Update README.md

README.md

@@ -35,4 +35,92 @@ configs:
     path: data/s3_multilingual_1-*
   - split: s4_multilingual_2
     path: data/s4_multilingual_2-*
+license: apache-2.0
+task_categories:
+- sentence-similarity
+language:
+- ar
+- en
+size_categories:
+- 10K<n<100K
 ---
+
+# Silma STS Arabic/English Dataset - v1.0
+
+## Overview
+
+The **Silma STS Arabic/English Dataset - v1.0** is a dataset designed for training and evaluating sentence embeddings for Arabic and English tasks. It consists of five different splits that cover monolingual and multilingual sentence pairs, with human-annotated similarity scores. The dataset includes both Arabic-to-Arabic and English-to-English pairs, as well as cross-lingual Arabic-English pairs, making it a valuable resource for multilingual and cross-lingual semantic similarity tasks.
+
+## Dataset Structure
+
+The dataset is divided into five splits, each containing sentence pairs and similarity scores.
+
+### Split 1: ar_ar
+- **Description:** Contains Arabic-to-Arabic sentence pairs with similarity scores.
+- **Size:** 11,512 examples
+- **JSON Sample:**
+  ```json
+  {
+    "sentence1": "رجلين يلعبان الشطرنج",
+    "sentence2": "ثلاثة رجال يلعبون الشطرنج",
+    "score": 0.52,
+    "langs": "ar-ar"
+  }
+
+### Split 2: en_en
+- **Description:** Contains English-to-English sentence pairs with similarity scores.
+- **Size:** 11,512 examples
+- **JSON Sample:**
+  ```json
+  {
+    "sentence1": "A plane is taking off.",
+    "sentence2": "An air plane is taking off.",
+    "score": 1.0
+  }
+
+### Split 3: multilingual_1
+- **Description:** Contains sentence pairs from both Arabic and English, with similarity scores. The sentences are aligned cross-lingually.
+- **Size:** 5,756 examples
+- **JSON Sample:**
+  ```json
+  {
+    "sentence1": "The man is playing the guitar. | الرجل يعزف على الغيتار",
+    "sentence2": "The man is playing the piano. | الرجل يعزف على البيانو",
+    "score": 0.32
+  }
+
+### Split 4: multilingual_2
+- **Description:** Similar to Split 3, but with reversed language pairs.
+- **Size:** 5,756 examples
+- **JSON Sample:**
+  ```json
+  {
+    "sentence1": "رجل يدخن | A man is smoking.",
+    "sentence2": "رجل يتزلج | A man is skating.",
+    "score": 0.1
+  }
+
+## Column Descriptions
+
+Each split in the dataset contains the following columns:
+
+- *sentence1:* The first sentence in the pair. It can be in Arabic or English depending on the split.
+- *sentence2:* The second sentence in the pair. It can also be in Arabic or English depending on the split.
+- *score:* A floating-point number between 0 and 1 representing the semantic similarity between the two sentences, where 1 indicates maximum similarity.
+- *langs:* Indicates the language pair of the sentences. The possible values are:
+  - ar-ar (Arabic-Arabic)
+  - en-en (English-English)
+  - Multilingual-1 (Multilingual, English-Arabic)
+  - Multilingual-2 (Multilingual, Arabic-English)
+
+## Use Cases
+
+The **Silma STS Arabic/English Dataset - v1.0** can be used in various NLP tasks, including but not limited to:
+
+1. **Sentence Embedding Training:** The dataset is well-suited for training models that generate sentence embeddings, enabling effective comparison of sentence-level semantics in both Arabic and English.
+2. **Multilingual and Cross-Lingual STS:** This dataset can be used for evaluating the performance of multilingual and cross-lingual sentence transformers, as it includes both monolingual and multilingual sentence pairs.
+3. **Semantic Similarity Tasks:** The dataset can be utilized in semantic similarity benchmarks, particularly for Arabic and English language pairs.
+4. **Cross-Lingual Transfer Learning:** The multilingual sentence pairs provide a good opportunity for training models in cross-lingual transfer learning, where knowledge from one language can be transferred to another.
+
+This dataset is a useful resource for researchers and developers working on NLP tasks that involve sentence semantics across different languages, especially for Arabic and English.
+