Upload repository_library model package

README.md

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+---
+base_model: allenai/scibert_scivocab_uncased
+library_name: transformers
+pipeline_tag: text-classification
+tags:
+- m2
+- metadata-category-classifier
+- repository-library
+- research-library
+- t1_metadata
+---
+
+# Metadata Category Classifier
+
+Predicts arXiv-style subject categories from metadata text.
+
+## Model Details
+
+- Artifact type: full fine-tuned model
+- Base model: `allenai/scibert_scivocab_uncased`
+- Model ID: `M2`
+- Tier: `T1_metadata`
+- Local mirror: `/arxiv/models/repository_library/metadata-category-classifier`
+- Source checkpoint: `models/checkpoints/M2`
+
+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.
+
+## Intended Use
+
+- Primary use: Predicts arXiv-style subject categories from metadata text.
+- 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.
+
+## Training Data
+
+This package was trained from the following declared datasets or corpus sources:
+
+- `source:arxiv_metadata`
+
+## Training Procedure
+
+- Sources: `arxiv_metadata`
+- Input fields: `title, abstract`
+- Target fields: `categories`
+- Max samples: `4000`
+- Precision: `bf16`
+- Objective: `cross_entropy`
+- Batch size: `8`
+- Learning rate: `5e-05`
+- Max source tokens: `512`
+- Max target tokens: `128`
+- Max steps: `1000`
+
+## Evaluation
+
+- Declared metrics: `accuracy, macro_f1`
+- Status: local experiment artifact mirrored for release; external benchmark reporting has not been standardized across the full model family yet.
+
+## 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)
+```
+
+## 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.
+
+## 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.

config.json

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+{
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "astro-ph",
+    "1": "astro-ph gr-qc",
+    "2": "astro-ph gr-qc hep-ph",
+    "3": "astro-ph gr-qc hep-ph hep-th",
+    "4": "astro-ph gr-qc hep-ph nucl-th",
+    "5": "astro-ph gr-qc hep-th",
+    "6": "astro-ph gr-qc hep-th nucl-th",
+    "7": "astro-ph gr-qc physics.space-ph",
+    "8": "astro-ph hep-ex",
+    "9": "astro-ph hep-ph",
+    "10": "astro-ph hep-ph hep-th",
+    "11": "astro-ph hep-ph nucl-th",
+    "12": "astro-ph hep-ph physics.atom-ph physics.space-ph",
+    "13": "astro-ph hep-th physics.atom-ph",
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+    "15": "astro-ph nlin.CD physics.geo-ph",
+    "16": "astro-ph nucl-th",
+    "17": "astro-ph physics.gen-ph",
+    "18": "astro-ph physics.geo-ph",
+    "19": "astro-ph physics.plasm-ph",
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