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--- |
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license: apache-2.0 |
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datasets: |
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- emrecan/all-nli-tr |
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language: |
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- tr |
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- en |
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metrics: |
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- spearmanr |
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- accuracy |
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- bertscore |
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base_model: |
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- nomic-ai/nomic-embed-text-v2-moe |
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pipeline_tag: zero-shot-classification |
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library_name: sentence-transformers |
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--- |
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# Model Card: Turkish Triplet Embedding Model (Nomic MoE) |
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## Model Description |
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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. |
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### **Intended Use** |
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- Semantic similarity tasks |
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- Text clustering |
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- Information retrieval |
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- Sentence and document-level embedding generation |
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### **Training Details** |
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- **Architecture:** Nomic Mixture of Experts (MoE) |
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- **Dataset:** `emrecan/all-nli-tr` |
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- **Training Objective:** Triplet loss for contrastive learning |
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### **Evaluation & Performance** |
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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. |
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### **How to Use** |
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You can use this model with Hugging Face's `transformers` or `sentence-transformers` library: |
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```python |
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from sentence_transformers import SentenceTransformer |
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model = SentenceTransformer("your-huggingface-model-name") |
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embeddings = model.encode(["Merhaba dünya!", "Bugün hava çok güzel."]) |
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print(embeddings) |
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``` |
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### **License & Citation** |
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Please refer to the repository for licensing details and citation instructions. |
