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README.md
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library_name: sentence-transformers
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pipeline_tag: sentence-similarity
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tags:
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- sentence-transformers
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- feature-extraction
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- sentence-similarity
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- transformers
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---
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#
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This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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<!--- Describe your model here -->
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from sentence_transformers import SentenceTransformer
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sentences = ["This is an example sentence", "Each sentence is converted"]
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model = SentenceTransformer(
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embeddings = model.encode(sentences)
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print(embeddings)
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```
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sentences = ['This is an example sentence', 'Each sentence is converted']
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# Load model from HuggingFace Hub
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tokenizer = AutoTokenizer.from_pretrained(
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model = AutoModel.from_pretrained(
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# Tokenize sentences
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encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
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```
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## Evaluation Results
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<!--- Describe how your model was evaluated -->
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The model was trained with the parameters:
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```
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{'batch_size': 128}
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```
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**Loss**:
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**DataLoader**:
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`torch.utils.data.dataloader.DataLoader` of length
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```
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{'batch_size':
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```
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**Loss**:
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`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
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Parameters of the fit()-Method:
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```
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{
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"epochs": 4,
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"evaluation_steps": 1000,
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"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
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"max_grad_norm": 1.0,
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"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
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"optimizer_params": {
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"lr": 1e-06
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},
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"scheduler": "WarmupLinear",
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"steps_per_epoch": null,
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"warmup_steps": 288,
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"weight_decay": 0.01
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}
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```
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## Full Model Architecture
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```
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SentenceTransformer(
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(0): Transformer({'max_seq_length': 128, 'do_lower_case':
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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
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)
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```
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## Citing & Authors
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<!--- Describe where people can find more information -->
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---
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pipeline_tag: sentence-similarity
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tags:
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- sentence-transformers
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- feature-extraction
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- sentence-similarity
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- transformers
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datasets:
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- kornlu
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language:
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- ko
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license: cc-by-4.0
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---
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# bi-matrix/gmatrix-embedding
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ํด๋น ๋ชจ๋ธ์ [KF-DeBERTa](https://huggingface.co/kakaobank/kf-deberta-base) ๋ชจ๋ธ๊ณผ KorSTS, KorNLI ๋ฐ์ดํฐ์
์ ํ์ฉํ์์ผ๋ฉฐ, sentence-transformers์ ๊ณต์ ๋ฌธ์ ๋ด ์๊ฐ๋ [continue-learning](https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/sts/training_stsbenchmark_continue_training.py) ๋ฐฉ๋ฒ์ ํตํด ์๋์ ๊ฐ์ด ํ์ต๋์์ต๋๋ค.
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1. NLI ๋ฐ์ดํฐ์
์ ํตํด nagative sampling ํ MultipleNegativeRankingLoss ํ์ฉ ๋ฐ STS ๋ฐ์ดํฐ์
์ ํตํด CosineSimilarityLoss๋ฅผ ํ์ฉํ์ฌ Multi-task Learning ํ์ต 10epoch ์งํ
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2. Learning Rate๋ฅผ 1e-06์ผ๋ก ์ค์ฌ์ 4epoch ์ถ๊ฐ Multi-task ํ์ต ์งํ
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---
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This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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<!--- Describe your model here -->
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from sentence_transformers import SentenceTransformer
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sentences = ["This is an example sentence", "Each sentence is converted"]
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model = SentenceTransformer("bi-matrix/gmatrix-embedding")
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embeddings = model.encode(sentences)
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print(embeddings)
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```
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sentences = ['This is an example sentence', 'Each sentence is converted']
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# Load model from HuggingFace Hub
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tokenizer = AutoTokenizer.from_pretrained("bi-matrix/gmatrix-embedding")
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model = AutoModel.from_pretrained("bi-matrix/gmatrix-embedding")
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# Tokenize sentences
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encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
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```
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## Evaluation Results
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<!--- Describe how your model was evaluated -->
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KorSTS ํ๊ฐ ๋ฐ์ดํฐ์
์ผ๋ก ํ๊ฐํ ๊ฒฐ๊ณผ์
๋๋ค.
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- Cosine Pearson: 85.77
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- Cosine Spearman: 86.30
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- Manhattan Pearson: 84.84
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- Manhattan Spearman: 85.33
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- Euclidean Pearson: 84.82
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- Euclidean Spearman: 85.29
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- Dot Pearson: 83.19
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- Dot Spearman: 83.19
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<br>
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|model|cosine_pearson|cosine_spearman|euclidean_pearson|euclidean_spearman|manhattan_pearson|manhattan_spearman|dot_pearson|dot_spearman|
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|:-------------------------|-----------------:|------------------:|--------------------:|---------------------:|--------------------:|---------------------:|--------------:|---------------:|
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|[**gmatrix-embedding**](https://huggingface.co/bi-matrix/gmatrix-embedding)|**85.77**|**86.30**|**84.82**|**85.29**|**84.84**|**85.33**|**83.19**|**83.19**|
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|[kf-deberta-multitask](https://huggingface.co/upskyy/kf-deberta-multitask)|85.75|86.25|84.79|85.25|84.80|85.27|82.93|82.86|
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|[ko-sroberta-multitask](https://huggingface.co/jhgan/ko-sroberta-multitask)|84.77|85.6|83.71|84.40|83.70|84.38|82.42|82.33|
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|[ko-sbert-multitask](https://huggingface.co/jhgan/ko-sbert-multitask)|84.13|84.71|82.42|82.66|82.41|82.69|80.05|79.69|
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|[ko-sroberta-base-nli](https://huggingface.co/jhgan/ko-sroberta-nli)|82.83|83.85|82.87|83.29|82.88|83.28|80.34|79.69|
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|[ko-sbert-nli](https://huggingface.co/jhgan/ko-sbert-multitask)|82.24|83.16|82.19|82.31|82.18|82.3|79.3|78.78|
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|[ko-sroberta-sts](https://huggingface.co/jhgan/ko-sroberta-sts)|81.84|81.82|81.15|81.25|81.14|81.25|79.09|78.54|
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|[ko-sbert-sts](https://huggingface.co/jhgan/ko-sbert-sts)|81.55|81.23|79.94|79.79|79.9|79.75|76.02|75.31|
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<br>
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<!--- Describe how your model was evaluated -->
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G-MATRIX Embedding ๋ฐ์ดํฐ์
์ธก์ ๊ฒฐ๊ณผ์
๋๋ค.
