SciBERT (scivocab uncased)

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by Communityscibert-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.

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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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Scientific text to embed

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Output

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About SciBERT (scivocab uncased)

TL;DRAs of 23 September 2026

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

SciBERT is a BERT-base model trained on a large corpus of full-text scientific papers from Semantic Scholar. Unlike vanilla BERT it uses scivocab, a WordPiece vocabulary built directly from scientific text, which improves token coverage for domain terminology. This scivocab-uncased checkpoint is the most downloaded variant. Through the Hugging Face feature-extraction pipeline it returns token-level hidden states; mean-pool or take the [CLS] vector for sentence or document embeddings in scientific search and classification pipelines.

Pricing

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

API

Call SciBERT (scivocab uncased) with your Railwail API key. Use this model ID in the request:

scibert-scivocab-uncasedAPI documentation Get an API key

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Specifications

Model ID: scibert-scivocab-uncased
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
Scientific text 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 SciBERT (scivocab uncased)?

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

How much does SciBERT (scivocab uncased) cost on Railwail?

SciBERT (scivocab uncased) 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 SciBERT (scivocab uncased)?

The context window of SciBERT (scivocab uncased) holds 512 tokens.

How fast is SciBERT (scivocab uncased)?

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

Is SciBERT (scivocab uncased) better than OpenAI text-embedding-3-large?

That depends on the task. SciBERT (scivocab uncased) (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.

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