SPECTER (Scientific Paper Embeddings)

EmbeddingsUnavailable

by Communityspecter-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.

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Status: Unavailable
Context: 512 tokens
Input → output: Text → Vector
Developer: Community
Updated: 23 September 2026

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Input & output

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Text

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Paper title and abstract (or any scientific text) to embed

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Output

The vector appears here.

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About SPECTER (Scientific Paper Embeddings)

TL;DRAs of 23 September 2026

SPECTER (Scientific Paper Embeddings) is a model by Community in the Embeddings category. SPECTER (Scientific Paper Embeddings) is currently not available on Railwail. The context window holds 512 tokens.

SPECTER from the Allen Institute for AI produces a single embedding per scientific document instead of per-token vectors. It was pretrained on a triplet objective over the Semantic Scholar citation graph, so the distance between two paper embeddings reflects topical and citation relatedness rather than just lexical overlap. The standard input is the paper title and abstract joined together. Served through Hugging Face with the feature-extraction pipeline; take the [CLS] token vector (768 dims) as the document embedding.

Pricing

Currently unavailable. There is no price for this model at the moment, so it cannot be run.

API

Call SPECTER (Scientific Paper Embeddings) with your Railwail API key. Use this model ID in the request:

specter-scientific-paper-embeddingsAPI documentation Get an API key

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The model has no verified price or is deactivated; API calls are refused.

Specifications

Model ID: specter-scientific-paper-embeddings
Developer: Community
Category: Embeddings
Input: Text
Output: Vector
Context window: 512 tokens
Catalog entry updated: 23 September 2026

Input parameters

Inputs and settings from the model's input schema. The example in the API section shows which of them the API accepts.

ParameterTypeDefaultAllowed values

inputsrequired
Paper title and abstract (or any scientific text) to embed
Type: Text
Default: –
Allowed values: up to 4,000 characters

Use cases

Related guides

Embedding Models for Search & Science

All models in Embeddings

Frequently asked questions

What is SPECTER (Scientific Paper Embeddings)?

SPECTER (Scientific Paper Embeddings) is a model by Community in the Embeddings category. It is listed on Railwail but cannot be run at the moment.

How much does SPECTER (Scientific Paper Embeddings) cost on Railwail?

SPECTER (Scientific Paper Embeddings) cannot be run on Railwail at the moment, so there is no current price. Available alternatives with prices are listed further down this page.

What is the context window of SPECTER (Scientific Paper Embeddings)?

The context window of SPECTER (Scientific Paper Embeddings) holds 512 tokens.

How fast is SPECTER (Scientific Paper Embeddings)?

There are not enough measured runs of SPECTER (Scientific Paper Embeddings) on Railwail yet to state a run time. It depends on the input, the settings and the load at the provider.

Is SPECTER (Scientific Paper Embeddings) better than OpenAI text-embedding-3-large?

That depends on the task. SPECTER (Scientific Paper Embeddings) (Community) and OpenAI text-embedding-3-large (OpenAI) are both models in the Embeddings category. The comparison page shows their prices and specifications side by side.

Compare SPECTER (Scientific Paper Embeddings) and OpenAI text-embedding-3-large

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