T-VEC: A Telecom-Specific Vectorization Model with Enhanced Semantic Understanding via Deep Triplet Loss Fine-Tuning

TL;DR

T-VEC addresses the challenge of telecom-domain language understanding by fine-tuning a 1.5B-parameter transformer end-to-end with a triplet loss on a large, curated telecom dataset (T-Embed). By constructing a telecom-focused tokenizer and releasing 75% of T-Embed under MIT, the work enables reproducible domain-specific embeddings that achieve state-of-the-art telecom retrieval and semantic understanding, as demonstrated on RFC- and vendor-manual–based benchmarks. The approach emphasizes deep, architecture-wide weight updates and hard-negative triplet mining to reshape the embedding space for telecom concepts. Real-world deployment in a production chatbot demonstrates practical benefits for enterprise telecom knowledge retrieval, with ongoing plans to expand data, improve generalization, and optimize integration. A noted limitation is reduced performance on general-domain tasks, highlighting the typical trade-off in domain-adaptive representations.

Abstract

The specialized vocabulary and nuanced concepts of the telecommunications industry pose persistent challenges for standard Natural Language Processing (NLP) models. Generic embedding models often struggle to represent telecom-specific semantics, limiting their utility in retrieval and downstream tasks. We present T-VEC (Telecom Vectorization Model), a domain-adapted embedding model fine-tuned from the gte-Qwen2-1.5B-instruct backbone using a triplet loss objective. Fine-tuning was performed on T-Embed, a high-quality, large-scale dataset covering diverse telecom concepts, standards, and operational scenarios. Although T-Embed contains some proprietary material and cannot be fully released, we open source 75% of the dataset to support continued research in domain-specific representation learning. On a custom benchmark comprising 1500 query-passage pairs from IETF RFCs and vendor manuals, T-VEC surpasses MPNet, BGE, Jina and E5, demonstrating superior domain grounding and semantic precision in telecom-specific retrieval. Embedding visualizations further showcase tight clustering of telecom-relevant concepts. We release T-VEC and its tokenizer to support semantically faithful NLP applications within the telecom domain.

Figures (5)

• Figure 1: From noisy telecom jargon to meaningful machine understanding. T-VEC learns telecom semantics by training on curated triplets: a domain-specific query (anchor), a true paraphrase (positive), and a deceptive distractor (negative). Through triplet loss fine-tuning, the model learns to pull related meanings closer while pushing apart unrelated ones, resulting in clear, telecom-aware clusters in embedding space.
• Figure 2: Embedding space analysis. Left: Cosine similarity distributions for positive (green) and negative (red) telecom pairs. T-VEC (right) demonstrates clearer separation than the base model. Right: t-SNE visualization of embeddings. T-VEC embeddings form tighter clusters with improved separation between anchor, positive, and negative samples.
• Figure 3: Estimated distribution of telecom-related queries across key annotated topic categories.
• Figure 4: Token count distributions across query, positive, and negative responses in the fine-tuning dataset.
• Figure 5: Visualization of Weight Adaptation. Left: Per-layer changes highlight systematic adaptation in MLP sub-components. Right: Distributional view emphasizes the extent and variance of fine-tuning across model weights.