Bert Base Cased Biomedical Ner

🚀 bert-base-cased-biological-ner

A fine - tuned BERT model for biomedical Named Entity Recognition (NER), enhancing information extraction in the biomedical domain.

🚀 Quick Start

from transformers import pipeline, AutoModelForTokenClassification, AutoTokenizer
import pandas as pd

tokenizer = AutoTokenizer.from_pretrained("Kushtrim/bert-base-cased-biomedical-ner")
model = AutoModelForTokenClassification.from_pretrained("Kushtrim/bert-base-cased-biomedical-ner")

ner = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy='first')

text = "Add your text here"

results = ner(text)

pd.DataFrame.from_records(results)

✨ Features

• Specifically designed for Named Entity Recognition (NER) in the biomedical domain.
• Fine - tuned on the SourceData Dataset, a comprehensive biomedical corpus.
• Can be used for various applications such as information retrieval, knowledge extraction, and data mining in the biomedical field.

📦 Installation

The README does not provide specific installation steps, so this section is skipped.

💻 Usage Examples

Basic Usage

from transformers import pipeline, AutoModelForTokenClassification, AutoTokenizer
import pandas as pd

tokenizer = AutoTokenizer.from_pretrained("Kushtrim/bert-base-cased-biomedical-ner")
model = AutoModelForTokenClassification.from_pretrained("Kushtrim/bert-base-cased-biomedical-ner")

ner = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy='first')

text = "Add your text here"

results = ner(text)

pd.DataFrame.from_records(results)

📚 Documentation

Model Details

Model Description

The bert-base-cased-biomedical-ner is a fine-tuned variant of the BERT (Bidirectional Encoder Representations from Transformers) model, designed specifically for the task of Named Entity Recognition (NER) in the biomedical domain. The model has been fine-tuned on the SourceData Dataset, which is a substantial and comprehensive biomedical corpus for machine learning and AI in the publishing context.

Named Entity Recognition is a crucial task in natural language processing, particularly in the biomedical field, where identifying and classifying entities like genes, proteins, diseases, and more is essential for various applications, including information retrieval, knowledge extraction, and data mining.

Intended Use

The bert-base-cased-biological-ner model is intended for NER tasks within the biomedical domain. It can be used for a range of applications, including but not limited to:

• Identifying and extracting biomedical entities (e.g., genes, proteins, diseases) from unstructured text.
• Enhancing information retrieval systems for scientific literature.
• Supporting knowledge extraction and data mining from biomedical literature.
• Facilitating the creation of structured biomedical databases.

Labels

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e - 05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e - 08
• lr_scheduler_type: linear
• num_epochs: 3

Framework versions

• Transformers 4.35.0
• Pytorch 2.1.0+cu118
• Datasets 2.14.6
• Tokenizers 0.14.1

🔧 Technical Details

The model is a fine - tuned version of BERT, which is a well - known Transformer - based architecture. Fine - tuning on the SourceData Dataset helps the model adapt to the specific requirements of biomedical NER tasks. The hyperparameters used in training are carefully selected to optimize the model's performance on this task.

📄 License

This model is licensed under the Apache 2.0 license.

⚠️ Important Note

This model has been superseded by newer versions fine - tuned on top of ModernBERT architectures.
Check out the improved models here:

• 🔬 [ModernBERT - base - biomedical - ner](https://huggingface.co/Kushtrim/ModernBERT - base - biomedical - ner)
• 🧬 [ModernBERT - large - biomedical - ner](https://huggingface.co/Kushtrim/ModernBERT - Large - biomedical - ner)

These newer models offer enhanced performance for biomedical NER tasks and are recommended for most use cases.