Enhance model card with repository context and sources

README.md

@@ -19,43 +19,59 @@ Learns author and community representations from paper metadata and graph contex
 
 - Artifact type: full fine-tuned model
 - Base model: `sentence-transformers/all-MiniLM-L6-v2`
+- Backbone type: `encoder`
 - Model ID: `M5`
 - Tier: `T1_metadata`
-- Local mirror: `/arxiv/models/repository_library/author-embedding`
-- Source checkpoint: `models/checkpoints/M5`
+- Role in stack: metadata-layer component in the paper understanding stack
 
-This repository is part of the `repository_library` model stack and is mirrored from `/data/repository_library/models/checkpoints` for publication under the `PeytonT` namespace.
+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/author-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/m5_author_embedding.json`
+- Models directory: `https://github.com/peytontolbert/research_library/tree/main/models`
 
 ## Intended Use
 
 - Primary use: Learns author and community representations from paper metadata and graph context.
-- Secondary use: retrieval, ranking, planning, or scientific paper tooling inside the broader Repository Library system, depending on the model family.
-- Out of scope: production safety claims, benchmark claims beyond the bundled experiment config, or use outside the model's narrow training objective without task-specific validation.
+- 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
 
-This package was trained from the following declared datasets or corpus sources:
+The training inputs for this package were assembled from the following Repository Library data sources:
 
-- `source:arxiv_metadata`
+- Source `arxiv_metadata`: arXiv metadata records spanning titles, abstracts, authors, and category labels.
 
 ## Training Procedure
 
 - Sources: `arxiv_metadata`
 - Input fields: `author_id, graph_neighbors`
 - Target fields: `author_embedding`
+- Train/val/test split: `[0.9, 0.1, 0.0]`
 - Max samples: `4000`
+- Batch size: `8`
 - Precision: `bf16`
 - Objective: `contrastive`
-- Batch size: `8`
 - Learning rate: `5e-05`
 - Max source tokens: `256`
 - Max target tokens: `128`
+- 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: local experiment artifact mirrored for release; external benchmark reporting has not been standardized across the full model family yet.
+- Status: this card reflects the current tracked experiment configuration and packaged weights in the Repository Library model stack.
 
 ## Usage
 
@@ -70,14 +86,12 @@ model = AutoModel.from_pretrained(repo_id)
 
 ## Limitations
 
-- These model cards reflect the packaged experiment configs and mirrored checkpoint contents, not an independently audited benchmark sheet.
-- Some training datasets are local corpora or exported shards, so reproducibility may require access to the surrounding Repository Library data pipeline.
-- Models in this stack are narrow components of a larger paper-and-repository system and should be validated on downstream tasks before deployment.
+- 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
 
-Repository Library is a research system for indexing, retrieving, aligning, and reasoning over scientific papers, structured paper content, repositories, and cross-domain links between them.
-
-## Contact
-
-Published under `PeytonT` from the local `repository_library` build.
+- GitHub repository: `https://github.com/peytontolbert/research_library`
+- Model collection: `https://huggingface.co/collections/PeytonT/research-library-6a49c589ef4d763f7539b50d`
+- Publisher: `PeytonT`