| --- |
| language: |
| - en |
| license: mit |
| library_name: sentence-transformers |
| pipeline_tag: feature-extraction |
| base_model: BAAI/bge-small-en-v1.5 |
| model-index: |
| - name: Mirror Memory Default Embedding Model |
| results: |
| - task: |
| type: information-retrieval |
| name: Information Retrieval |
| dataset: |
| type: gooaq |
| name: GooAQ |
| metrics: |
| - type: recall_at_1 |
| value: 0.8237 |
| name: Recall@1 |
| - type: ndcg_at_10 |
| value: 0.9075 |
| name: NDCG@10 |
| - type: mrr_at_10 |
| value: 0.8837 |
| name: MRR@10 |
| datasets: |
| - sentence-transformers/gooaq |
| tags: |
| - sentence-transformers |
| - embeddings |
| - text-embeddings |
| - semantic-search |
| - information-retrieval |
| - rag |
| - retrieval |
| - bge |
| - bert |
| --- |
| |
| # Mirror Memory Default Embedding Model |
|
|
| A fine-tuned English text embedding model developed for **Mirror Memory**, a Rust-based retrieval-augmented generation (RAG) and long-term AI memory library. |
|
|
| This model is based on [`BAAI/bge-small-en-v1.5`](https://huggingface.co/BAAI/bge-small-en-v1.5) and was fine-tuned on a cleaned subset of the **GooAQ** question-answer dataset using `MultipleNegativesRankingLoss`. |
|
|
| The resulting model produces **384-dimensional dense embeddings** and is intended for semantic retrieval workloads such as vector search, RAG, document retrieval, and long-term AI memory. |
|
|
| --- |
|
|
| ## Model Details |
|
|
| | Property | Value | |
| |---|---| |
| | Model | Mirror Memory Default Embedding Model | |
| | Base model | `BAAI/bge-small-en-v1.5` | |
| | Architecture | BERT | |
| | Language | English | |
| | Embedding dimension | 384 | |
| | Maximum sequence length | 512 tokens | |
| | Pooling | CLS | |
| | Normalization | L2 normalization | |
| | Training objective | Multiple Negatives Ranking Loss | |
| | Training dataset | GooAQ | |
| | Training subset | ~30,000 cleaned samples | |
| | Weight format | SafeTensors | |
| | Intended tasks | Semantic search, retrieval, RAG, AI memory | |
|
|
| --- |
|
|
| ## Motivation |
|
|
| Mirror Memory uses vector embeddings to convert documents and text into representations that can be efficiently stored and retrieved from a vector database. |
|
|
| The default embedding model is designed to provide a compact embedding representation while remaining practical for local and resource-conscious inference. |
|
|
| It is intended to work particularly well for: |
|
|
| - Semantic search |
| - Retrieval-Augmented Generation (RAG) |
| - Long-term AI memory |
| - Document retrieval |
| - Question-answer retrieval |
| - Similarity search |
| - Knowledge-base retrieval |
| - Vector databases |
|
|
| --- |
|
|
| ## Training |
|
|
| ### Base Model |
|
|
| The model starts from: |
|
|
| **`BAAI/bge-small-en-v1.5`** |
|
|
| The base model is an English BERT-based sentence embedding model with a 384-dimensional output space. |
|
|
| ### Pooling and Normalization |
|
|
| The exported model package contains `1_Pooling/config.json` with: |
|
|
| ```json |
| { |
| "embedding_dimension": 384, |
| "pooling_mode": "cls", |
| "include_prompt": true |
| } |
| ``` |
|
|
| Therefore, the shipped model uses **CLS pooling**, not mean pooling. |
|
|
| The package also contains the `2_Normalize` Sentence Transformers module, so the final sentence embeddings are L2-normalized. |
|
|
| ### Dataset |
|
|
| Training used a cleaned subset of the **GooAQ** dataset. |
|
|
| - Dataset: [`sentence-transformers/gooaq`](https://huggingface.co/datasets/sentence-transformers/gooaq) |
| - Training subset: approximately 30,000 samples |
| - Duplicate entries were removed during preprocessing. |
| - The training data consists of question-answer pairs. |
|
|
| GooAQ is a large-scale question-answer dataset originally collected from Google search/autocomplete and answer boxes. See the original dataset card for details. |
|
|
| ### Objective |
|
|
| The model was fine-tuned using: |
|
|
| ```text |
| MultipleNegativesRankingLoss |
| ``` |
|
|
| This training objective encourages semantically related query/document pairs to have closer representations while using other examples in the batch as negative examples. |
