |
435
- | 8.2305 | 6000 | 0.0014 | - |
436
- | 8.3114 | 6059 | - | 0.3225 |
437
- | 8.4115 | 6132 | - | 0.3290 |
438
- | 8.5117 | 6205 | - | 0.3260 |
439
- | 8.6118 | 6278 | - | 0.3248 |
440
- | 8.7119 | 6351 | - | 0.3285 |
441
- | 8.8121 | 6424 | - | 0.3163 |
442
- | 8.9122 | 6497 | - | 0.3295 |
443
- | 8.9163 | 6500 | 0.0029 | - |
444
- | 9.0 | 6561 | - | 0.3299 |
445
- | 9.0123 | 6570 | - | 0.3299 |
446
- | 9.1125 | 6643 | - | 0.3283 |
447
- | 9.2126 | 6716 | - | 0.3115 |
448
- | 9.3128 | 6789 | - | 0.3150 |
449
- | 9.4129 | 6862 | - | 0.3281 |
450
- | 9.5130 | 6935 | - | 0.3279 |
451
- | 9.6022 | 7000 | 0.0021 | - |
452
- | 9.6132 | 7008 | - | 0.3279 |
453
- | 9.7133 | 7081 | - | 0.3285 |
454
- | 9.8134 | 7154 | - | 0.3263 |
455
- | 9.9136 | 7227 | - | 0.3301 |
456
- | 10.0 | 7290 | - | 0.3291 |
457
- | 10.0137 | 7300 | - | 0.3286 |
458
- | 10.1139 | 7373 | - | 0.3271 |
459
- | 10.2140 | 7446 | - | 0.3292 |
460
- | 10.2881 | 7500 | 0.0022 | - |
461
- | 10.3141 | 7519 | - | 0.3302 |
462
- | 10.4143 | 7592 | - | 0.3026 |
463
- | 10.5144 | 7665 | - | 0.3007 |
464
- | 10.6145 | 7738 | - | 0.3099 |
465
- | 10.7147 | 7811 | - | 0.3301 |
466
- | 10.8148 | 7884 | - | 0.3247 |
467
- | 10.9150 | 7957 | - | 0.3287 |
468
- | 10.9739 | 8000 | 0.0027 | - |
469
- | 11.0 | 8019 | - | 0.3289 |
470
- | 11.0151 | 8030 | - | 0.3289 |
471
- | 11.1152 | 8103 | - | 0.3297 |
472
- | 11.2154 | 8176 | - | 0.3303 |
473
- | 11.3155 | 8249 | - | 0.3299 |
474
- | 11.4156 | 8322 | - | 0.3301 |
475
- | 11.5158 | 8395 | - | 0.3292 |
476
- | 11.6159 | 8468 | - | 0.3295 |
477
- | 11.6598 | 8500 | 0.0008 | - |
478
- | 11.7160 | 8541 | - | 0.3286 |
479
- | 11.8162 | 8614 | - | 0.3283 |
480
- | 11.9163 | 8687 | - | 0.3303 |
481
- | 12.0 | 8748 | - | 0.3302 |
482
- | 12.0165 | 8760 | - | 0.3301 |
483
- | 12.1166 | 8833 | - | 0.3302 |
484
- | 12.2167 | 8906 | - | 0.3301 |
485
- | 12.3169 | 8979 | - | 0.3300 |
486
- | 12.3457 | 9000 | 0.0008 | - |
487
- | 12.4170 | 9052 | - | 0.3301 |
488
- | 12.5171 | 9125 | - | 0.3301 |
489
- | 12.6173 | 9198 | - | 0.3299 |
490
- | 12.7174 | 9271 | - | 0.3296 |
491
- | 12.8176 | 9344 | - | 0.3297 |
492
- | 12.9177 | 9417 | - | 0.3304 |
493
- | 13.0 | 9477 | - | 0.3301 |
494
- | 13.0178 | 9490 | - | 0.3301 |
495
- | 13.0316 | 9500 | 0.0003 | - |
496
- | 13.1180 | 9563 | - | 0.3301 |
497
- | 13.2181 | 9636 | - | 0.3302 |
498
- | 13.3182 | 9709 | - | 0.3301 |
499
