Convert dataset to Parquet

Convert dataset to Parquet.

README.md

@@ -34,10 +34,15 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 3617097
+    num_bytes: 3617085
     num_examples: 8364
-  download_size: 3503230
-  dataset_size: 3617097
+  download_size: 1887029
+  dataset_size: 3617085
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
 ---
 
 # Dataset Card for ArRestReviews

data/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:140cf35289bed629d676a9db236fa9955a1609794816090defd7350c8abc9e43
+size 1887029

dataset_infos.json

@@ -1 +1,49 @@
-{"default": {"description": "Dataset of 8364 restaurant reviews scrapped from qaym.com in Arabic for sentiment analysis\n", "citation": "@InProceedings{10.1007/978-3-319-18117-2_2,\nauthor=\"ElSahar, Hady\nand El-Beltagy, Samhaa R.\",\neditor=\"Gelbukh, Alexander\",\ntitle=\"Building Large Arabic Multi-domain Resources for Sentiment Analysis\",\nbooktitle=\"Computational Linguistics and Intelligent Text Processing\",\nyear=\"2015\",\npublisher=\"Springer International Publishing\",\naddress=\"Cham\",\npages=\"23--34\",\nisbn=\"978-3-319-18117-2\"\n}\n", "homepage": "https://github.com/hadyelsahar/large-arabic-sentiment-analysis-resouces", "license": "", "features": {"polarity": {"num_classes": 2, "names": ["negative", "positive"], "names_file": null, "id": null, "_type": "ClassLabel"}, "text": {"dtype": "string", "id": null, "_type": "Value"}, "restaurant_id": {"dtype": "string", "id": null, "_type": "Value"}, "user_id": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "ar_res_reviews", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 3617097, "num_examples": 8364, "dataset_name": "ar_res_reviews"}}, "download_checksums": {"https://raw.githubusercontent.com/hadyelsahar/large-arabic-sentiment-analysis-resouces/master/datasets/RES1.csv": {"num_bytes": 3503230, "checksum": "afdb587d41310302372ed154a91a7231f566c137cadeea9df166e7326c2c4b19"}}, "download_size": 3503230, "post_processing_size": null, "dataset_size": 3617097, "size_in_bytes": 7120327}}
+{
+    "default": {
+        "description": "Dataset of 8364 restaurant reviews scrapped from qaym.com in Arabic for sentiment analysis\n",
+        "citation": "@InProceedings{10.1007/978-3-319-18117-2_2,\nauthor=\"ElSahar, Hady\nand El-Beltagy, Samhaa R.\",\neditor=\"Gelbukh, Alexander\",\ntitle=\"Building Large Arabic Multi-domain Resources for Sentiment Analysis\",\nbooktitle=\"Computational Linguistics and Intelligent Text Processing\",\nyear=\"2015\",\npublisher=\"Springer International Publishing\",\naddress=\"Cham\",\npages=\"23--34\",\nisbn=\"978-3-319-18117-2\"\n}\n",
+        "homepage": "https://github.com/hadyelsahar/large-arabic-sentiment-analysis-resouces",
+        "license": "",
+        "features": {
+            "polarity": {
+                "names": [
+                    "negative",
+                    "positive"
+                ],
+                "_type": "ClassLabel"
+            },
+            "text": {
+                "dtype": "string",
+                "_type": "Value"
+            },
+            "restaurant_id": {
+                "dtype": "string",
+                "_type": "Value"
+            },
+            "user_id": {
+                "dtype": "string",
+                "_type": "Value"
+            }
+        },
+        "builder_name": "parquet",
+        "dataset_name": "ar_res_reviews",
+        "config_name": "default",
+        "version": {
+            "version_str": "0.0.0",
+            "major": 0,
+            "minor": 0,
+            "patch": 0
+        },
+        "splits": {
+            "train": {
+                "name": "train",
+                "num_bytes": 3617085,
+                "num_examples": 8364,
+                "dataset_name": null
+            }
+        },
+        "download_size": 1887029,
+        "dataset_size": 3617085,
+        "size_in_bytes": 5504114
+    }
+}