|
|
| --- |
|
|
| ## Evaluation |
|
|
| The model was evaluated using retrieval-oriented metrics on the project's evaluation setup. The structured |
| `model-index` metadata above exposes these same results to the Hugging Face Hub. |
|
|
| | Metric | Result | |
| |---|---:| |
| | Recall@1 | 82.37% | |
| | NDCG@10 | 90.75% | |
| | MRR@10 | 88.37% | |
|
|
| These are **project-specific evaluation results**, not official MTEB benchmark results. |
|
|
| For reproducibility, benchmark details such as the exact evaluation split, preprocessing, and evaluation code should be considered alongside the Mirror Memory project source. |
|
|
| --- |
|
|
| ## Inference |
|
|
| ### Sentence Transformers |
|
|
| Install: |
|
|
| ```bash |
| pip install -U sentence-transformers |
| ``` |
|
|
| Load the model: |
|
|
| ```python |
| from sentence_transformers import SentenceTransformer |
| |
| model = SentenceTransformer( |
| "KeZZ08/mirror-memory-default-embedding-model" |
| ) |
| |
| texts = [ |
| "Rust provides memory safety and high performance.", |
| "Retrieval augmented generation combines search with language models.", |
| "Vector databases store embeddings for semantic retrieval.", |
| ] |
| |
| embeddings = model.encode( |
| texts, |
| normalize_embeddings=True |
| ) |
| |
| print(embeddings.shape) |
| # (3, 384) |
| ``` |
|
|
| The returned embeddings have **384 dimensions**. |
|
|
| --- |
|
|
| ## Single Text Embedding |
|
|
| ```python |
| from sentence_transformers import SentenceTransformer |
| |
| model = SentenceTransformer( |
| "KeZZ08/mirror-memory-default-embedding-model" |
| ) |
| |
| text = "Mirror Memory provides long-term retrieval for AI systems." |
| |
| embedding = model.encode( |
| text, |
| normalize_embeddings=True |
| ) |
| |
| print(embedding.shape) |
| # (384,) |
| ``` |
|
|
| --- |
|
|
| ## Batch Embedding |
|
|
| For retrieval systems, batch inference is recommended: |
|
|
| ```python |
| from sentence_transformers import SentenceTransformer |
| |
| model = SentenceTransformer( |
| "KeZZ08/mirror-memory-default-embedding-model" |
| ) |
| |
| documents = [ |
| "Rust is a systems programming language.", |
| "Qdrant is a vector database.", |
| "RAG combines retrieval with generation.", |
| "Embeddings represent text as numerical vectors.", |
| ] |
| |
| embeddings = model.encode( |
| documents, |
| batch_size=32, |
| normalize_embeddings=True, |
| show_progress_bar=True |
| ) |
| |
| print(embeddings.shape) |
| # (4, 384) |
| ``` |
|
|
| --- |
|
|
| ## Similarity Search Example |
|
|
| ```python |
| from sentence_transformers import SentenceTransformer |
| import numpy as np |
| |
| model = SentenceTransformer( |
| "KeZZ08/mirror-memory-default-embedding-model" |
| ) |
| |
| query = "How does vector search work?" |
| documents = [ |
| "Vector databases store numerical representations of data.", |
| "Python is commonly used for machine learning.", |
| "Rust provides memory safety without garbage collection.", |
| ] |
| |
| query_embedding = model.encode( |
| query, |
| normalize_embeddings=True |
| ) |
| |
| document_embeddings = model.encode( |
| documents, |
| normalize_embeddings=True |
| ) |
| |
| scores = document_embeddings @ query_embedding |
| |
| for document, score in sorted( |
| zip(documents, scores), |
| key=lambda x: x[1], |
| reverse=True |
| ): |
| print(f"{score:.4f} {document}") |
| ``` |
|
|
| Because the embeddings are normalized, the dot product can be used as cosine similarity. |
|
|
| --- |
|
|
| ## Mirror Memory |
|
|
| This model serves as the default embedding model for the **Mirror Memory** project. |
|
|
| Mirror Memory is a Rust library designed for high-performance: |
|
|
| - Document processing |
| - Text extraction |
| - Text cleaning |
| - Text chunking |
| - Embedding generation |
| - Vector storage |
| - Retrieval-Augmented Generation |
| - Long-term AI memory |
|
|
| The project supports multiple embedding backends, including custom Hugging Face/Candle models and FastEmbed models. |
|
|
| The model in this repository is the default embedding model intended for Mirror Memory's retrieval pipeline. |
|
|
| --- |
|
|
| ## Model Architecture |
|
|