- | 13.4184 | 9782 | - | 0.3302 |
500
- | 13.5185 | 9855 | - | 0.3303 |
501
- | 13.6187 | 9928 | - | 0.3303 |
502
- | 13.7174 | 10000 | 0.0003 | - |
503
- | 13.7188 | 10001 | - | 0.3302 |
504
- | 13.8189 | 10074 | - | 0.3302 |
505
- | 13.9191 | 10147 | - | 0.3302 |
506
- | 14.0 | 10206 | - | 0.3295 |
507
- | 14.0192 | 10220 | - | 0.3297 |
508
- | 14.1193 | 10293 | - | 0.3296 |
509
- | 14.2195 | 10366 | - | 0.3302 |
510
- | 14.3196 | 10439 | - | 0.3190 |
511
- | 14.4033 | 10500 | 0.0013 | - |
512
- | 14.4198 | 10512 | - | 0.3301 |
513
- | 14.5199 | 10585 | - | 0.3281 |
514
- | 14.6200 | 10658 | - | 0.3297 |
515
- | 14.7202 | 10731 | - | 0.3288 |
516
- | 14.8203 | 10804 | - | 0.3291 |
517
- | 14.9204 | 10877 | - | 0.3294 |
518
- | 15.0 | 10935 | - | 0.3303 |
519
- | 15.0206 | 10950 | - | 0.3303 |
520
- | 15.0892 | 11000 | 0.0013 | - |
521
- | 15.1207 | 11023 | - | 0.3303 |
522
- | 15.2209 | 11096 | - | 0.3304 |
523
- | 15.3210 | 11169 | - | 0.3304 |
524
- | 15.4211 | 11242 | - | 0.3304 |
525
- | 15.5213 | 11315 | - | 0.3304 |
526
- | 15.6214 | 11388 | - | 0.3304 |
527
- | 15.7215 | 11461 | - | 0.3304 |
528
- | 15.7750 | 11500 | 0.0006 | - |
529
- | 15.8217 | 11534 | - | 0.3304 |
530
- | 15.9218 | 11607 | - | 0.3304 |
531
- | 16.0 | 11664 | - | 0.3304 |
532
- | 16.0219 | 11680 | - | 0.3304 |
533
- | 16.1221 | 11753 | - | 0.3304 |
534
- | 16.2222 | 11826 | - | 0.3304 |
535
- | 16.3224 | 11899 | - | 0.3304 |
536
- | 16.4225 | 11972 | - | 0.3304 |
537
- | 16.4609 | 12000 | 0.0001 | - |
538
- | 16.5226 | 12045 | - | 0.3304 |
539
- | 16.6228 | 12118 | - | 0.3304 |
540
- | 16.7229 | 12191 | - | 0.3304 |
541
- | 16.8230 | 12264 | - | 0.3304 |
542
- | 16.9232 | 12337 | - | 0.3304 |
543
- | 17.0 | 12393 | - | 0.3304 |
544
- | 17.0233 | 12410 | - | 0.3304 |
545
- | 17.1235 | 12483 | - | 0.3304 |
546
- | 17.1468 | 12500 | 0.0001 | - |
547
- | 17.2236 | 12556 | - | 0.3304 |
548
- | 17.3237 | 12629 | - | 0.3304 |
549
- | 17.4239 | 12702 | - | 0.3304 |
550
- | 17.5240 | 12775 | - | 0.3304 |
551
- | 17.6241 | 12848 | - | 0.3304 |
552
- | 17.7243 | 12921 | - | 0.3304 |
553
- | 17.8244 | 12994 | - | 0.3304 |
554
- | 17.8326 | 13000 | 0.0001 | - |
555
- | 17.9246 | 13067 | - | 0.3304 |
556
- | 18.0 | 13122 | - | 0.3304 |
557
- | 18.0247 | 13140 | - | 0.3304 |
558
- | 18.1248 | 13213 | - | 0.3304 |
559
- | 18.2250 | 13286 | - | 0.3304 |
560
- | 18.3251 | 13359 | - | 0.3304 |
561
