Welcome to the world of Textual Entailment! This blog will guide you through using a powerful model known as ParsBERT for handling logical entailment problems in Persian. With this guide, you’ll learn how to set up and run your entailment predictions in a straightforward manner.

Getting Started with ParsBERT

The ParsBERT model is designed specifically for textual entailment tasks. It can determine relationships such as entailment, contradiction, and neutrality between pairs of texts. Follow the steps below to set it up:

Step 1: Installation

• Ensure you have Python and the required libraries installed. You can install the required libraries using pip:

pip install torch transformers numpy

Step 2: Import Required Libraries

Start your Python script by importing necessary libraries:

import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import numpy as np

Step 3: Load the Model and Tokenizer

Set the model path and initialize the model and tokenizer:

model_name_or_path = "persiannlp/parsbert-base-parsinlu-entailment"
labels = ["entails", "contradicts", "neutral"]
model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path)
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)

Step 4: Create the Prediction Function

Define a function to process text pairs and make predictions:

def model_predict(text_a, text_b):
    features = tokenizer([(text_a, text_b)], padding=True, truncation=True, return_tensors="pt")
    output = model(**features)
    logits = output[0]
    probs = torch.nn.functional.softmax(logits, dim=1).tolist()
    idx = np.argmax(np.array(probs))
    print(labels[idx], probs)

Step 5: Make Predictions

Finally, call the function with your pairs of sentences:

model_predict("این مسابقات بین آوریل و دسامبر در هیپودروم ولیفندی در نزدیکی باکرکی ، ۱۵ کیلومتری (۹ مایل) غرب استانبول برگزار می شود.", "در ولیفندی هیپودروم، مسابقاتی از آوریل تا دسامبر وجود دارد.")
model_predict("آیا کودکانی وجود دارند که نیاز به سرگرمی دارند؟", "هیچ کودکی هرگز نمی خواهد سرگرم شود.")
model_predict("ما به سفرهایی رفته ایم که در نهرهایی شنا کرده ایم.", "علاوه بر استحمام در نهرها ، ما به اسپا ها و سونا ها نیز رفته ایم.")

Understanding the Model Through Analogy

Think of the ParsBERT model as a highly-trained judge in a competition of statements. Just like a judge listens carefully to each competitor, the model analyzes the two sentences you provide. Depending on how closely related they are, the judge (or model) will either declare that one sentence clearly supports the other (entails), that they oppose each other (contradicts), or that they are reasonably neutral. This meticulous analysis ensures accurate results for complex entailment tasks.

Troubleshooting Tips

• Model Not Loading: Ensure that the internet connection is stable, as the model needs to be downloaded at first use.
• Unexpected Output: Verify that your input texts are formatted correctly. Ensure proper sentence structure and language compatibility.
• Package Import Errors: Double-check that all required libraries are installed and up to date. You can update packages using:

pip install --upgrade transformers torch numpy

• For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

Conclusion

With this guide, you should now be able to successfully utilize the ParsBERT model for textual entailment. This powerful tool will assist you in a range of applications, from enhancing natural language understanding systems to improving dialogue models. Dive into the world of AI and enrich your projects with logical entailment today!