The conversion of knowledge from natural language (NL) into first-order logic (FOL) plays a crucial role in Knowledge Representation and Cognitive Science, particularly in the context of hybrid Neural-Symbolic models. This process not only facilitates logical knowledge modeling but also enhances the effectiveness of language models and symbolic reasoning systems. The binary nature of FOL—where statements are either true or false—ensures greater consistency and efficiency in these models. However, converting natural language sentences into logical expressions remains a significant challenge due to contextual dependencies, varying levels of complexity, and diverse linguistic constraints. Traditional rule-based or statistical approaches often fail to handle this task effectively. Recently, the emergence of large language models (LLMs) has led to breakthroughs in translation and summarization, yet they continue to face difficulties in this specific conversion task. In this paper, we introduce Text-JEPA (Joint Embedding Predictive Architecture for Text), a self-supervised learning approach trained on textual data. We successfully apply this method to the task of NL-to-FOL conversion. To assess the quality of the conversion process, we refine existing evaluation metrics and propose a comprehensive evaluation framework that incorporates syntactic well-formedness, predicate-level semantic equivalence, and logical equivalence. Our experimental results on benchmark datasets demonstrate that this approach opens a new direction for NL-to-FOL conversion, improving the accuracy of results while maintaining computational efficiency. Furthermore, our model outperformed all tested models, including models of the same size fine-tuned and the large-scale model Gemini 1.5 Flash, highlighting its effectiveness in this domain.