- | 18.4252 | 13432 | - | 0.3304 |
562
- | 18.5185 | 13500 | 0.0001 | - |
563
- | 18.5254 | 13505 | - | 0.3304 |
564
- | 18.6255 | 13578 | - | 0.3304 |
565
- | 18.7257 | 13651 | - | 0.3304 |
566
- | 18.8258 | 13724 | - | 0.3304 |
567
- | 18.9259 | 13797 | - | 0.3304 |
568
- | 19.0 | 13851 | - | 0.3304 |
569
- | 19.0261 | 13870 | - | 0.3304 |
570
- | 19.1262 | 13943 | - | 0.3304 |
571
- | 19.2044 | 14000 | 0.0001 | - |
572
- | 19.2263 | 14016 | - | 0.3304 |
573
- | 19.3265 | 14089 | - | 0.3304 |
574
- | 19.4266 | 14162 | - | 0.3304 |
575
- | 19.5267 | 14235 | - | 0.3304 |
576
- | 19.6269 | 14308 | - | 0.3304 |
577
- | 19.7270 | 14381 | - | 0.3304 |
578
- | 19.8272 | 14454 | - | 0.3304 |
579
- | 19.8903 | 14500 | 0.0001 | - |
580
- | 19.9273 | 14527 | - | 0.3304 |
581
- | 20.0 | 14580 | - | 0.3304 |
582
- | 20.0274 | 14600 | - | 0.3304 |
583
- | 20.1276 | 14673 | - | 0.3304 |
584
- | 20.2277 | 14746 | - | 0.3304 |
585
- | 20.3278 | 14819 | - | 0.3304 |
586
- | 20.4280 | 14892 | - | 0.3304 |
587
- | 20.5281 | 14965 | - | 0.3304 |
588
- | 20.5761 | 15000 | 0.0001 | - |
589
- | 20.6283 | 15038 | - | 0.3304 |
590
- | 20.7284 | 15111 | - | 0.3304 |
591
- | 20.8285 | 15184 | - | 0.3304 |
592
- | 20.9287 | 15257 | - | 0.3304 |
593
- | 21.0 | 15309 | - | 0.3304 |
594
- | 21.0288 | 15330 | - | 0.3304 |
595
- | 21.1289 | 15403 | - | 0.3304 |
596
- | 21.2291 | 15476 | - | 0.3304 |
597
- | 21.2620 | 15500 | 0.0001 | - |
598
- | 21.3292 | 15549 | - | 0.3304 |
599
- | 21.4294 | 15622 | - | 0.3304 |
600
- | 21.5295 | 15695 | - | 0.3304 |
601
- | 21.6296 | 15768 | - | 0.3304 |
602
- | 21.7298 | 15841 | - | 0.3304 |
603
- | 21.8299 | 15914 | - | 0.3304 |
604
- | 21.9300 | 15987 | - | 0.3304 |
605
- | 21.9479 | 16000 | 0.0001 | - |
606
- | 22.0 | 16038 | - | 0.3304 |
607
- | 22.0302 | 16060 | - | 0.3304 |
608
- | 22.1303 | 16133 | - | 0.3304 |
609
- | 22.2305 | 16206 | - | 0.3304 |
610
- | 22.3306 | 16279 | - | 0.3304 |
611
- | 22.4307 | 16352 | - | 0.3304 |
612
- | 22.5309 | 16425 | - | 0.3304 |
613
- | 22.6310 | 16498 | - | 0.3304 |
614
- | 22.6337 | 16500 | 0.0001 | - |
615
- | 22.7311 | 16571 | - | 0.3304 |
616
- | 22.8313 | 16644 | - | 0.3304 |
617
- | 22.9314 | 16717 | - | 0.3304 |
618
- | 23.0 | 16767 | - | 0.3304 |
619
- | 23.0316 | 16790 | - | 0.3304 |
620
- | 23.1317 | 16863 | - | 0.3304 |
621
- | 23.2318 | 16936 | - | 0.3304 |
622
- | 23.3196 | 17000 | 0.0001 | - |
623
- | 23.3320 | 17009 | - | 0.3304 |
