| --- |
| base_model: sentence-transformers/all-MiniLM-L6-v2 |
| library_name: transformers |
| pipeline_tag: feature-extraction |
| tags: |
| - code |
| - embeddings |
| - file-embedding |
| - r2 |
| - repositories |
| - repository-library |
| - research-library |
| - t4_repo |
| --- |
| |
| # File Embedding |
|
|
| Produces file-level dense representations for retrieval and matching. |
|
|
| ## Model Details |
|
|
| - Artifact type: full fine-tuned model |
| - Base model: `sentence-transformers/all-MiniLM-L6-v2` |
| - Backbone type: `encoder` |
| - Model ID: `R2` |
| - Tier: `T4_repo` |
| - Role in stack: specialized Repository Library component |
|
|
| This model is part of the Repository Library stack, a research system for indexing, retrieving, aligning, and reasoning over scientific papers, structured paper content, repositories, and cross-domain links between them. |
|
|
| ## Model Sources |
|
|
| - Hugging Face repo: `https://huggingface.co/PeytonT/file-embedding` |
| - Hugging Face collection: `https://huggingface.co/collections/PeytonT/research-library-6a49c589ef4d763f7539b50d` |
| - GitHub repository: `https://github.com/peytontolbert/research_library` |
| - Experiment config: `https://github.com/peytontolbert/research_library/blob/main/models/experiments/r2_file_embedding.json` |
| - Models directory: `https://github.com/peytontolbert/research_library/tree/main/models` |
|
|
| ## Intended Use |
|
|
| - Primary use: Produces file-level dense representations for retrieval and matching. |
| - Downstream use: retrieval, ranking, planning, paper understanding, or cross-domain reasoning inside the broader Repository Library system, depending on the model family. |
| - Out of scope: production safety claims, benchmark claims beyond the tracked experiment config, or deployment without task-specific validation. |
|
|
| ## Training Data |
|
|
| The training inputs for this package were assembled from the following Repository Library data sources: |
|
|
| - Source `github_repos`: repository graph and code chunk data exported from the Repository Library repo pipeline. |
|
|
| ## Training Procedure |
|
|
| - Sources: `github_repos` |
| - Input fields: `file_query` |
| - Target fields: `source_chunk` |
| - Train/val/test split: `[0.9, 0.1, 0.0]` |
| - Max samples: `4000` |
| - Batch size: `8` |
| - Precision: `bf16` |
| - Objective: `contrastive` |
| - Learning rate: `5e-05` |
| - Max source tokens: `256` |
| - Max target tokens: `256` |
| - Fine-tune strategy: `full_finetune` |
| - Max steps: `1000` |
|
|
| ## Compute |
|
|
| - Hardware: 4x RTX_3090 (24 GB) |
| - Distributed strategy: `ddp` |
| - Estimated GPU hours in config: `0` |
| |
| ## Evaluation |
| |
| - Declared metrics: `recall_at_10, ndcg_at_10` |
| - Status: this card reflects the current tracked experiment configuration and packaged weights in the Repository Library model stack. |
| |
| ## Usage |
| |
| ```python |
| from transformers import AutoModel, AutoTokenizer |
| |
| repo_id = "PeytonT/file-embedding" |
|
|
| tokenizer = AutoTokenizer.from_pretrained(repo_id) |
| model = AutoModel.from_pretrained(repo_id) |
| ``` |
| |
| ## Limitations |
| |
| - These cards are generated from tracked experiment metadata and packaged artifacts, not from a separate benchmark report or external audit. |
| - Several training sources are pipeline outputs from the Repository Library codebase rather than standalone public datasets. |
| - These models are components of a larger research system and should be validated in their target workflow before deployment. |
| |
| ## Project Context |
| |
| - GitHub repository: `https://github.com/peytontolbert/research_library` |
| - Model collection: `https://huggingface.co/collections/PeytonT/research-library-6a49c589ef4d763f7539b50d` |
| - Publisher: `PeytonT` |
| |
