s8frbroy
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talk2ref_ref_key_cited_paper_encoder

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 ---
 library_name: transformers
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 ---
+language: en
+license: cc-by-4.0
+tags:
+- scientific-retrieval
+- dense-passage-retrieval
+- dual-encoder
+- citation-prediction
+- talk2ref
+- SBERT
 library_name: transformers
+pipeline_tag: feature-extraction
+base_model: sentence-transformers/all-MiniLM-L6-v2
+datasets:
+- s8frbroy/talk2ref
 ---
+# 📚 Talk2Ref Cited Paper Encoder
+This model encodes **scientific papers** (titles, abstracts, and publication years) into dense embeddings for **Reference Prediction from Talks (RPT)** within the [Talk2Ref](https://huggingface.co/datasets/s8frbroy/talk2ref) framework.
+It serves as the **key-side encoder** in a **dual-encoder (DPR-style)** retrieval setup, paired with the [Talk2Ref Query Talk Encoder](https://huggingface.co/s8frbroy/talk2ref_query_talk_encoder).
+---
+## 🧩 Model Overview
+| Property | Description |
+|-----------|-------------|
+| **Architecture** | Sentence-BERT (all-MiniLM-L6-v2 backbone) |
+| **Pooling** | Mean pooling |
+| **Max sequence length** | 512 tokens |
+| **Training data** | Talk2Ref dataset (≈ 43 k cited papers linked to 6 k talks) |
+| **Objective** | Contrastive binary (DPR-style) loss |
+| **Task** | Encode cited papers into a shared semantic space with talk transcripts |
+---
+## 🧠 Input Features
+| Feature | Description |
+|----------|-------------|
+| **Title** | Title of the cited paper |
+| **Abstract** | Abstract text content |
+| **Year** | Publication year |
+These inputs are short enough to fit within the model’s 512-token limit — no chunking required.
+---
+## 🧮 Training Setup
+The cited-paper encoder was trained jointly with the query-talk encoder under a **dual-encoder contrastive framework** inspired by Dense Passage Retrieval (Karpukhin et al., 2020).
+Each talk *Ti* and paper *Rj* is encoded into embeddings *fT(Ti)* and *fR(Rj)*.
+Their dot-product similarity *sij = fT(Ti)·fR(Rj)* is optimized using a sigmoid-based binary loss supporting multiple positives per query:
+\[
+L = - \sum_i [y_i \log \sigma(s_i) + (1 - y_i)\log(1 - \sigma(s_i))]
+\]
+Negatives are sampled in-batch from other talk–paper pairs.
+Before training, a **domain adaptation stage** aligned each talk with its own paper’s abstract to adapt to scientific and spoken-language data.
+---
+## 💡 Usage Example
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+model = AutoModel.from_pretrained("s8frbroy/talk2ref_ref_key_cited_paper_encoder")
+tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
+paper_text = "We propose a retrieval architecture for linking long spoken documents to their references..."
+inputs = tokenizer(paper_text, return_tensors="pt", truncation=True, padding=True, max_length=512)
+embeddings = model(**inputs).last_hidden_state.mean(dim=1)  # mean pooling