Update README.md

README.md

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-# This is an embedding model trained on triplet turkish corpus data. It performs well compared to other preexisting turkish embedding models. Thats all
 ---
 license: apache-2.0
 datasets:
-- emrecan/stsb-mt-turkish
+- 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
-tags:
-- embedding
----
+---
+# 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.