624
- | 23.4321 | 17082 | - | 0.3304 |
625
- | 23.5322 | 17155 | - | 0.3304 |
626
- | 23.6324 | 17228 | - | 0.3304 |
627
- | 23.7325 | 17301 | - | 0.3304 |
628
- | 23.8326 | 17374 | - | 0.3304 |
629
- | 23.9328 | 17447 | - | 0.3304 |
630
- | 24.0 | 17496 | - | 0.3304 |
631
- | 24.0055 | 17500 | 0.0001 | - |
632
- | 24.0329 | 17520 | - | 0.3304 |
633
- | 24.1331 | 17593 | - | 0.3304 |
634
- | 24.2332 | 17666 | - | 0.3304 |
635
- | 24.3333 | 17739 | - | 0.3304 |
636
- | 24.4335 | 17812 | - | 0.3304 |
637
- | 24.5336 | 17885 | - | 0.3304 |
638
- | 24.6337 | 17958 | - | 0.3304 |
639
- | 24.6914 | 18000 | 0.0001 | - |
640
- | 24.7339 | 18031 | - | 0.3304 |
641
- | 24.8340 | 18104 | - | 0.3304 |
642
- | 24.9342 | 18177 | - | 0.3304 |
643
- | 25.0 | 18225 | - | 0.3304 |
644
- | 25.0343 | 18250 | - | 0.3304 |
645
- | 25.1344 | 18323 | - | 0.3299 |
646
- | 25.2346 | 18396 | - | 0.3266 |
647
- | 25.3347 | 18469 | - | 0.3304 |
648
- | 25.3772 | 18500 | 0.0014 | - |
649
- | 25.4348 | 18542 | - | 0.3304 |
650
- | 25.5350 | 18615 | - | 0.3304 |
651
- | 25.6351 | 18688 | - | 0.3304 |
652
- | 25.7353 | 18761 | - | 0.3304 |
653
- | 25.8354 | 18834 | - | 0.3304 |
654
- | 25.9355 | 18907 | - | 0.3304 |
655
- | 26.0 | 18954 | - | 0.3304 |
656
- | 26.0357 | 18980 | - | 0.3304 |
657
- | 26.0631 | 19000 | 0.0003 | - |
658
- | 26.1358 | 19053 | - | 0.3303 |
659
- | 26.2359 | 19126 | - | 0.3303 |
660
- | 26.3361 | 19199 | - | 0.3304 |
661
- | 26.4362 | 19272 | - | 0.3303 |
662
- | 26.5364 | 19345 | - | 0.3303 |
663
- | 26.6365 | 19418 | - | 0.3303 |
664
- | 26.7366 | 19491 | - | 0.3304 |
665
- | 26.7490 | 19500 | 0.0006 | - |
666
- | 26.8368 | 19564 | - | 0.3304 |
667
- | 26.9369 | 19637 | - | 0.3304 |
668
- | 27.0 | 19683 | - | 0.3304 |
669
- | 27.0370 | 19710 | - | 0.3304 |
670
- | 27.1372 | 19783 | - | 0.3304 |
671
- | 27.2373 | 19856 | - | 0.3304 |
672
- | 27.3374 | 19929 | - | 0.3304 |
673
- | 27.4348 | 20000 | 0.0001 | - |
674
- | 27.4376 | 20002 | - | 0.3304 |
675
- | 27.5377 | 20075 | - | 0.3304 |
676
- | 27.6379 | 20148 | - | 0.3304 |
677
- | 27.7380 | 20221 | - | 0.3304 |
678
- | 27.8381 | 20294 | - | 0.3303 |
679
- | 27.9383 | 20367 | - | 0.3303 |
680
- | 28.0 | 20412 | - | 0.3303 |
681
- | 28.0384 | 20440 | - | 0.3303 |
682
- | 28.1207 | 20500 | 0.0001 | - |
683
- | 28.1385 | 20513 | - | 0.3303 |
684
- | 28.2387 | 20586 | - | 0.3303 |
685
- | 28.3388 | 20659 | - | 0.3303 |
