--- license: apache-2.0 datasets: - emrecan/all-nli-tr language: - tr - en metrics: - spearmanr - accuracy - bertscore base_model: - nomic-ai/nomic-embed-text-v2-moe pipeline_tag: zero-shot-classification library_name: sentence-transformers --- # Model Card: Turkish Triplet Embedding Model (Nomic MoE) ## Model Description This is an embedding model trained on a Turkish triplet corpus, utilizing the dataset [`emrecan/all-nli-tr`](https://huggingface.co/datasets/emrecan/all-nli-tr). The model is based on **Nomic Mixture of Experts (MoE)** and achieves strong performance compared to other existing Turkish embedding models. ### **Intended Use** - Semantic similarity tasks - Text clustering - Information retrieval - Sentence and document-level embedding generation ### **Training Details** - **Architecture:** Nomic Mixture of Experts (MoE) - **Dataset:** `emrecan/all-nli-tr` - **Training Objective:** Triplet loss for contrastive learning ### **Evaluation & Performance** Compared to other Turkish embedding models, this model demonstrates strong performance in capturing semantic relationships within the language. Further evaluations and benchmarks will be shared as they become available. ### **How to Use** You can use this model with Hugging Face's `transformers` or `sentence-transformers` library: ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer("your-huggingface-model-name") embeddings = model.encode(["Merhaba dünya!", "Bugün hava çok güzel."]) print(embeddings) ``` ### **License & Citation** Please refer to the repository for licensing details and citation instructions.