| --- |
| base_model: sentence-transformers/all-MiniLM-L6-v2 |
| library_name: transformers |
| pipeline_tag: text-classification |
| tags: |
| - cross-encoder-reranker |
| - l2 |
| - repository-library |
| - repository_library_search_stack |
| - research-library |
| - retrieval |
| --- |
| |
| # Cross Encoder Reranker |
|
|
| Reranks retrieved candidates with a cross-encoder scoring pass. |
|
|
| ## Model Details |
|
|
| - Artifact type: full fine-tuned model |
| - Base model: `sentence-transformers/all-MiniLM-L6-v2` |
| - Backbone type: `encoder` |
| - Model ID: `L2` |
| - Tier: `repository_library_search_stack` |
| - Role in stack: search-stack component for retrieval, reranking, or routing |
|
|
| 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/cross-encoder-reranker` |
| - 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/l2_cross_encoder_reranker.json` |
| - Models directory: `https://github.com/peytontolbert/research_library/tree/main/models` |
|
|
| ## Intended Use |
|
|
| - Primary use: Reranks retrieved candidates with a cross-encoder scoring pass. |
| - 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: `query, candidate_row` |
| - Target fields: `relevance_label` |
| - Train/val/test split: `[0.9, 0.1, 0.0]` |
| - Max samples: `4000` |
| - Batch size: `8` |
| - Precision: `bf16` |
| - Objective: `cross_entropy` |
| - Learning rate: `5e-05` |
| - Max source tokens: `256` |
| - Max target tokens: `256` |
| - Fine-tune strategy: `full_finetune` |
| - Max steps: `1000` |
| - Notes: Search-stack role added to match models.md coverage; dataset builder may need role-specific supervised labels before promotion. |
|
|
| ## Compute |
|
|
| - Hardware: not specified |
| - Distributed strategy: `unknown` |
| - Estimated GPU hours in config: `unknown` |
|
|
| ## Evaluation |
|
|
| - Declared metrics: `accuracy` |
| - Status: this card reflects the current tracked experiment configuration and packaged weights in the Repository Library model stack. |
|
|
| ## Usage |
|
|
| ```python |
| from transformers import AutoModelForSequenceClassification, AutoTokenizer |
| |
| repo_id = "PeytonT/cross-encoder-reranker" |
| |
| tokenizer = AutoTokenizer.from_pretrained(repo_id) |
| model = AutoModelForSequenceClassification.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` |
|
|