686
- | 28.4390 | 20732 | - | 0.3303 |
687
- | 28.5391 | 20805 | - | 0.3303 |
688
- | 28.6392 | 20878 | - | 0.3303 |
689
- | 28.7394 | 20951 | - | 0.3303 |
690
- | 28.8066 | 21000 | 0.0001 | - |
691
- | 28.8395 | 21024 | - | 0.3303 |
692
- | 28.9396 | 21097 | - | 0.3303 |
693
- | 29.0 | 21141 | - | 0.3303 |
694
- | 29.0398 | 21170 | - | 0.3303 |
695
- | 29.1399 | 21243 | - | 0.3303 |
696
- | 29.2401 | 21316 | - | 0.3303 |
697
- | 29.3402 | 21389 | - | 0.3303 |
698
- | 29.4403 | 21462 | - | 0.3303 |
699
- | 29.4925 | 21500 | 0.0001 | - |
700
- | 29.5405 | 21535 | - | 0.3303 |
701
- | 29.6406 | 21608 | - | 0.3303 |
702
- | 29.7407 | 21681 | - | 0.3303 |
703
- | 29.8409 | 21754 | - | 0.3303 |
704
- | 29.9410 | 21827 | - | 0.3303 |
705
- | 30.0 | 21870 | - | 0.3303 |
706
- | 30.0412 | 21900 | - | 0.3303 |
707
- | 30.1413 | 21973 | - | 0.3303 |
708
- | 30.1783 | 22000 | 0.0001 | - |
709
- | 30.2414 | 22046 | - | 0.3303 |
710
- | 30.3416 | 22119 | - | 0.3303 |
711
- | 30.4417 | 22192 | - | 0.3303 |
712
- | 30.5418 | 22265 | - | 0.3303 |
713
- | 30.6420 | 22338 | - | 0.3303 |
714
- | 30.7421 | 22411 | - | 0.3303 |
715
- | 30.8422 | 22484 | - | 0.3303 |
716
- | 30.8642 | 22500 | 0.0001 | - |
717
- | 30.9424 | 22557 | - | 0.3304 |
718
- | 31.0 | 22599 | - | 0.3304 |
719
- | 31.0425 | 22630 | - | 0.3304 |
720
- | 31.1427 | 22703 | - | 0.3304 |
721
- | 31.2428 | 22776 | - | 0.3304 |
722
- | 31.3429 | 22849 | - | 0.3304 |
723
- | 31.4431 | 22922 | - | 0.3304 |
724
- | 31.5432 | 22995 | - | 0.3304 |
725
- | 31.5501 | 23000 | 0.0001 | - |
726
- | 31.6433 | 23068 | - | 0.3304 |
727
- | 31.7435 | 23141 | - | 0.3304 |
728
- | 31.8436 | 23214 | - | 0.3304 |
729
- | 31.9438 | 23287 | - | 0.3304 |
730
- | 32.0 | 23328 | - | 0.3304 |
731
- | 32.0439 | 23360 | - | 0.3304 |
732
- | 32.1440 | 23433 | - | 0.3304 |
733
- | 32.2359 | 23500 | 0.0001 | - |
734
- | 32.2442 | 23506 | - | 0.3304 |
735
- | 32.3443 | 23579 | - | 0.3304 |
736
- | 32.4444 | 23652 | - | 0.3304 |
737
- | 32.5446 | 23725 | - | 0.3304 |
738
- | 32.6447 | 23798 | - | 0.3304 |
739
- | 32.7449 | 23871 | - | 0.3304 |
740
- | 32.8450 | 23944 | - | 0.3304 |
741
- | 32.9218 | 24000 | 0.0001 | - |
742
- | 32.9451 | 24017 | - | 0.3304 |
743
- | 33.0 | 24057 | - | 0.3304 |
744
- | 33.0453 | 24090 | - | 0.3304 |
745
- | 33.1454 | 24163 | - | 0.3304 |
746
- | 33.2455 | 24236 | - | 0.3304 |
747
- | 33.3457 | 24309 | - | 0.3304 |
