update readme.md

README.md

@@ -1,6 +1,5 @@
 ---
 license: apache-2.0
-extra_gated_prompt: "You agree to not use the dataset to conduct experiments that cause harm to human subjects, and to provide proper citation when using this dataset in your work."
 extra_gated_fields:
   Name: text
   Affilation: text
@@ -9,10 +8,51 @@ extra_gated_fields:
   Specific date: date_picker
   I want to use this dataset for:
     type: select
-    options: 
-      - Research
-      - Education
-      - label: Other
-        value: other
+    options:
+    - Research
+    - Education
+    - label: Other
+      value: other
   I agree to use this dataset for non-commercial use ONLY: checkbox
+task_categories:
+- sentence-similarity
+language:
+- ar
+tags:
+- STS
+- Embeddings
+- Arabic
+pretty_name: Arab3M-Triplets
+size_categories:
+- 1M<n<10M
 ---
+
+# Contrastive Learning Dataset
+
+This dataset is designed for training and evaluating models using contrastive learning techniques, particularly in the context of natural language understanding. The dataset consists of triplets: an anchor sentence, a positive sentence, and a negative sentence. The goal is to encourage models to learn meaningful representations by distinguishing between semantically similar and dissimilar sentences.
+
+## Dataset Overview
+
+- **Format**: Parquet
+- **Number of rows**: 3.03 million
+- **Columns**:
+  - `anchor`: A sentence serving as the reference point.
+  - `positive`: A sentence that is semantically similar to the `anchor`.
+  - `negative`: A sentence that is semantically dissimilar to the `anchor`.
+
+## Usage
+
+This dataset can be used to train models for various NLP tasks, including:
+
+- **Sentence Similarity**: Training models to identify sentences with similar meanings.
+- **Contrastive Learning**: Teaching models to differentiate between semantically related and unrelated sentences.
+- **Representation Learning**: Developing models that learn robust sentence embeddings.
+
+### Loading the Dataset
+
+You can load the dataset using the Hugging Face `datasets` library:
+
+```python
+from datasets import load_dataset
+dataset = load_dataset('Omartificial-Intelligence-Space/Arab3M-Triplets')
+```