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Model: SamilPwC-AXNode-GenAI/PwC-Embedding_expr
Source: Original Platform

.gitattributes

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1_Pooling/config.json

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+{
+    "word_embedding_dimension": 1024,
+    "pooling_mode_cls_token": false,
+    "pooling_mode_mean_tokens": true,
+    "pooling_mode_max_tokens": false,
+    "pooling_mode_mean_sqrt_len_tokens": false,
+    "pooling_mode_weightedmean_tokens": false,
+    "pooling_mode_lasttoken": false,
+    "include_prompt": true
+}

README.md

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+---
+language:
+- ko
+license: apache-2.0
+tags:
+- sentence-transformers
+- sentence-similarity
+- transformers
+---
+
+## PwC-Embedding-expr
+
+We trained the **PwC-Embedding-expr** model on top of the [multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) embedding model.  
+To enhance performance in Korean, we applied our curated augmentation to STS datasets and fine-tuned the E5 model using a carefully balanced ratio across datasets.
+
+> ⚠️ This is an experimental model and is under continuous development.
+
+### To-do
+- [x] MTEB Leaderboard  
+- [ ] Technical Report
+
+
+## MTEB
+PwC-Embedding_expr was evaluated on the Korean subset of MTEB.  
+A leaderboard link will be added once it is published.
+
+| Task             | PwC-Embedding_expr |
+|------------------|--------------------|
+| KLUE-STS         | 0.88               | 
+| KLUE-TC          | 0.73               |
+| Ko-StrategyQA    | 0.80               | 
+| KorSTS           | 0.84               | 
+| MIRACL-Reranking | 0.72               | 
+| MIRACL-Retrieval | 0.65               |
+| **Average**      | **0.77**           |
+
+
+## Model
+- Base Model: [intfloat/multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct)
+- Model Size: 0.56B
+- Embedding Dimension: 1024
+- Max Input Tokens: 514
+
+
+## Requirements
+It works with the dependencies included in the latest version of MTEB.
+
+
+## Citation
+
+TBD (technical report expected September 2025)

config.json

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+{
+  "architectures": [
+    "XLMRobertaModel"
+  ],
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+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.54.1",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "5.0.0",
+    "transformers": "4.54.1",
+    "pytorch": "2.7.1+cu126"
+  },
+  "model_type": "SentenceTransformer",
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

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modules.json

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+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
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+    "type": "sentence_transformers.models.Pooling"
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+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

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

special_tokens_map.json

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tokenizer_config.json

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