Textual entailment is a fascinating area in natural language processing that examines whether a given statement follows from another. This article will guide you through using a model designed specifically for Persian (فارس) language entailment tasks, leveraging the capabilities of BERT.

Getting Started

To run this model, ensure you have Python installed, along with the necessary libraries such as PyTorch and the Hugging Face Transformers. Here’s a step-by-step guide to get you started:

Installation

First, make sure you have the required libraries installed. You can easily do this using pip:

pip install torch transformers

How to Run the Model

Below is the complete code you need to execute the model for textual entailment tasks:

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

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

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)

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 Code

Think of the code as a well-tuned musical ensemble. Each line plays an important note in harmony, creating a beautiful melody of functionalities:

• Import Statements: Just like musicians warming up, this part loads the necessary tools and libraries.
• Label Definition: Labels here are like the names of the musical pieces you’ll be performing – “entails,” “contradicts,” and “neutral.”
• Model and Tokenizer Initialization: This step is akin to tuning your instrument – it prepares the model for action.
• Prediction Function: The model_predict function serves as the conductor, coordinating inputs (text pairs) and informing you of the outcome (the label along with its probability).

Troubleshooting

Sometimes, you may encounter issues while running the model. Here are some tips to address common problems:

• Module Not Found: Ensure all necessary libraries are installed correctly.
• Incorrect Model Path: Verify that the model’s name and path are accurate.
• Tensor Compatibility Issues: Ensure that your input data is formatted correctly and adheres to the expected types.

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Conclusion

Textual entailment allows for advanced understanding and reasoning with language. With the steps outlined above, you can effectively deploy the Persian textual entailment model and enhance your natural language processing skills.

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.