SciBERT (scivocab uncased)
AllenAI BERT-base pretrained from scratch on 1.14M scientific papers (mostly biomedical and computer science) with its own scientific WordPiece vocabulary. Used as a feature extractor it gives 768-dim contextual embeddings tuned to scientific text, outperforming general BERT on tasks like NER and relation extraction in research corpora.
SciBERT (scivocab uncased) 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 SciBERT (scivocab uncased) into your application.
npm install railwailimport railwail from "railwail";
const rw = railwail("YOUR_API_KEY");
const vectors = await rw.run("scibert-scivocab-uncased", "Hello world", { type: "embed" });
console.log(vectors[0].length); // embedding dimensions
// Or use the embed() method for full control
const res = await rw.embed("scibert-scivocab-uncased", ["Hello", "World"]);
for (const item of res.data) {
console.log(item.embedding.length);
}Frequently asked questions
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