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README.md

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+---
+license: apache-2.0
+task_categories:
+- sentence-similarity
+language:
+- tr
+tags:
+- dataset
+- sentence-similarity
+- tr
+- turkish
+- ms-marco
+- contrastive-learning
+size_categories:
+- 1K<n<10K
+---
+
+# MS MARCO Turkish Triplets Dataset
+
+## Dataset Description
+
+Turkish version of the original MS MARCO dataset. This dataset contains query-passage triplets prepared specifically for contrastive learning tasks in Turkish language. This dataset is formatted from [parsak/msmarco-tr](https://huggingface.co/datasets/parsak/msmarco-tr) for triplet-based contrastive learning.
+
+## Dataset Source
+
+**Source**: This dataset is formatted from [parsak/msmarco-tr](https://huggingface.co/datasets/parsak/msmarco-tr), which is based on the original MS MARCO dataset.
+
+## Dataset Structure
+
+### Data Fields
+
+- **query_text**: Query text in Turkish
+- **pos_text**: Positive passage text in Turkish  
+- **neg_text**: Negative passage text in Turkish
+
+### Data Splits
+
+This dataset contains the following splits:
+- **train**: Training data
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("newmindai/ms-marco-turkish-triplets")
+
+# Access training data
+train_data = dataset['train']
+
+# Example usage
+for example in train_data:
+    query = example['query_text']
+    positive = example['pos_text']
+    negative = example['neg_text']
+    print(f"Query: {query}")
+    print(f"Positive: {positive}")
+    print(f"Negative: {negative}")
+    break
+```
+
+## Recommended Loss Functions
+
+This dataset is optimized for the following loss functions:
+
+- **MultipleNegativesRankingLoss**
+- **CachedMultipleNegativesRankingLoss**
+- **TripletLoss**
+
+### Loss Function Details
+
+#### MultipleNegativesRankingLoss (MNR)
+**Purpose**: Bring similar examples closer while pushing different examples apart. Used when you only have positive pairs (anchor, positive) and want to derive negatives from within the batch.
+
+**Mathematical Formula**:
+```
+L = - (1/N) * Σ log(exp(s(ai, pi) * scale) / Σ exp(s(ai, pj) * scale))
+```
+
+Where:
+- `s(ai, pj)`: similarity function (e.g., cosine similarity)
+- `scale`: temperature coefficient
+- `N`: batch size
+
+**Logic**: Maximizes the probability of the correct positive example for each anchor. Other positives in the batch act as negatives ("in-batch negatives"). Larger batches provide more negative examples, leading to better separation.
+
+#### CachedMultipleNegativesRankingLoss
+**Purpose**: Same mathematical principle as MNR but with higher memory efficiency.
+
+**Key Difference**: Used when large batches cannot fit directly into GPU memory. Pre-caches embeddings and then calculates losses, allowing for "virtually" larger in-batch negatives.
+
+**Formula**: Same as MNR, only the computation method differs (mini-batch caching).
+
+#### TripletLoss
+**Purpose**: Bring anchor-positive pairs closer while pushing anchor-negative pairs apart by a specific margin.
+
+**Mathematical Formula**:
+```
+L = max(0, d(a, p) - d(a, n) + m)
+```
+
+Where:
+- `d(·, ·)`: distance function (e.g., Euclidean, 1 - cosine)
+- `m`: margin (safety interval)
+
+**Logic**: If the negative is already far enough → loss = 0. If the negative is too close to the positive → loss > 0 (penalty).
+
+### Usage Example with Sentence Transformers
+
+```python
+from sentence_transformers import SentenceTransformer, losses, InputExample
+from datasets import load_dataset
+
+# Load dataset
+dataset = load_dataset("newmindai/ms-marco-turkish-triplets")
+
+# Initialize model
+model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+
+# Prepare training examples
+train_examples = []
+for example in dataset['train']:
+    train_examples.append(InputExample(texts=[example['query_text'], example['pos_text']], label=1))
+    train_examples.append(InputExample(texts=[example['query_text'], example['neg_text']], label=0))
+
+# Initialize loss function
+train_loss = losses.MultipleNegativesRankingLoss(model)
+
+# Train the model
+model.fit(
+    train_objectives=[(train_examples, train_loss)],
+    epochs=1,
+    warmup_steps=100
+)
+```
+
+### Usage Example with TripletLoss
+
+```python
+from sentence_transformers import SentenceTransformer, losses, InputExample
+
+# Prepare triplet examples
+train_examples = []
+for example in dataset['train']:
+    train_examples.append(InputExample(
+        texts=[example['query_text'], example['pos_text'], example['neg_text']]
+    ))
+
+# Initialize triplet loss
+train_loss = losses.TripletLoss(model)
+
+# Train the model
+model.fit(
+    train_objectives=[(train_examples, train_loss)],
+    epochs=1,
+    warmup_steps=100
+)
+```
+
+## Dataset Statistics
+
+- **Language**: tr (Turkish)
+- **Task**: sentence-similarity
+- **Source**: [parsak/msmarco-tr](https://huggingface.co/datasets/parsak/msmarco-tr) (based on original MS MARCO dataset)
+- **Format**: Query-Passage Triplets
+- **Use Case**: Contrastive Learning, Sentence Embeddings
+
+## Performance Tips
+
+1. **Batch Size**: Use batch sizes between 16-32 for optimal performance
+2. **Learning Rate**: Start with 2e-5 and adjust based on validation performance
+3. **Epochs**: 1-3 epochs are usually sufficient for fine-tuning
+4. **Warmup**: Use 10% warmup steps for stable training
+
+## Citation
+
+```bibtex
+@dataset{ms-marco-turkish-triplets,
+  title={MS MARCO Turkish Triplets Dataset},
+  author={NewMind AI},
+  year={2024},
+  language={Turkish},
+  source={parsak/msmarco-tr (https://huggingface.co/datasets/parsak/msmarco-tr)},
+  url={https://huggingface.co/datasets/newmindai/ms-marco-turkish-triplets}
+}
+```
+
+## License
+
+This dataset is released under the Apache 2.0 License.
+
+## Contact
+
+For questions or issues regarding this dataset, please contact NewMind AI.
+
+## Related Datasets
+
+- [parsak/msmarco-tr](https://huggingface.co/datasets/parsak/msmarco-tr) - Source dataset used to create this formatted triplet version
+- [MS MARCO NLI Triplets](https://huggingface.co/datasets/newmindai/ms-marco-nli-triplets)
+- [MS MARCO Multiple Negatives](https://huggingface.co/datasets/newmindai/ms-marco-multiple-negatives)

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