**DeepSeek AI Tutorial**
Table of Contents
Introduction
DeepSeek AI is an open-source, cloud-based Natural Language Processing (NLP) platform that allows you to
build, deploy, and manage your own NLP models using Python. In this tutorial, we will guide you through
the process of setting up DeepSeek AI, creating a simple NLP model, and deploying it using Python.
Prerequisites
- Python 3.6+
- DeepSeek AI account (create one if you don’t have one already)
- Basic understanding of Python programming
Step 1: Create a New Project in DeepSeek AI
- Log in to your DeepSeek AI account.
- Click on the “Projects” tab and then click on “New Project”.
- Enter a name for your project and select “Python” as the programming language.
- Choose the NLP framework you want to use (e.g., NLTK, spaCy).
- Click on “Create Project”.
Step 2: Install Required Libraries
- Open a new terminal or command prompt.
- Activate your Python environment using `source /path/to/your/environment/bin/activate` (on Linux/Mac) or `activate` (on Windows).
- Install the required libraries using pip:
bash
pip install deepseek-ai
Step 3: Import DeepSeek AI Library
1. Create a new Python file (e.g., `nlp_example.py`) and add the following import statement:
python
from deepseek_aisdk import Client
2. Initialize the DeepSeek AI client using your project ID and API key:
python
client = Client(project_id="YOUR_PROJECT_ID", api_key="YOUR_API_KEY")
Replace `YOUR_PROJECT_ID` and `YOUR_API_KEY` with your actual project ID and API key.
Step 4: Load Data
1. Load your text data into a list or array:
python
text_data = ["This is an example sentence.", "Another sentence for demonstration purposes."]
2. Preprocess the text data using NLTK’s `word_tokenize` function:
python
from nltk.tokenize import word_tokenize
preprocessed_text = [word_tokenize(sentence) for sentence in text_data]
Step 5: Create a Simple NLP Model
1. Define a simple NLP model that takes preprocessed text input and outputs the sentiment (positive or
negative):
Pythondef nlp_model(text):
# Tokenize the input text
tokens = word_tokenize(text)
# Remove stop words
stop_words = set(["the", "and", "a"])
filtered_tokens = [token for token in tokens if token.lower() not in stop_words]
# Calculate sentiment using NLTK's VADER sentiment analysis tool
from nltk.sentiment.vader import SentimentIntensityAnalyzer
sia = SentimentIntensityAnalyzer()
sentiment_scores = sia.polarity_scores(" ".join(filtered_tokens))
return sentiment_scores["compound"]
2. Create an instance of the `nlp_model` function:
Python
model = nlp_model
Step 6: Deploy the Model
1. Use DeepSeek AI’s built-in deployment feature to deploy your NLP model to a cloud-based environment:
Python
from deepseek_aisdk import Deployment
deployment = Deployment(client, "YOUR_MODEL_NAME", model)
Replace `YOUR_MODEL_NAME` with the name you want to give to your deployed model.
2. Wait for the deployment to complete (this may take a few minutes):
Python
deployment.run()
Step 7: Test Your Model
1. Use DeepSeek AI’s built-in testing feature to test your deployed NLP model:
Python
from deepseek_aisdk import Testing
testing = Testing(client, "YOUR_MODEL_NAME")
results = testing.test_input("This is a positive sentence from Saurabh.")
print(results)
2. Replace `YOUR_MODEL_NAME` with the name of your deployed model.
Conclusion
In this tutorial, we demonstrated how to use DeepSeek AI’s Python SDK to build and deploy a simple NLP
model. We hope this tutorial has provided you with a solid foundation for building your own NLP models
using Python and DeepSeek AI. Happy modeling!
