| --- |
| pipeline_tag: feature-extraction |
| tags: |
| - eidora |
| - eidora-model-zoo |
| - onnx |
| - onnxruntime |
| - embeddings |
| - text |
| - modality:text |
| - minilm |
| license: apache-2.0 |
| base_model: sentence-transformers/all-MiniLM-L6-v2 |
| --- |
| |
| # MiniLM L6 v2 — Sentence Embeddings |
|
|
| MiniLM L6 v2 is a compact sentence-embedding model. EIDORA converts sentences into normalized 384-dimensional vectors for semantic comparison. |
|
|
| ## Training Data and Checkpoint Provenance |
|
|
| - **Architecture:** Six-layer MiniLM Transformer with sentence-level mean pooling. |
| - **Original checkpoint:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). |
| - **Pinned revision:** `1110a243fdf4706b3f48f1d95db1a4f5529b4d41`. |
| - **Training data:** Sentence-pair training data described by the upstream Sentence Transformers model card. |
| - **Training method:** Sentence-pair training described in the upstream Sentence Transformers model card. |
| - **Source:** all-MiniLM-L6-v2 (original author attribution retained). |
|
|
| ## Model Overview |
|
|
| - **Model:** MiniLM L6 v2 — Sentence Embeddings |
| - **EIDORA identifier:** `eidora/text-minilm-l6-v2-light` |
| - **Modality:** Text |
| - **Embedding dimensions:** 384 |
| - **Embedding normalization:** L2-normalized |
| - **Similarity measure:** Cosine similarity |
| - **Package schema:** Version 3 |
| - **Model version:** `1.0.0` |
|
|
| ## EIDORA Use and Supported Modalities |
|
|
| **Supported modality:** Text only. |
|
|
| Researchers can use this package to: |
|
|
| - Compare short sentences by semantic similarity. |
| - Build simple text retrieval or search workflows. |
| - Cluster or explore groups of related passages. |
|
|
| ## Input and Preprocessing |
|
|
| EIDORA reads UTF-8 text and uses its packaged tokenizer from `tokenizer/`. |
|
|
| 1. Add model-specific special tokens. |
| 2. Pad or truncate to **256 tokens**, using right-side padding and right-side truncation. |
| 3. Pass integer `input_ids` (sourced from `token_ids`) and `attention_mask` tensors to ONNX. |
|
|
| **Input tensor shapes:** `[batch, 256]` for each input, dtype `int64`. The tokenizer and its configuration are included in the repository. Text beyond the configured maximum is truncated; callers should chunk longer inputs when needed. |
|
|
| ## Output Embeddings |
|
|
| The ONNX output is `float32` with shape `['batch', 384]`. Each vector has **384 dimensions**, is **L2-normalized**, and is intended for **cosine similarity** comparisons. |
|
|
| EIDORA encodes text using the packaged tokenizer and performs attention-mask-aware mean pooling with L2 normalization. |
|
|
| ## Runtime and Compatibility |
|
|
| The package contains `model.onnx`, its tokenizer files and schema-v3 text input preparation. |
|
|
| It declares **`CPUExecutionProvider`**. Actual execution availability depends on the installed ONNX Runtime and EIDORA Desktop environment. `config.yaml` is authoritative for graph inputs, preprocessing, provider and embedding dimensions. |
|
|
| ## Limitations |
|
|
| - Long text is truncated at 256 tokens unless it is split into chunks. |
| - Similarity is not a measurement of factual correctness or truth. |
| - The original training distribution can differ from a specialized collection. |
|
|
| Results should be evaluated against the intended research data and questions. |
|
|
| ## Research Context and References |
|
|
| ### Main paper |
|
|
| **Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks** |
|
|
| Paper: https://aclanthology.org/D19-1410/ |
|
|
| The publication describes the upstream learning method and original model. The embeddings returned by EIDORA additionally depend on the specific preprocessing and output adaptation documented here. |
|
|
| ### Primary citation (BibTeX) |
|
|
| ```bibtex |
| @inproceedings{reimers-2019-sentence-bert, |
| title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
| author = "Reimers, Nils and Gurevych, Iryna", |
| booktitle = "Proceedings of EMNLP-IJCNLP", |
| year = "2019", |
| pages = "3982--3992", |
| url = "https://aclanthology.org/D19-1410/" |
| } |
| ``` |
|
|
| ## License and Attribution |
|
|
| The upstream sentence-transformers/all-MiniLM-L6-v2 checkpoint is distributed under Apache License 2.0. See the included `LICENSE`. EIDORA credits the Sentence Transformers contributors and provides the ONNX adaptation. |
|
|
| See the `LICENSE` file included in this repository. |
|
|
| ## Package Information |
|
|
| - **Model repository:** `EIDORA/MiniLM_L6_v2_Text` |
| - **Package version:** `1.0.0` |
| - **Package schema:** `3` |
| - **EIDORA configuration:** `config.yaml` |
| - **ONNX graph(s):** `model.onnx` |
| - **Supported modality:** Text |
| - **License identifier:** `apache-2.0` |
| - **Declared provider:** CPUExecutionProvider |
| - **Required supplementary files:** `tokenizer/` (packaged tokenizer), `LICENSE` |
|
|
| The configuration defines the authoritative model-facing runtime contract. Other packaged assets, where present, do not introduce additional inference modalities. |
|
|
