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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+assets/concept.jpg filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

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+{
+    "word_embedding_dimension": 1280,
+    "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

@@ -0,0 +1,104 @@
+---
+language:
+- ja
+license: mit
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+tags:
+- mirei
+- llama
+- transformers
+- llm2vec
+- text-embedding
+- embeddings
+- feature-extraction
+- retrieval
+base_model: iamtatsuki05/Sentence-Sarashina-Bi-0.5B-PT
+datasets:
+- cl-nagoya/ruri-v3-dataset-ft
+---
+
+# Sentence-Sarashina-Bi-0.5B
+
+English / [Japanese](README_JA.md)
+
+
+## Overview
+Sentence-Sarashina-Bi-0.5B fine-tunes [iamtatsuki05/Sentence-Sarashina-Bi-0.5B-PT](https://huggingface.co/iamtatsuki05/Sentence-Sarashina-Bi-0.5B-PT) with supervised examples from [cl-nagoya/ruri-v3-dataset-ft](https://huggingface.co/datasets/cl-nagoya/ruri-v3-dataset-ft), resulting in 1,280-dimensional Japanese embeddings.
+
+
+- **[Hugging Face Collection](https://huggingface.co/collections/iamtatsuki05/mirei)**
+- **[GitHub](https://github.com/iamtatsuki05/MIREI)**
+
+![Consept](assets/concept.jpg)
+
+## Usage
+### Requirements
+```
+sentence-transformers>=4.1.0
+transformers>=4.51.0
+accelerate>=1.6.0
+sentencepiece>=0.2.0
+flash-attn>=2.7.3
+```
+
+### Sample Code
+```python
+import torch
+from sentence_transformers import SentenceTransformer
+
+model_name = "iamtatsuki05/Sentence-Sarashina-Bi-0.5B"
+model_kwargs = {
+  "torch_dtype": torch.bfloat16,
+  "attn_implementation": "flash_attention_2",
+}
+model = SentenceTransformer(model_name, model_kwargs=model_kwargs)
+
+queries = ["ハチワレはどのようなキャラクターですか？"]
+docs = [
+    "ハチワレは、『ちいかわ』に登場する猫風のキャラクターで、明るく社交的、前向きな性格が特徴。ちいかわたちと共に日常を楽しみつつ、討伐などの冒険にも積極的に挑む存在です。",
+    "うさぎは、天真爛漫でマイペースな性格が特徴のキャラクターで、突飛な行動力と鋭い直感でちいかわたちを引っ張る存在。自由気ままながらも仲間思いな一面を併せ持ちます。",
+]
+q_emb = model.encode(queries, normalize_embeddings=True)
+d_emb = model.encode(docs, normalize_embeddings=True)
+scores = model.similarity(q_emb, d_emb)
+print(scores)
+```
+
+
+## Model Details
+- **Base model:** [iamtatsuki05/Sentence-Sarashina-Bi-0.5B-PT](https://huggingface.co/iamtatsuki05/Sentence-Sarashina-Bi-0.5B-PT)
+- **Architecture:** Llama
+- **Maximum sequence length:** 8,192 tokens
+- **Embedding dimension:** 1280 (mean pooling)
+- **Tokenizer:** SentencePiece / vocabulary size 102,400
+- **Positional encoding:** RoPE
+- **Supported languages:** Japanese
+- **Similarity metric:** cosine
+
+
+## Model Series
+
+The encoders below inherit from weakly supervised models and receive supervised refinement on [cl-nagoya/ruri-v3-dataset-ft](https://huggingface.co/datasets/cl-nagoya/ruri-v3-dataset-ft).
+
+| ID                                                                                                          | Architecture | #Param. | #Param.<br>w/o Emb. | JMTEB-Avg | JMTEB-Retrieval | JMTEB-STS | JMTEB-Classification | JMTEB-Reranking | JMTEB-Clustering |
+|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|
+| [iamtatsuki05/Sentence-ModernBERT-JP-0.5B](https://huggingface.co/iamtatsuki05/Sentence-ModernBERT-JP-0.5B) | ModernBERT   | 679M    | 548M                | 65.31     | 57.95           | 80.78     | 71.73                | 75.50           | **50.03**        |
+| [iamtatsuki05/Sentence-Llama-Bi-JP-0.5B](https://huggingface.co/iamtatsuki05/Sentence-Llama-Bi-JP-0.5B)     | Llama        | 661M    | 530M                | 61.02     | 51.55           | 78.01     | 68.51                | 71.96           | 48.69            |
+| [iamtatsuki05/Sentence-Sarashina-Bi-0.5B](https://huggingface.co/iamtatsuki05/Sentence-Sarashina-Bi-0.5B)<br>(this model)   | Llama        | 661M    | 530M                | **66.84** | **59.00**       | **83.50** | **74.35**            | **77.36**       | 49.40            |
+
+
+## Licence
+This model is distributed under the [MIT License](https://opensource.org/license/mit/).
+
+
+## How to Cite
+
+```tex
+@article{MIREI
+  title={同一条件下における Encoder/Decoderアーキテクチャの文埋め込み性能分析},
+  author={岡田 龍樹 and 杉本 徹},
+  journal={言語処理学会第 32 回年次大会 (NLP2026)},
+  year={2026}
+}
+```

