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RaduGabriel/
gene-entity-recognition

Name: gene-entity-recognition
Rating: 2.5 (15 reviews)

safetensors

bert

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Gene Extraction Model

This model is fine-tuned for gene extraction using BERT-CRF architecture.

Model Description

This model uses a custom BERT-CRF architecture for token classification, specifically designed for gene entity recognition. The model combines BERT with a Conditional Random Field (CRF) layer for improved sequence labeling.

Usage

python

from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline

model_name = "RaduGabriel/gene-entity-recognition"
hf_token = None
tokenizer = AutoTokenizer.from_pretrained(model_name, token=hf_token)
model = AutoModelForTokenClassification.from_pretrained(model_name, token=hf_token)

text = "TIF1gamma, a novel member of the transcriptional intermediary factor 1 family, plays a crucial role in gene regulation."

# Create NER pipeline
ner_pipeline = pipeline(
    "ner",
    model=model,
    tokenizer=tokenizer,
    aggregation_strategy="simple"
)


results = ner_pipeline(text)
print(results)

Labels

O
B-GENE
I-GENE

Model Details

Architecture: BERT-CRF
Base Model: dmis-lab/biobert-v1.1
Number of Labels: 3
CRF Layer: Enabled

Training Details

Training Data: GNormPlus dataset
Optimizer: AdamW
Learning Rate: 2e-05
Batch Size: 32
Epochs: 3