Update full Metadata Category Classifier v2 model card

README.md

@@ -3,40 +3,123 @@ library_name: transformers
 base_model: allenai/scibert_scivocab_uncased
 tags:
 - research-library
-- v2
+- repository-library
+- metadata-category-classifier
 - m2
+- t1_metadata
+- text-classification
+- v2
 ---
 
-# Metadata Category Classifier v2
+# Metadata Category Classifier
 
-Research Library model `M2` uploaded from `/data/repository_library`.
+Classifies paper metadata from title and abstract text into arXiv-style category labels.
 
-## Version
+## Model Details
 
-`v2`
+- Artifact type: full fine-tuned Transformers model
+- Base model: `allenai/scibert_scivocab_uncased`
+- Backbone type: encoder
+- Model ID: `M2`
+- Tier: `T1_metadata`
+- Role in stack: metadata and global science-graph component
+- Version: `v2`
 
-## Experiment
+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 ID: `M2`
-- Experiment: `m2_metadata_category_classifier_draft`
-- Base model: `allenai/scibert_scivocab_uncased`
-- Training objective: `cross_entropy`
+## Model Sources
+
+- Hugging Face repo: https://huggingface.co/PeytonT/metadata-category-classifier
+- 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/m2_metadata_category_classifier.json
+- Models directory: https://github.com/peytontolbert/research_library/tree/main/models
+
+## Intended Use
+
+- Primary use: Classifies paper title and abstract metadata into category labels for metadata indexing and filtering.
+- Downstream use: retrieval, ranking, routing, clustering, paper understanding, and global science-graph construction inside the broader Repository Library system.
+- 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 `arxiv_metadata`: arXiv metadata records containing titles, abstracts, authors, categories, and update metadata.
+
+The v2 hardening pass restricts the classifier target space to a denser category set:
+
+- `max_labels`: 32
+- `min_per_label`: 16
+- `max_per_label`: 128
+
+## Training Procedure
+
+- Sources: `arxiv_metadata`
+- Input fields: `title`, `abstract`
+- Target fields: `categories`
+- Filters: years `[2000, 2025]`
+- 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: 512
+- Max target tokens: 128
+- Fine-tune strategy: `full_finetune`
+- Number of labels: 32
+- Max steps: 1000
+
+## Compute
+
+- Hardware: 2x RTX_3090 (24 GB)
+- Distributed strategy: `ddp`
+- Estimated GPU hours in config: 0
 
 ## Evaluation
 
+Declared metrics: `accuracy`, `macro_f1`
+
+Tracked v2 eval metrics on the current held-out split:
+
 - `eval_loss`: `0.003253802889958024`
 - `eval_accuracy`: `1.0`
 - `eval_macro_f1`: `1.0`
 - `eval_balanced_accuracy`: `1.0`
 - `eval_label_count`: `31`
 
-## Notes
+Status: this card reflects the current tracked experiment configuration and packaged weights in the Repository Library model stack. The v2 scores are very high and should receive a leakage audit before being treated as a final benchmark.
+
+## Usage
+
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+repo_id = "PeytonT/metadata-category-classifier"
+
+tokenizer = AutoTokenizer.from_pretrained(repo_id)
+model = AutoModelForSequenceClassification.from_pretrained(repo_id)
+
+text = "TITLE: Example paper title
+ABSTRACT: This paper studies representation learning for scientific documents."
+inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
+outputs = model(**inputs)
+predicted_id = outputs.logits.argmax(dim=-1).item()
+label = model.config.id2label.get(predicted_id, str(predicted_id))
+print(label)
+```
+
+## Limitations
 
-Hardened metadata classifier v2 with known-label eval filtering and a denser 32-label target. Metrics are high and should receive a leakage audit before use as a final benchmark.
+- 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.
+- The v2 classifier uses a hardened 32-label target space rather than the full arXiv category universe.
+- The current v2 eval result is perfect on the tracked split, which is useful as a pipeline check but should be audited for label leakage, split leakage, and class-distribution artifacts.
+- These models are components of a larger research system and should be validated in their target workflow before deployment.
 
-## Included Files
+## Project Context
 
-- Trained model/tokenizer artifacts
-- `experiment_config.json`
-- `trainer_state.json` when available
-- `research_library_v2_metadata.json`
+- GitHub repository: https://github.com/peytontolbert/research_library
+- Model collection: https://huggingface.co/collections/PeytonT/research-library-6a49c589ef4d763f7539b50d
+- Publisher: PeytonT