README_JA.md

@@ -0,0 +1,104 @@
+---
+language:
+- ja
+license: mit
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+tags:
+- mirei
+- llama
+- transformers
+- llm2vec
+- text-embedding
+- embeddings
+- feature-extraction
+- retrieval
+base_model: iamtatsuki05/Sentence-Sarashina-Bi-0.5B-PT
+datasets:
+- cl-nagoya/ruri-v3-dataset-ft
+---
+
+# Sentence-Sarashina-Bi-0.5B
+
+[English](README.md) / Japanese
+
+
+## Overview
+Sentence-Sarashina-Bi-0.5B は、 [iamtatsuki05/Sentence-Sarashina-Bi-0.5B-PT](https://huggingface.co/iamtatsuki05/Sentence-Sarashina-Bi-0.5B-PT) を [cl-nagoya/ruri-v3-dataset-ft](https://huggingface.co/datasets/cl-nagoya/ruri-v3-dataset-ft) で教師あり対照学習を行った 1280 次元の日本語埋め込みモデルです。
+
+
+- **[Hugging Face Collection](https://huggingface.co/collections/iamtatsuki05/mirei)**
+- **[GitHub](https://github.com/iamtatsuki05/MIREI)**
+
+![Consept](assets/concept.jpg)
+
+## Usage
+### Requirements
+```
+sentence-transformers>=4.1.0
+transformers>=4.51.0
+accelerate>=1.6.0
+sentencepiece>=0.2.0
+flash-attn>=2.7.3
+```
+
+### Sample Code
+```python
+import torch
+from sentence_transformers import SentenceTransformer
+
+model_name = "iamtatsuki05/Sentence-Sarashina-Bi-0.5B"
+model_kwargs = {
+  "torch_dtype": torch.bfloat16,
+  "attn_implementation": "flash_attention_2",
+}
+model = SentenceTransformer(model_name, model_kwargs=model_kwargs)
+
+queries = ["ハチワレはどのようなキャラクターですか？"]
+docs = [
+    "ハチワレは、『ちいかわ』に登場する猫風のキャラクターで、明るく社交的、前向きな性格が特徴。ちいかわたちと共に日常を楽しみつつ、討伐などの冒険にも積極的に挑む存在です。",
+    "うさぎは、天真爛漫でマイペースな性格が特徴のキャラクターで、突飛な行動力と鋭い直感でちいかわたちを引っ張る存在。自由気ままながらも仲間思いな一面を併せ持ちます。",
+]
+q_emb = model.encode(queries, normalize_embeddings=True)
+d_emb = model.encode(docs, normalize_embeddings=True)
+scores = model.similarity(q_emb, d_emb)
+print(scores)
+```
+
+
+## Model Details
+- **ベースモデル:** [iamtatsuki05/Sentence-Sarashina-Bi-0.5B-PT](https://huggingface.co/iamtatsuki05/Sentence-Sarashina-Bi-0.5B-PT)
+- **アーキテクチャ:** Llama
+- **最大シーケンス長:** 8,192トークン
+- **埋め込み次元:** 1280（Mean Pooling）
+- **トークナイザ:** SentencePiece / 語彙数 102,400
+- **位置エンコーディング:** RoPE
+- **対応言語:** 日本語
+- **類似度指標:** cosine
+
+
+## Model Series
+
+弱教師あり学習済みモデルに対して [cl-nagoya/ruri-v3-dataset-ft](https://huggingface.co/datasets/cl-nagoya/ruri-v3-dataset-ft) で教師あり学習したモデル群です。
+
+| ID                                                                                                          | Architecture | #Param. | #Param.<br>w/o Emb. | JMTEB-Avg | JMTEB-Retrieval | JMTEB-STS | JMTEB-Classification | JMTEB-Reranking | JMTEB-Clustering |
+|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|
