BioBERT v1.2 (Biomedical Embeddings)
DMIS-Lab (Korea University) BERT-base initialized from English BERT and further pretrained on PubMed abstracts. Used as a feature extractor it yields 768-dim contextual embeddings tuned for biomedical text mining tasks such as NER, relation extraction and biomedical question answering.
BioBERT v1.2 (Biomedical Embeddings) is embeddings AI model from huggingface, priced at €0.000 per 1M input tokens with a 512 tokens context window.
About this model
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API Integration
Use our OpenAI-compatible API to integrate BioBERT v1.2 (Biomedical Embeddings) into your application.
npm install railwailimport railwail from "railwail";
const rw = railwail("YOUR_API_KEY");
const vectors = await rw.run("biobert-base-cased-v1-2", "Hello world", { type: "embed" });
console.log(vectors[0].length); // embedding dimensions
// Or use the embed() method for full control
const res = await rw.embed("biobert-base-cased-v1-2", ["Hello", "World"]);
for (const item of res.data) {
console.log(item.embedding.length);
}Frequently asked questions
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