Armenian Text Embeddings 1
| Property | Value |
|---|---|
| Parameter Count | 278M |
| License | Apache 2.0 |
| Base Model | multilingual-e5-base |
| Embedding Dimension | 384 |
| Max Sequence Length | 128 tokens |
What is armenian-text-embeddings-1?
Armenian Text Embeddings 1 is a specialized transformer-based model designed to create high-quality text embeddings for Armenian language content. Built upon the multilingual-e5-base architecture, this model has been fine-tuned on a massive dataset of 1.08M English-Armenian translation pairs derived from Reddit content. The model employs advanced training techniques including weight averaging and DeepSpeed optimization to deliver robust performance for various NLP tasks.
Implementation Details
The model was trained using a sophisticated setup involving 4 NVIDIA A100 40GB GPUs over a 48-hour period. It implements DeepSpeed Stage 2 optimization with mixed-precision training and gradient checkpointing for efficient computation. The training process utilized a carefully balanced approach with a 0.6 weight for the base model and 0.4 for the fine-tuned components.
- Trained on 218M tokens of Armenian text
- Uses FP16 training with dynamic loss scaling
- Implements tensor parallelism with size 2
- Employs gradient clipping at 1.0
Core Capabilities
- Semantic search in Armenian language content
- Retrieval-augmented generation (RAG) applications
- Document similarity computation
- Cross-lingual text understanding
- Information retrieval and classification tasks
Frequently Asked Questions
Q: What makes this model unique?
This model is specifically optimized for Armenian language processing, trained on a large-scale dataset of Reddit content. It combines the power of multilingual-e5-base with specialized fine-tuning for Armenian language understanding, making it particularly effective for semantic search and RAG applications in Armenian.
Q: What are the recommended use cases?
The model excels in semantic similarity tasks, document classification, and clustering for Armenian text. It's particularly well-suited for building search systems, content recommendation engines, and cross-lingual applications involving Armenian language content. However, users should be aware of potential limitations with domain-specific terminology and cultural contexts.
