Create README.md

README.md

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+---
+language:
+- id
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+task_categories:
+- feature-extraction
+- sentence-similarity
+tags:
+- sentence-transformers
+pretty_name: STSB-indo-mt
+dataset_info:
+  features:
+  - name: sentence1
+    dtype: string
+  - name: sentence2
+    dtype: string
+  - name: score
+    dtype: float64
+  splits:
+  - name: train
+    num_bytes: 755098
+    num_examples: 5749
+  - name: validation
+    num_bytes: 216064
+    num_examples: 1500
+  - name: test
+    num_bytes: 169987
+    num_examples: 1379
+  download_size: 720899
+  dataset_size: 1141149
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: validation
+    path: data/validation-*
+  - split: test
+    path: data/test-*
+---
+
+
+
+# Dataset Card for STSB
+
+This dataset is machine translated using DeepL.
+
+The Semantic Textual Similarity Benchmark (Cer et al., 2017) is a collection of sentence pairs drawn from news headlines, video and image captions, and natural language inference data.
+Each pair is human-annotated with a similarity score from 1 to 5. However, for this variant, the similarity scores are normalized to between 0 and 1. 
+
+## Dataset Details
+
+* Columns: "sentence1", "sentence2", "score"
+* Column types: `str`, `str`, `float`
+* Examples:
+    ```python
+    {
+      'sentence1': 'A man is playing a large flute.',
+      'sentence2': 'A man is playing a flute.',
+      'score': 0.76,
+    }
+    ```
+* Collection strategy: Reading the sentences and score from STSB dataset and dividing the score by 5.
+* Deduplified: No