# `BioGPT` Documentation

## Table of Contents
1. [Introduction](#introduction)
2. [Overview](#overview)
3. [Installation](#installation)
4. [Usage](#usage)
   1. [BioGPT Class](#biogpt-class)
   2. [Examples](#examples)
5. [Additional Information](#additional-information)
6. [Conclusion](#conclusion)

---

## 1. Introduction <a name="introduction"></a>

The `BioGPT` module is a domain-specific generative language model designed for the biomedical domain. It is built upon the powerful Transformer architecture and pretrained on a large corpus of biomedical literature. This documentation provides an extensive guide on using the `BioGPT` module, explaining its purpose, parameters, and usage.

---

## 2. Overview <a name="overview"></a>

The `BioGPT` module addresses the need for a language model specialized in the biomedical domain. Unlike general-purpose language models, `BioGPT` excels in generating coherent and contextually relevant text specific to biomedical terms and concepts. It has been evaluated on various biomedical natural language processing tasks and has demonstrated superior performance.

Key features and parameters of the `BioGPT` module include:
- `model_name`: Name of the pretrained model.
- `max_length`: Maximum length of generated text.
- `num_return_sequences`: Number of sequences to return.
- `do_sample`: Whether to use sampling in generation.
- `min_length`: Minimum length of generated text.

The `BioGPT` module is equipped with features for generating text, extracting features, and more.

---

## 3. Installation <a name="installation"></a>

Before using the `BioGPT` module, ensure you have the required dependencies installed, including the Transformers library and Torch. You can install these dependencies using pip:

```bash
pip install transformers
pip install torch
```

---

## 4. Usage <a name="usage"></a>

In this section, we'll cover how to use the `BioGPT` module effectively. It consists of the `BioGPT` class and provides examples to demonstrate its usage.

### 4.1. `BioGPT` Class <a name="biogpt-class"></a>

The `BioGPT` class is the core component of the `BioGPT` module. It is used to create a `BioGPT` instance, which can generate text, extract features, and more.

#### Parameters:
- `model_name` (str): Name of the pretrained model.
- `max_length` (int): Maximum length of generated text.
- `num_return_sequences` (int): Number of sequences to return.
- `do_sample` (bool): Whether or not to use sampling in generation.
- `min_length` (int): Minimum length of generated text.

### 4.2. Examples <a name="examples"></a>

Let's explore how to use the `BioGPT` class with different scenarios and applications.

#### Example 1: Generating Biomedical Text

```python
from biogpt import BioGPT

# Initialize the BioGPT model
biogpt = BioGPT()

# Generate biomedical text
input_text = "The patient has a fever"
generated_text = biogpt(input_text)

print(generated_text)
```

#### Example 2: Extracting Features

```python
from biogpt import BioGPT

# Initialize the BioGPT model
biogpt = BioGPT()

# Extract features from a biomedical text
input_text = "The patient has a fever"
features = biogpt.get_features(input_text)

print(features)
```

#### Example 3: Using Beam Search Decoding

```python
from biogpt import BioGPT

# Initialize the BioGPT model
biogpt = BioGPT()

# Generate biomedical text using beam search decoding
input_text = "The patient has a fever"
generated_text = biogpt.beam_search_decoding(input_text)

print(generated_text)
```

### 4.3. Additional Features

The `BioGPT` class also provides additional features:

#### Set a New Pretrained Model
```python
biogpt.set_pretrained_model("new_pretrained_model")
```

#### Get the Model's Configuration
```python
config = biogpt.get_config()
print(config)
```

#### Save and Load the Model
```python
# Save the model and tokenizer to a directory
biogpt.save_model("saved_model")

# Load a model and tokenizer from a directory
biogpt.load_from_path("saved_model")
```

#### Print the Model's Architecture
```python
biogpt.print_model()
```

---

## 5. Additional Information <a name="additional-information"></a>

- **Biomedical Text Generation**: The `BioGPT` module is designed specifically for generating biomedical text, making it a valuable tool for various biomedical natural language processing tasks.
- **Feature Extraction**: It also provides the capability to extract features from biomedical text.
- **Beam Search Decoding**: Beam search decoding is available for generating text with improved quality.
- **Customization**: You can set a new pretrained model and save/load models for customization.

---

## 6. Conclusion <a name="conclusion"></a>

The `BioGPT` module is a powerful and specialized tool for generating and working with biomedical text. This documentation has provided a comprehensive guide on its usage, parameters, and examples, enabling you to effectively leverage it for various biomedical natural language processing tasks.

By using `BioGPT`, you can enhance your biomedical text generation and analysis tasks with contextually relevant and coherent text.

*Please check the official `BioGPT` repository and documentation for any updates beyond the knowledge cutoff date.*