openbioner-base

Model card for OpenBioNER

We introduce OpenBioNER, a lightweight BERT-based model tailored for open-domain Biomedical NER. This model can find unseen target entity types based solely on their natural language descriptions, eliminating the need for retraining.

OpenBioNER is pretrained on synthetic silver annotations generated through LLM self-supervision. Extensive experiments demonstrate that OpenBioNER outperforms specialized LLMs, such as UniNER and GPT-4o, achieving an F1 score improvement of up to 10% in zero-shot settings across various biomedical benchmarks. In comparison to smaller baselines such as GLiNER, our model achieves better performance while using up to 4x fewer parameters.

Links

• Blog: link to blog
• Demo: link to demo
• Example usage in Colab: link to colab

Installation

To use this model, you must install the IBM Zshot library (from main branch before next release):

!pip install -U zshot==0.0.11 datasets gliner
!python -m spacy download en_core_web_sm

Usage

import spacy

from zshot import PipelineConfig, displacy
from zshot.linker import LinkerSMXM
from zshot.evaluation.metrics._seqeval._seqeval import Seqeval
from zshot.utils.data_models import Entity
from zshot.evaluation.zshot_evaluate import evaluate, prettify_evaluate_report

# define your list of candidate entity types
entities = [
     Entity(name='BACTERIUM', description='A bacterium refers to a type of microorganism that can exist as a single cell and may cause infections or play a role in various biological processes. Examples include species like Streptococcus pneumoniae and Streptomyces ahygroscopicus.', vocabulary=None),
]

nlp = spacy.blank("en")
nlp_config = PipelineConfig(
    linker=LinkerSMXM(model_name="disi-unibo-nlp/openbioner-base"),
    entities=entities,
    device='cuda' # or 'cpu' if GPU not available
)
nlp.add_pipe("zshot", config=nlp_config, last=True)


sentence = "Impact of cofactor - binding loop mutations on thermotolerance and activity of E. coli transketolase"
doc = nlp(sentence)

displacy.render(doc, style="ent")

Performance

OpenBioNER outperforms all competing models, achieving the highest average performance across all datasets.

⚠️ Disclaimer: Please note that running evaluations using the zshot library may lead to slightly different results on certain benchmarks compared to those reported in the paper (above). This discrepancy is due to differences in token alignment: zshot uses spaCy's character-based span matching, while our experiments use token-level alignment as handled by BERT-based NER pipelines. These differences can affect how entity spans are matched and evaluated, particularly in cases with subword tokenization or punctuation.

Descriptions

Below we provide all the descriptions used to evaluate OpenBioNER for each dataset.

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Negative Class

This is the description used as NEG class (e.g. not an entity) for all the datasets, execept for MedMentions-Rare:

Coal, water, oil, etc. are normally used for traditional electricity generation. However using liquefied natural gas as fuel for joint circulatory electricity generation has advantages. The chief financial officer is the only one there taking the fall. It has a very talented team, eh. What will happen to the wildlife? I just tell them, you've got to change. They're here to stay. They have no insurance on their cars. What else would you like? Whether holding an international cultural event or setting the city's cultural policies, she always asks for the participation or input of other cities and counties.

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NCBI

AnatEM

BC4CHEMD

BC2GM

BC5CDR

JNLPBA

JNLPBA-Rare

MedMentions-Rare

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🧬 How to Write Effective Entity Type Descriptions

Entity type descriptions are crucial for improving generalization in OpenBioNER. Well-written descriptions help models disambiguate types, handle rare classes, and align with real-world usage across diverse datasets.

✅ Best Practices

• Start with a clear definition: Briefly explain what the entity type is.

• Include functions or context: Add what it does, its purpose, or where it appears.

• List 3–5 concrete examples: Use domain-relevant examples (e.g., real diseases, proteins, or food items).

• Mention subtypes or synonyms (optional): Helps capture lexical variation and rare mentions.

• Keep it concise: 1–3 well-structured sentences are ideal.

⚠️ Common Mistakes to Avoid

• Vague or overly generic descriptions
• No examples
• Just a list of terms
• Redundant or circular wording

🧪 Template (Recommended Format)

A [TYPE] refers to [concise definition]. It includes examples such as [example1], [example2], and [example3].

Authors

• Alessio Cocchieri
• Giacomo Frisoni
• Marcos Martinez Galindo
• Gianluca Moro
• Giuseppe Tagliavini
• Francesco Candoli

📬 Contacts

For questions, collaborations, or feedback, feel free to reach out:

• Alessio: [email protected]
• Giacomo: [email protected]
• Marcos: [email protected]