| The model retains the BERT-based architecture of the BGE-small-en-v1.5 family. |
|
|
| Key characteristics: |
|
|
| ```text |
| Architecture: BERT |
| Hidden size: 384 |
| Transformer layers: 12 |
| Attention heads: 12 |
| Intermediate size: 1536 |
| Maximum sequence: 512 tokens |
| Embedding size: 384 |
| Pooling: CLS |
| Normalization: L2 |
| ``` |
|
|
| The exported repository contains the Sentence Transformers configuration required to load the model, including: |
|
|
| ```text |
| 1_Pooling/ |
| 2_Normalize/ |
| config.json |
| config_sentence_transformers.json |
| model.safetensors |
| modules.json |
| sentence_bert_config.json |
| tokenizer.json |
| tokenizer_config.json |
| ``` |
|
|
| --- |
|
|
| ## Intended Use |
|
|
| This model is intended for English-language embedding and retrieval applications, including: |
|
|
| - Semantic search |
| - Document similarity |
| - Question-answer retrieval |
| - RAG systems |
| - AI memory systems |
| - Knowledge-base search |
| - Vector database indexing |
| - Clustering and similarity analysis |
|
|
| --- |
|
|
| ## Limitations |
|
|
| This model should not be treated as a general-purpose generative language model. |
|
|
| It generates embeddings rather than natural-language responses. |
|
|
| Important limitations include: |
|
|
| - The model is optimized for English text. |
| - Performance can vary across domains that differ substantially from the training data. |
| - The reported evaluation results come from the project's own evaluation setup. |
| - The model has not been presented here as an official MTEB benchmark submission. |
| - As with other embedding models, retrieval quality depends on preprocessing, chunking, query formulation, vector similarity method, and downstream retrieval configuration. |
|
|
| --- |
|
|
| ## Files |
|
|
| The repository contains the exported Sentence Transformers model: |
|
|
| ```text |
| . |
| ├── 1_Pooling/ |
| │ └── config.json |
| ├── 2_Normalize/ |
| │ └── config.json |
| ├── config.json |
| ├── config_sentence_transformers.json |
| ├── model.safetensors |
| ├── modules.json |
| ├── README.md |
| ├── sentence_bert_config.json |
| ├── tokenizer.json |
| └── tokenizer_config.json |
| ``` |
|
|
| The model weights are provided in **SafeTensors** format. |
|
|
| No Python training checkpoint is required for inference. |
|
|
| --- |
|
|
| ## Relationship to BGE |
|
|
| This model is a fine-tuned version of: |
|
|
| [`BAAI/bge-small-en-v1.5`](https://huggingface.co/BAAI/bge-small-en-v1.5) |
|
|
| The original BGE model and its documentation are maintained by the Beijing Academy of Artificial Intelligence (BAAI). |
|
|
| This repository contains the fine-tuned model used by Mirror Memory and should not be confused with the original BGE release. |
|
|
| For the original model, architecture details, benchmarks, and upstream documentation, refer to the official BGE model card. |
|
|
| --- |
|
|
| ## License and Attribution |
|
|
| This repository is released under the **MIT License**. |
|
|
| The model is based on `BAAI/bge-small-en-v1.5`, which is also listed by its official Hugging Face repository under the MIT License. |
|
|
| The training data includes a cleaned subset of GooAQ. The original AllenAI GooAQ dataset is listed under the **Apache License 2.0**. Users should review the original dataset terms and attribution requirements when using this model or building derivative systems. |
|
|
| ### Upstream references |
|
|
| - BGE model: https://huggingface.co/BAAI/bge-small-en-v1.5 |
| - GooAQ dataset: https://huggingface.co/datasets/allenai/gooaq |
| - Sentence Transformers GooAQ: https://huggingface.co/datasets/sentence-transformers/gooaq |
|
|
| --- |
|
|
| ## Citation |
|
|
| If you use this model in your project, please reference this repository: |
|
|
| ```text |
| KeZZ08/mirror-memory-default-embedding-model |
| ``` |
|
|
| For work involving the original BGE model, please also refer to the official BGE publication and model repository. |
|
|
| --- |
|
|
| ## Disclaimer |
|
|
| This model is provided for research and software-development purposes. Users are responsible for evaluating the model for their own application, data, domain, and deployment requirements. |
|
|
