SPECTER (Scientific Paper Embeddings)
AllenAI document-level embedding model for scientific papers. Built on SciBERT and trained on the citation graph so that papers citing each other land close together. Feed it a title plus abstract and it returns one 768-dim vector per paper, useful for recommendation, clustering and citation-based retrieval.
SPECTER (Scientific Paper Embeddings) is embeddings AI model from huggingface, priced at €0.000 per 1M input tokens with a 512 tokens context window.
About this model
Pricing
API Integration
Use our OpenAI-compatible API to integrate SPECTER (Scientific Paper Embeddings) into your application.
npm install railwailimport railwail from "railwail";
const rw = railwail("YOUR_API_KEY");
const vectors = await rw.run("specter-scientific-paper-embeddings", "Hello world", { type: "embed" });
console.log(vectors[0].length); // embedding dimensions
// Or use the embed() method for full control
const res = await rw.embed("specter-scientific-paper-embeddings", ["Hello", "World"]);
for (const item of res.data) {
console.log(item.embedding.length);
}Frequently asked questions
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