748
- | 33.4458 | 24382 | - | 0.3304 |
749
- | 33.5460 | 24455 | - | 0.3304 |
750
- | 33.6077 | 24500 | 0.0 | - |
751
- | 33.6461 | 24528 | - | 0.3304 |
752
- | 33.7462 | 24601 | - | 0.3304 |
753
- | 33.8464 | 24674 | - | 0.3304 |
754
- | 33.9465 | 24747 | - | 0.3304 |
755
- | 34.0 | 24786 | - | 0.3304 |
756
- | 34.0466 | 24820 | - | 0.3304 |
757
- | 34.1468 | 24893 | - | 0.3304 |
758
- | 34.2469 | 24966 | - | 0.3304 |
759
- | 34.2936 | 25000 | 0.0 | - |
760
- | 34.3471 | 25039 | - | 0.3304 |
761
- | 34.4472 | 25112 | - | 0.3304 |
762
- | 34.5473 | 25185 | - | 0.3304 |
763
- | 34.6475 | 25258 | - | 0.3304 |
764
- | 34.7476 | 25331 | - | 0.3304 |
765
- | 34.8477 | 25404 | - | 0.3304 |
766
- | 34.9479 | 25477 | - | 0.3304 |
767
- | 34.9794 | 25500 | 0.0 | - |
768
- | 35.0 | 25515 | - | 0.3304 |
769
- | 35.0480 | 25550 | - | 0.3304 |
770
- | 35.1481 | 25623 | - | 0.3304 |
771
- | 35.2483 | 25696 | - | 0.3304 |
772
- | 35.3484 | 25769 | - | 0.3304 |
773
- | 35.4486 | 25842 | - | 0.3304 |
774
- | 35.5487 | 25915 | - | 0.3304 |
775
- | 35.6488 | 25988 | - | 0.3304 |
776
- | 35.6653 | 26000 | 0.0 | - |
777
- | 35.7490 | 26061 | - | 0.3304 |
778
- | 35.8491 | 26134 | - | 0.3304 |
779
- | 35.9492 | 26207 | - | 0.3304 |
780
- | 36.0 | 26244 | - | 0.3304 |
781
- | 36.0494 | 26280 | - | 0.3304 |
782
- | 36.1495 | 26353 | - | 0.3304 |
783
- | 36.2497 | 26426 | - | 0.3304 |
784
- | 36.3498 | 26499 | - | 0.3304 |
785
- | 36.3512 | 26500 | 0.0 | - |
786
- | 36.4499 | 26572 | - | 0.3304 |
787
- | 36.5501 | 26645 | - | 0.3304 |
788
- | 36.6502 | 26718 | - | 0.3304 |
789
- | 36.7503 | 26791 | - | 0.3304 |
790
- | 36.8505 | 26864 | - | 0.3304 |
791
- | 36.9506 | 26937 | - | 0.3304 |
792
- | 37.0 | 26973 | - | 0.3304 |
793
- | 37.0370 | 27000 | 0.0 | - |
794
- | 37.0508 | 27010 | - | 0.3304 |
795
- | 37.1509 | 27083 | - | 0.3304 |
796
- | 37.2510 | 27156 | - | 0.3304 |
797
- | 37.3512 | 27229 | - | 0.3304 |
798
- | 37.4513 | 27302 | - | 0.3304 |
799
- | 37.5514 | 27375 | - | 0.3304 |
800
- | 37.6516 | 27448 | - | 0.3304 |
801
- | 37.7229 | 27500 | 0.0 | - |
802
- | 37.7517 | 27521 | - | 0.3304 |
803
- | 37.8519 | 27594 | - | 0.3304 |
804
- | 37.9520 | 27667 | - | 0.3304 |
805
- | 38.0 | 27702 | - | 0.3304 |
806
- | 38.0521 | 27740 | - | 0.3304 |
807
- | 38.1523 | 27813 | - | 0.3304 |
808
- | 38.2524 | 27886 | - | 0.3304 |
809
- | 38.3525 | 27959 | - | 0.3304 |
810
- | 38.4088 | 28000 | 0.0 | - |
