PubMedBERT Embeddings (NeuML)

EmbeddingsUnavailable

by Communitypubmedbert-base-embeddings

Sentence-transformers model fine-tuned from Microsoft PubMedBERT on PubMed title-abstract pairs by the NeuML team. Produces 768-dim sentence embeddings tuned for biomedical semantic search and similarity, and is the embedding backbone behind the paperai and txtai medical search tools.

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

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Text

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Biomedical sentence or passage to embed

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Output

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About PubMedBERT Embeddings (NeuML)

TL;DRAs of 23 September 2026

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

This model takes Microsoft's PubMedBERT, which was pretrained from scratch on PubMed abstracts and PMC full text, and fine-tunes it with a sentence-transformers objective on PubMed title-abstract pairs. The result maps biomedical sentences and short passages into a 768-dim space where cosine similarity reflects clinical and biomedical relatedness. It is widely used for retrieval over medical literature. Served via the Hugging Face feature-extraction pipeline; mean-pool the token outputs to obtain the sentence embedding.

Pricing

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

API

Call PubMedBERT Embeddings (NeuML) with your Railwail API key. Use this model ID in the request:

pubmedbert-base-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: pubmedbert-base-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
Biomedical sentence or passage to embed
Type: Text
Default: –
Allowed values: up to 4,000 characters

Use cases

Related guides

Embedding Models for Search & Science

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Frequently asked questions

What is PubMedBERT Embeddings (NeuML)?

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

How much does PubMedBERT Embeddings (NeuML) cost on Railwail?

PubMedBERT Embeddings (NeuML) 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 PubMedBERT Embeddings (NeuML)?

The context window of PubMedBERT Embeddings (NeuML) holds 512 tokens.

How fast is PubMedBERT Embeddings (NeuML)?

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

Is PubMedBERT Embeddings (NeuML) better than OpenAI text-embedding-3-large?

That depends on the task. PubMedBERT Embeddings (NeuML) (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 PubMedBERT Embeddings (NeuML) and OpenAI text-embedding-3-large

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