Initial upload: laal-embedding-v1 (Sentence-Transformers, instruction model)

.gitattributes

@@ -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
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

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
+- en
+license: apache-2.0
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- text-embeddings
+- retrieval
+- mteb
+- korean
+- multilingual
+- e5
+pipeline_tag: sentence-similarity
+
+---
+
+# laal-embedding-v1
+
+**laal-embedding-v1** is a Sentence-Transformers embedding model fine-tuned from
+**`intfloat/multilingual-e5-large-instruct`** for improved **retrieval-oriented semantic search**, with a focus on **Korean fire-safety and legal-domain text**.
+
+* **Base model:** `intfloat/multilingual-e5-large-instruct`
+* **Embedding dimension:** 1024
+* **Similarity function:** cosine
+* **Max tokens:** 512
+* **Architecture:** XLM-RoBERTa (24 layers)
+* **HF repo:** [https://huggingface.co/jjp97/laal-embedding-v1](https://huggingface.co/jjp97/laal-embedding-v1)
+
+> ⚠️ **Important**
+> This model uses **fixed instruction prefixes** defined in `config_sentence_transformers.json`.
+> **Always pass raw text to `encode()`**.
+> Do **NOT** manually prepend instruction strings.
+
+---
+
+## Prompting (Important)
+
+This model applies different fixed prefixes depending on the input type.
+
+### Query prefix
+
+```
+Instruct: Given a web search query, retrieve relevant passages that answer the query.
+Query:
+```
+
+### Passage prefix
+
+```
+title: none
+text:
+```
+
+These prefixes are **automatically applied** by Sentence-Transformers via
+`config_sentence_transformers.json`.
+
+### Correct usage ✅
+
+```python
+from sentence_transformers import SentenceTransformer
+
+model = SentenceTransformer("jjp97/laal-embedding-v1")
+
+q_emb = model.encode("화재 시 대피 방법")
+p_emb = model.encode("화재가 발생하면 즉시 119에 신고하고 안전한 경로로 대피해야 한다.")
+```
+
+### Incorrect usage ❌ (double-prefixing)
+
+```python
+# Do NOT do this
+q = "Instruct: Given a web search query, retrieve relevant passages...\nQuery: 화재 시 대피 방법"
+emb = model.encode(q)
+```
+
+---
+
+## Training
+
+### Objective
+
+* Contrastive learning (InfoNCE)
+* In-batch negatives
+* Temperature (`tau`): **0.05**
+* Regularization: **GOR (spread-out loss)**
+
+  * `gor_lambda = 0.001`
+  * `gor_max_samples = 64`
+
+### Data
+
+* Training examples: **43,983**
+* Format: (query, positive passage)
+* Hard negatives: **enabled**
+
+  * `max_hn_per_example_train = 2`
+
+> Training data consists of domain-specific Korean fire-safety and legal documents
+> (private / curated dataset).
+
+### Hyperparameters (summary)
+
+* Batch size: **512**
+* Epochs: **3**
+* Learning rate: **1e-5**
+* Warmup ratio: **0.1**
+* Approx. total steps: **255**
+
+---
+
+## Model Architecture
+
+This model follows the standard Sentence-Transformers pipeline:
+
+1. **Transformer**: XLM-RoBERTa (24 layers, hidden size 1024)
+2. **Pooling**: mean pooling
+3. **Normalization**: L2 normalization
+
+---
+
+## Intended Use
+
+* Retrieval and semantic search (RAG pipelines)
+* Domain-specific QA (fire safety, legal text)
+* Embedding-based similarity and clustering
+  (best performance in retrieval-style settings)
+
+---
+
+## Evaluation
+
+### Sanity check
+
+Query–passage cosine similarity shows reasonable separation between relevant and irrelevant passages.
+
+### MTEB
+
+This model is intended for evaluation on the **MTEB leaderboard**.
+When reporting results, please specify:
+
+* model name: `jjp97/laal-embedding-v1`
+* exact revision (commit hash)
+* benchmark suite used
+
+---
+
+## Limitations
+
+* Performance may degrade if instruction prefixes are manually added (double-prefixing).
+* Fine-tuned primarily for retrieval; performance on classification/STS tasks may vary.
+* Domain bias toward Korean fire-safety / legal text.
+
+---
+
+## License
+
+* Released under **Apache-2.0**, following the base model license.
+
+---
+
+## Acknowledgements
+
+* Base model: **Multilingual E5**
+  Liang Wang et al., *Multilingual E5 Text Embeddings*, arXiv:2402.05672
+* Sentence-Transformers library
+
+---
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@misc{laal_embedding_v1_2025,
+  title = {laal-embedding-v1},
+  author = {Park, Jeongjae},
+  year = {2025},
+  howpublished = {Hugging Face model card},
+}
+```

config.json

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+    "XLMRobertaModel"
+  ],
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+  "hidden_size": 1024,
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+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
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+  "num_hidden_layers": 24,
+  "output_past": true,
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+  "position_embedding_type": "absolute",
+  "transformers_version": "4.56.1",
+  "type_vocab_size": 1,
+  "use_cache": false,
+  "vocab_size": 250002
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "5.1.0",
+    "transformers": "4.56.1",
+    "pytorch": "2.8.0+cu128"
+  },
+  "model_type": "SentenceTransformer",
+  "prompts": {
+    "query": "Instruct: Given a web search query, retrieve relevant passages that answer the query.\nQuery: ",
+    "passage": "title: none\ntext: "
+  },
+  "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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special_tokens_map.json

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tokenizer_config.json

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training_summary.json

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+  "train_examples": 43983,
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+  "epochs": 3,
+  "learning_rate": 1e-05,
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+}