Intellicuria - Healthcare AI Company

Welcome to the Intellicuria model repository on Hugging Face! We are a healthcare AI company at the forefront of innovation, dedicated to transforming the healthcare industry through the power of artificial intelligence. Our cutting-edge language models aim to improve patient outcomes, enhance diagnostics, and streamline healthcare processes.

Table of Contents

1. Introduction
2. Model Overview
3. Installation
4. Usage
5. Model API
6. Contributing
7. License
8. Contact
9. Acknowledgments

Introduction

Intellicuria is a healthcare AI company that specializes in developing state-of-the-art artificial intelligence solutions for the healthcare sector. Our mission is to revolutionize the industry by leveraging AI's potential to enhance patient care, accelerate diagnostics, and optimize various healthcare processes. This Hugging Face repository serves as a central hub for our pre-trained language models and other NLP resources.

Model Overview

Our pre-trained language models are designed to handle various healthcare-related NLP tasks, including medical text classification, entity recognition, sentiment analysis, and more. These models have been fine-tuned on large-scale healthcare datasets to provide accurate and reliable predictions for medical applications.

Installation

You can install the Intellicuria language models from the Hugging Face model hub using transformers library:

pip install transformers

Usage

With the transformers library installed, you can easily use the Intellicuria language models in your Python code. Here's a simple example of loading and using one of our pre-trained models for medical text classification:

from transformers import AutoTokenizer, AutoModelForSequenceClassification

# Load the tokenizer and model for medical text classification
model_name = "intellicuria/medical-text-classification"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Prepare input text
text = "Patient presented with symptoms of fever and cough."

# Tokenize the input text and feed it to the model
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)

# Get predicted label and probability
predicted_class = outputs.logits.argmax().item()
class_probabilities = outputs.logits.softmax(dim=1).tolist()[0]

print(f"Predicted Class: {predicted_class}")
print(f"Class Probabilities: {class_probabilities}")

Please refer to the model card and documentation for each specific model to understand its input format and supported tasks.

Model API

Our models support the Hugging Face inference API, allowing you to easily integrate them into your applications or services. You can use Hugging Face's pipeline module to perform inference directly:

from transformers import pipeline

# Load the model pipeline for named entity recognition (NER)
ner_model = "intellicuria/ner-model"
ner = pipeline(task="ner", model=ner_model, tokenizer=ner_model)

# Perform named entity recognition on input text
text = "The patient was prescribed Aspirin for pain relief."
entities = ner(text)

print(entities)

Contributing

We welcome contributions from the open-source community! If you wish to contribute to our language models or provide feedback, please follow the guidelines outlined in CONTRIBUTING.md.

License

This project is licensed under the MIT License.

Contact

For inquiries and support, please contact us at:

• Email: contact@intellicuria.com
• Website: https://www.intellicuria.com
• Twitter: @intellicuria

Acknowledgments

We would like to express our gratitude to the open-source community for their valuable contributions and continuous support in making healthcare AI accessible to all.

Thank you for choosing Intellicuria! Together, we can revolutionize healthcare through the power of NLP and AI.