Upload M1 paper metadata embedding adapter

README.md

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+---
+base_model: allenai/scibert_scivocab_uncased
+library_name: peft
+pipeline_tag: feature-extraction
+tags:
+- peft
+- lora
+- scibert
+- sentence-transformers
+- feature-extraction
+- embeddings
+- retrieval
+- scientific-papers
+- arxiv
+- research-library
+datasets:
+- PeytonT/1m_papers_text
+---
+
+# M1 Paper Metadata Embedding
+
+M1 is a LoRA adapter for `allenai/scibert_scivocab_uncased` trained for scientific paper retrieval inside the Research Library project. It embeds paper queries and metadata cards so a user can search, rank, and navigate papers by title, abstract, category, and author metadata.
+
+This repository contains the PEFT adapter only. Load it on top of `allenai/scibert_scivocab_uncased`.
+
+## Intended Use
+
+- Encode paper search queries for retrieval.
+- Encode paper metadata records for nearest-neighbor search.
+- Rank candidate papers in a research-library interface.
+
+This is not a generative model and should not be used to synthesize paper text.
+
+## Training Data
+
+The adapter was trained from `PeytonT/1m_papers_text`, a 1M-paper full-text and metadata dataset. Training used the metadata fields available in the local Research Library pipeline:
+
+- `title`
+- `abstract`
+- `categories`
+- `authors`
+
+The training stream covered one clean full epoch over the 1M-paper corpus using positive and negative contrastive pairs.
+
+## Training Procedure
+
+- Base model: `allenai/scibert_scivocab_uncased`
+- Adapter: LoRA
+- Task type: `FEATURE_EXTRACTION`
+- LoRA rank: `8`
+- LoRA alpha: `32`
+- LoRA dropout: `0.05`
+- Target modules: `query`, `value`
+- Objective: contrastive metadata retrieval
+- Batch size: `512`
+- Max source tokens: `256`
+- Precision: `bf16`
+- Optimizer: `adamw`
+- Learning rate: `1e-4`
+- Warmup steps: `1000`
+- Training steps: `3907`
+- Epochs: `1.0`
+- Final train loss: `0.0250`
+- Hardware: single H100-class GPU
+
+## Usage
+
+```python
+import torch
+import torch.nn.functional as F
+from transformers import AutoModel, AutoTokenizer
+from peft import PeftModel
+
+repo_id = "PeytonT/m1-paper-metadata-embedding"
+base_id = "allenai/scibert_scivocab_uncased"
+
+tokenizer = AutoTokenizer.from_pretrained(repo_id)
+base = AutoModel.from_pretrained(base_id)
+model = PeftModel.from_pretrained(base, repo_id)
+model.eval()
+
+def embed(texts):
+    batch = tokenizer(
+        texts,
+        padding=True,
+        truncation=True,
+        max_length=256,
+        return_tensors="pt",
+    )
+    with torch.no_grad():
+        outputs = model(**batch)
+        mask = batch["attention_mask"].unsqueeze(-1)
+        pooled = (outputs.last_hidden_state * mask).sum(dim=1) / mask.sum(dim=1).clamp_min(1)
+        return F.normalize(pooled, dim=1)
+
+query = embed(["retrieval augmented generation for scientific literature"])
+docs = embed([
+    "Title: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks\nCategories: cs.CL",
+    "Title: Quantum error correction with superconducting qubits\nCategories: quant-ph",
+])
+
+scores = query @ docs.T
+print(scores)
+```
+
+## Limitations
+
+- The adapter is optimized for metadata retrieval, not full-text semantic chunk retrieval.
+- It depends on the SciBERT base model and PEFT adapter loading.
+- Training was contrastive and library-oriented; external benchmarks have not yet been run.
+- Metadata quality, missing abstracts, and noisy category labels can affect retrieval quality.
+
+## Project Context
+
+This model is part of the Research Library system for exploring repositories and scientific papers through search, metadata views, paper graphs, and 3D universe visualizations.
+
+## Framework Versions
+
+- PEFT `0.19.1`

adapter_config.json

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