811
- | 38.4527 | 28032 | - | 0.3304 |
812
- | 38.5528 | 28105 | - | 0.3304 |
813
- | 38.6529 | 28178 | - | 0.3304 |
814
- | 38.7531 | 28251 | - | 0.3304 |
815
- | 38.8532 | 28324 | - | 0.3304 |
816
- | 38.9534 | 28397 | - | 0.3304 |
817
- | 39.0 | 28431 | - | 0.3304 |
818
- | 39.0535 | 28470 | - | 0.3304 |
819
- | 39.0947 | 28500 | 0.0 | - |
820
- | 39.1536 | 28543 | - | 0.3304 |
821
- | 39.2538 | 28616 | - | 0.3304 |
822
- | 39.3539 | 28689 | - | 0.3304 |
823
- | 39.4540 | 28762 | - | 0.3304 |
824
- | 39.5542 | 28835 | - | 0.3304 |
825
-
826
- </details>
827
-
828
- ### Framework Versions
829
- - Python: 3.10.11
830
- - Sentence Transformers: 4.1.0
831
- - Transformers: 4.51.3
832
- - PyTorch: 2.6.0+cu124
833
- - Accelerate: 1.0.1
834
- - Datasets: 3.5.0
835
- - Tokenizers: 0.21.1
836
-
837
- ## Citation
838
-
839
- ### BibTeX
840
-
841
- #### Sentence Transformers
842
- ```bibtex
843
- @inproceedings{reimers-2019-sentence-bert,
844
- title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
845
- author = "Reimers, Nils and Gurevych, Iryna",
846
- booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
847
- month = "11",
848
- year = "2019",
849
- publisher = "Association for Computational Linguistics",
850
- url = "https://arxiv.org/abs/1908.10084",
851
- }
852
- ```
853
-
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- ## Model Card Authors
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-
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- *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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-
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- ## Model Card Contact
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1
+ ---
2
+ license: mit
3
+ language:
4
+ - ko
5
+ base_model:
6
+ - klue/roberta-base
7
+ pipeline_tag: text-classification
8
+ ---
9
+
10
+ S-BERT model for **Successful Answer Prediction** for **Interview** function of [Oh-LoRA ๐Ÿ‘ฑโ€โ™€๏ธ (์˜ค๋กœ๋ผ) ML Tutor](https://github.com/WannaBeSuperteur/AI_Projects/tree/main/2025_07_02_OhLoRA_ML_Tutor).
11
+
12
+ * This S-BERT model is a Fine-tuned version of ```klue/roberta-base```.
13
+ * [Detailed info (in Korean)](https://github.com/WannaBeSuperteur/AI_Projects/tree/main/2025_07_02_OhLoRA_ML_Tutor/ai_interview#1-1-%EC%82%AC%EC%9A%A9%EC%9E%90-%EB%8B%B5%EB%B3%80-%EC%84%B1%EA%B3%B5-%EC%97%AC%EB%B6%80-%ED%8F%89%EA%B0%80)