---
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.