# NLI-TR for Supervised SimCSE

This dataset is a modified version of [NLI-TR](https://huggingface.co/datasets/nli_tr) dataset. Its intended use is to train Supervised [SimCSE](https://github.com/princeton-nlp/SimCSE) models for sentence-embeddings. Steps followed to produce this dataset are listed below:
1. Merge train split of snli_tr and multinli_tr subsets.
2. Find every premise that has an entailment hypothesis **and** a contradiction hypothesis.
3. Write found triplets into sent0 (premise), sent1 (entailment hypothesis), hard_neg (contradiction hypothesis) format.

See this [Colab Notebook](https://colab.research.google.com/drive/1Ysq1SpFOa7n1X79x2HxyWjfKzuR_gDQV?usp=sharing) for training and evaluation on Turkish sentences.
task_categories:text-classification task_ids:semantic-similarity-scoring task_ids:text-scoring size_categories:100K<n<1M source_datasets:nli_tr language:tr region:us
{"language": ["tr"], "size_categories": ["100K<n<1M"], "source_datasets": ["nli_tr"], "task_categories": ["text-classification"], "task_ids": ["semantic-similarity-scoring", "text-scoring"]}
TAGS
#task_categories-text-classification #task_ids-semantic-similarity-scoring #task_ids-text-scoring #size_categories-100K<n<1M #source_datasets-nli_tr #language-Turkish #region-us 

# NLI-TR for Supervised SimCSE

This dataset is a modified version of NLI-TR dataset. Its intended use is to train Supervised SimCSE models for sentence-embeddings. Steps followed to produce this dataset are listed below:
1. Merge train split of snli_tr and multinli_tr subsets.
2. Find every premise that has an entailment hypothesis and a contradiction hypothesis.
3. Write found triplets into sent0 (premise), sent1 (entailment hypothesis), hard_neg (contradiction hypothesis) format.

See this Colab Notebook for training and evaluation on Turkish sentences.
# NLI-TR for Supervised SimCSE

This dataset is a modified version of NLI-TR dataset. Its intended use is to train Supervised SimCSE models for sentence-embeddings. Steps followed to produce this dataset are listed below:
1. Merge train split of snli_tr and multinli_tr subsets.
2. Find every premise that has an entailment hypothesis and a contradiction hypothesis.
3. Write found triplets into sent0 (premise), sent1 (entailment hypothesis), hard_neg (contradiction hypothesis) format.

See this Colab Notebook for training and evaluation on Turkish sentences.
TAGS
#task_categories-text-classification #task_ids-semantic-similarity-scoring #task_ids-text-scoring #size_categories-100K<n<1M #source_datasets-nli_tr #language-Turkish #region-us 
 # NLI-TR for Supervised SimCSE

This dataset is a modified version of NLI-TR dataset. Its intended use is to train Supervised SimCSE models for sentence-embeddings. Steps followed to produce this dataset are listed below:
1. Merge train split of snli_tr and multinli_tr subsets.
2. Find every premise that has an entailment hypothesis and a contradiction hypothesis.
3. Write found triplets into sent0 (premise), sent1 (entailment hypothesis), hard_neg (contradiction hypothesis) format.

See this Colab Notebook for training and evaluation on Turkish sentences.
passage: TAGS
#task_categories-text-classification #task_ids-semantic-similarity-scoring #task_ids-text-scoring #size_categories-100K<n<1M #source_datasets-nli_tr #language-Turkish #region-us 
# NLI-TR for Supervised SimCSE

This dataset is a modified version of NLI-TR dataset. Its intended use is to train Supervised SimCSE models for sentence-embeddings. Steps followed to produce this dataset are listed below:
1. Merge train split of snli_tr and multinli_tr subsets.
2. Find every premise that has an entailment hypothesis and a contradiction hypothesis.
3. Write found triplets into sent0 (premise), sent1 (entailment hypothesis), hard_neg (contradiction hypothesis) format.

See this Colab Notebook for training and evaluation on Turkish sentences.