Publish MiniLM L6 v2 EIDORA ONNX text embeddings

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README.md

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+---
+license: apache-2.0
+base_model: sentence-transformers/all-MiniLM-L6-v2
+pipeline_tag: feature-extraction
+tags:
+  - eidora
+  - eidora-model-zoo
+  - onnx
+  - onnxruntime
+  - embeddings
+  - text
+  - minilm
+---
+
+# MiniLM L6 v2 — Sentence Embeddings
+
+MiniLM converts text into normalized numerical embeddings. It is useful for comparing sentences, searching text collections and grouping related passages.
+
+## Source and provenance
+
+- Base model/checkpoint: `sentence-transformers/all-MiniLM-L6-v2`
+- Source: https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2
+- Original model: all-MiniLM-L6-v2
+- Architecture paper: https://aclanthology.org/D19-1410/
+- Training data: Sentence-pair training data described by the upstream Sentence Transformers model card.
+
+## EIDORA package
+
+- Package schema: `3` (Desktop preparation implementation `1`)
+- Declared providers: `CPUExecutionProvider`
+- Provider declarations require certification in validation-report.json before publication.
+- Preparation runs in EIDORA; exposed defaults are recommended, alternatives are not equivalent-quality claims.
+- Modality: `text`
+- Embedding dimensions: `384`
+- Normalized: `true`
+- Similarity: `cosine`
+
+## Preparation
+
+```yaml
+tokenizer_path: tokenizer
+max_length: 256
+padding_side: right
+truncation_side: right
+add_special_tokens: true
+prefix: ''
+suffix: ''
+parameters:
+  truncation_side:
+    default: right
+```
+
+## Intended use
+
+- Sentence similarity
+- Semantic text search
+- Text clustering
+
+## Limitations
+
+MiniLM generates sentence-level embeddings, not factual judgments. Text is truncated after 256 tokens. For longer documents, chunking is required. Similarity scores depend on the training distribution and should not be interpreted as semantic ground truth.
+
+## License and attribution
+
+Based on sentence-transformers/all-MiniLM-L6-v2. Original checkpoint and tokenizer are credited to the upstream Sentence Transformers project.
+
+## Citation
+
+```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/"
+}
+```
+
+
+## Validation and distribution
+
+This is an EIDORA ONNX conversion of the upstream model, not the original upstream checkpoint. The complete conversion and validation evidence is retained in the EIDORA development release package. This public distribution contains the files required for inference.

config.yaml

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+schema_version: 3
+id: eidora/text-minilm-l6-v2-light
+name: MiniLM L6 v2 — Sentence Embeddings
+version: 1.0.0
+modality: text
+license: apache-2.0
+bibtex: "@inproceedings{reimers-2019-sentence-bert,\n  title = \"Sentence-BERT: Sentence\
+  \ Embeddings using Siamese BERT-Networks\",\n  author = \"Reimers, Nils and Gurevych,\
+  \ Iryna\",\n  booktitle = \"Proceedings of EMNLP-IJCNLP\",\n  year = \"2019\",\n\
+  \  pages = \"3982--3992\",\n  url = \"https://aclanthology.org/D19-1410/\"\n}"
+compatibility:
+  min_eidora_version: 0.1.0
+artifacts:
+- path: model.onnx
+  sha256: 14ec04822edb8d74fdf375e0e815acc35a134746f266db2187e5b209a7e45304
+  role: graph
+- path: tokenizer/special_tokens_map.json
+  sha256: 303df45a03609e4ead04bc3dc1536d0ab19b5358db685b6f3da123d05ec200e3
+  role: tokenizer
+- path: tokenizer/tokenizer.json
+  sha256: be50c3628f2bf5bb5e3a7f17b1f74611b2561a3a27eeab05e5aa30f411572037
+  role: tokenizer
+- path: tokenizer/tokenizer_config.json
+  sha256: acb92769e8195aabd29b7b2137a9e6d6e25c476a4f15aa4355c233426c61576b
+  role: tokenizer
+- path: tokenizer/vocab.txt
+  sha256: 07eced375cec144d27c900241f3e339478dec958f92fddbc551f295c992038a3
+  role: tokenizer
+runtime:
+  graph: model.onnx
+  inputs:
+  - name: input_ids
+    source: token_ids
+    dtype: int64
+    shape:
+    - batch
+    - 256
+  - name: attention_mask
+    source: attention_mask
+    dtype: int64
+    shape:
+    - batch
+    - 256
+  output:
+    name: embedding
+    dtype: float32
+    shape:
+    - batch
+    - 384
+  execution_providers:
+  - CPUExecutionProvider
+preparation:
+  tokenizer_path: tokenizer
+  max_length: 256
+  padding_side: right
+  truncation_side: right
+  add_special_tokens: true
+  prefix: ''
+  suffix: ''
+  parameters:
+    truncation_side:
+      default: right
+embedding:
+  dimensions: 384
+  normalized: true
+  similarity: cosine
+metadata:
+  summary: Compact sentence-transformer model producing 384-dimensional embeddings
+    for text similarity.
+  trained_on: Sentence-pair training data described by the upstream Sentence Transformers
+    model card.
+  feature_type: embedding
+  pooling: attention-mask-aware mean
+  group:
+    id: text
+    label: Text Models
+    order: 20
+  provenance:
+    base_model: sentence-transformers/all-MiniLM-L6-v2
+    source_repository: https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2
+    original_name: all-MiniLM-L6-v2
+    original_model_url: https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2
+    paper_title: 'Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks'
+    paper_url: https://aclanthology.org/D19-1410/
+    training_data: Sentence-pair training data described by the upstream Sentence
+      Transformers model card.
+    upstream_license: Apache-2.0
+    authors:
+    - Sentence Transformers contributors
+    source_model: sentence-transformers/all-MiniLM-L6-v2
+    source_revision: 1110a243fdf4706b3f48f1d95db1a4f5529b4d41
+    exported_opset: 17
+    export_date: '2026-10-26'
+    exporter_version: eidora-onnx 1.0.0

model.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:14ec04822edb8d74fdf375e0e815acc35a134746f266db2187e5b209a7e45304
+size 90385603

tokenizer/special_tokens_map.json

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+{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}

tokenizer/tokenizer_config.json

@@ -0,0 +1 @@
+{"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "name_or_path": "nreimers/MiniLM-L6-H384-uncased", "do_basic_tokenize": true, "never_split": null, "tokenizer_class": "BertTokenizer", "model_max_length": 512}