+| [iamtatsuki05/Sentence-ModernBERT-JP-0.5B](https://huggingface.co/iamtatsuki05/Sentence-ModernBERT-JP-0.5B) | ModernBERT   | 679M    | 548M                | 65.31     | 57.95           | 80.78     | 71.73                | 75.50           | **50.03**        |
+| [iamtatsuki05/Sentence-Llama-Bi-JP-0.5B](https://huggingface.co/iamtatsuki05/Sentence-Llama-Bi-JP-0.5B)     | Llama        | 661M    | 530M                | 61.02     | 51.55           | 78.01     | 68.51                | 71.96           | 48.69            |
+| [iamtatsuki05/Sentence-Sarashina-Bi-0.5B](https://huggingface.co/iamtatsuki05/Sentence-Sarashina-Bi-0.5B)<br>(this model)   | Llama        | 661M    | 530M                | **66.84** | **59.00**       | **83.50** | **74.35**            | **77.36**       | 49.40            |
+
+
+## Licence
+このモデルは [MIT](https://licenses.opensource.jp/MIT/MIT.html) でライセンスされています。
+
+
+## How to Cite
+
+```tex
+@article{MIREI
+  title={同一条件下における Encoder/Decoderアーキテクチャの文埋め込み性能分析},
+  author={岡田 龍樹 and 杉本 徹},
+  journal={言語処理学会第 32 回年次大会 (NLP2026)},
+  year={2026}
+}
+```

config.json

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+{
+  "architectures": [
+    "LlamaBiModel"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoModel": "iamtatsuki05/sarashina2.2-Bi-0.5b--modeling_bidirectional_llama.LlamaBiModel",
+    "AutoModelForCausalLM": "iamtatsuki05/sarashina2.2-Bi-0.5b--modeling_bidirectional_llama.LlamaBiForMNTP",
+    "AutoModelForSequenceClassification": "iamtatsuki05/sarashina2.2-Bi-0.5b--modeling_bidirectional_llama.LlamaBiForSequenceClassification"
+  },
+  "bos_token_id": 1,
+  "dtype": "float32",
+  "eos_token_id": 2,
+  "head_dim": 80,
+  "hidden_act": "silu",
+  "hidden_size": 1280,
+  "initializer_range": 0.02,
+  "intermediate_size": 4480,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 500000,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.56.1",
+  "use_cache": true,
+  "vocab_size": 102400
+}

config_sentence_transformers.json

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+  "model_type": "SentenceTransformer",
+  "__version__": {
+    "sentence_transformers": "5.1.0",
+    "transformers": "4.56.1",
+    "pytorch": "2.8.0+cu128"
+  },
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

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modules.json

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sentence_bert_config.json

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+    "do_lower_case": false
+}

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