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์ฌ๋ 3๋ช
์ด์ 0~5์ ์ผ๋ก ๋ ๋ฌธ์ฅ๊ฐ์ ์ ์ฌ๋๋ฅผ ์ธก์ ํ์ฌ ์ ์๋ฅผ ๋ด๊ณ ํ๊ท ์ ๊ตฌํ์ฌ ๊ฐ ๋ชจ๋ธ์ ์๋ฒ ๋ฉ๊ฐ์ ํตํด
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์ฝ์ฌ์ธ ์ ์ฌ๋, ์ ํด๋ฆฌ๋์ ๊ฑฐ๋ฆฌ, ๋งจํํ ๊ฑฐ๋ฆฌ, Dot-product๋ฅผ ๊ตฌํ์ฌ ํผ์ด์จ, ์คํผ์ด๋ง ์๊ด๊ณ์๋ฅผ ๊ตฌํ ๊ฐ์
๋๋ค.
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- Cosine Pearson: 75.86
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- Cosine Spearman: 65.75
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- Manhattan Pearson: 72.65
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- Manhattan Spearman: 65.20
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- Euclidean Pearson: 72.48
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- Euclidean Spearman: 65.32
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- Dot Pearson: 64.71
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- Dot Spearman: 53.90
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<br>
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model|cosine_pearson|cosine_spearman|euclidean_pearson|euclidean_spearman|manhattan_pearson|manhattan_spearman|dot_pearson|dot_spearman|
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|[**gmatrix-embedding**](https://huggingface.co/bi-matrix/gmatrix-embedding)|**75.86**|**65.75**|**72.65**|**65.20**|**72.48**|**65.32**|**64.71**|**53.90**|
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|[ko-sroberta-multitask](https://huggingface.co/jhgan/ko-sroberta-multitask)|71.78|63.16|70.80|63.47|70.89|63.72|53.57|44.23|
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|[bge-m3](https://huggingface.co/BAAI/bge-m3)|64.15|60.65|61.88|60.68|61.88|60.19|64.16|60.71|
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<br>
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<br>
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## G-MATRIX Embedding ๋ ์ด๋ธ๋ง ํ๋จ ๊ธฐ์ค (KLUE-RoBERTa์ STS ๋ฐ์ดํฐ ์์ฑ ์ฐธ๊ณ )
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1. ๋ ๋ฌธ์ฅ์ ์ ์ฌํ ์ ๋๋ฅผ ๋ณด๊ณ 0~5์ ์ผ๋ก ํ๋จ
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2. ๋ง์ถค๋ฒ, ๋์ด์ฐ๊ธฐ, ์จ์ ์ด๋ ์ผํ ์ฐจ์ด๋ ํ๋จ ๋์์ด ์๋
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3. ๋ฌธ์ฅ์ ์๋, ํํ์ด ๋ด๊ณ ์๋ ์๋ฏธ๋ฅผ ๋น๊ต
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4. ๋ ๋ฌธ์ฅ์ ๊ณตํต์ ์ผ๋ก ์ฌ์ฉ๋ ๋จ์ด์ ์ ๋ฌด๋ฅผ ์ฐพ๋ ๊ฒ์ด ์๋, ๋ฌธ์ฅ์ ์๋ฏธ๊ฐ ์ ์ฌํ์ง๋ฅผ ๋น๊ต
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5. 0์ ์๋ฏธ์ ์ ์ฌ์ฑ์ด ์๋ ๊ฒฝ์ฐ์ด๊ณ , 5๋ ์๋ฏธ์ ์ผ๋ก ๋๋ฑํจ์ ๋ปํจ
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## Training
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The model was trained with the parameters:
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**DataLoader**:
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`torch.utils.data.dataloader.DataLoader` of length 329 with parameters:
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```
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{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
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```
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**Loss**:
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`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
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## Full Model Architecture
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```
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SentenceTransformer(
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(0): Transformer({'max_seq_length': 128, 'do_lower_case': True}) with Transformer model: DeBERTaV2Model
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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})
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```
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## Citing & Authors
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<!--- Describe where people can find more information -->
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[MINSANG SONG] at [BI-Matrix](https://www.bimatrix.co.kr/)
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