---
title: BioBERT limits & performance
description: 512-token segments with 64-token overlap, 1,000-document batches and 50MB uploads. BioBERT is deprecated and chat cannot call it.
---

This page helps you plan around BioBERT's input and throughput constraints — the 512-token segment limit, batch and file size ceilings, and how task type, document length, and entity density drive processing time. Document length and batch size most affect how long a job takes.

> **Not available in chat**

> BioBERT is deprecated on the platform and not registered with the orchestrator, so chat cannot call it. A current LLM performs BioBERT's entity extraction and relevance ranking natively over text retrieved with [PubMed](/docs/pubmed); for curated identifiers an LLM must not invent (NCBI Gene IDs, MeSH terms), use PubTator3. Existing BioBERT job results remain viewable.

## Text limits

BioBERT uses a BERT-based architecture with specific input constraints:

- **Token limit**: 512 tokens per segment (WordPiece tokenization)
- **Character limit**: Approximately 2,000-3,000 characters per segment depending on vocabulary coverage
- **Long documents**: Automatically split with 64-token overlap to preserve context across boundaries
- **Batch size**: Up to 1,000 documents per job (abstracts or paragraphs)
- **File size**: 50MB maximum upload per file
- **Total job size**: 10,000 documents maximum across all input files

## Token counting

BioBERT uses WordPiece tokenization where:
- Common biomedical terms may be single tokens (e.g., "cancer" → 1 token)
- Rare terms get split (e.g., "pembrolizumab" → 4 tokens)
- Average PubMed abstract: ~200-300 tokens

## Throughput

Processing times depend on task complexity and hardware:

| Task | Single Document | Batch (100) | Large Batch (1000) |
|------|-----------------|-------------|-------------------|
| NER | ~0.5-1 second | ~30-60 seconds | ~5-10 minutes |
| RE | ~1-2 seconds | ~1-2 minutes | ~10-20 minutes |
| QA | ~1-3 seconds | ~2-3 minutes | ~15-30 minutes |

## Factors affecting speed

- **Document length**: Longer documents = more segments = longer processing
- **Entity density**: Text with many entities takes longer for NER
- **Relationship complexity**: Multiple entity pairs increase RE time
- **Queue depth**: Wait times vary with platform load

## GPU acceleration

BioBERT inference leverages GPU acceleration when available:

- **GPU inference**: 5-10× faster than CPU for large batches
- **Batch optimization**: Larger batches benefit more from GPU parallelism
- **Memory requirements**: ~4GB GPU memory for standard inference
- **CPU fallback**: Automatic fallback if GPU unavailable (slower but functional)

## Quotas and rate limits

- **API rate limit**: 60 requests/minute per user
- **Concurrent jobs**: Maximum 5 simultaneous BioBERT jobs
- **Daily quota**: Varies by workspace tier
- **Polling frequency**: Minimum 5-second intervals recommended

## Optimization tips

- **Use batch processing**: Submit multiple documents in one job rather than individual requests
- **Pre-filter text**: Remove irrelevant sections (references, acknowledgments) to reduce processing
- **Set appropriate confidence thresholds**: Higher thresholds (>0.8) reduce noise; lower thresholds (<0.6) increase recall
- **Choose relevant entity types**: Only request entity types you need to reduce output size
- **Segment long documents**: Pre-split very long documents at natural boundaries (paragraphs, sections)
- **Cache results**: Store processed documents to avoid redundant API calls

## Memory considerations

- **Input buffering**: Large files are streamed, not fully loaded
- **Output size**: Dense annotation can produce outputs larger than inputs
- **Download timeouts**: Large result sets may need chunked downloads