Update README with accrurate tests of the dataset

README.md

@@ -63,5 +63,6 @@ To load the dataset:
 ```python
 from datasets import load_dataset
 
-dataset = load_dataset("ipip_ffm", split="train")
+dataset = load_dataset("Tetratics/2018-11-08-OpenPsychometrics-IPIP-FFM")
+df = pd.DataFrame(dataset["train"])
 ```

data-final.csv

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:dfbd5253f3f21f0569b34f2d1f47fbb71f5324ed26c3debbe29e84d42ce6d563
-size 416273844

ipip_ffm.py

@@ -1,49 +0,0 @@
-from datasets import DatasetBuilder, DatasetInfo, SplitGenerator, Split, Value, Features
-import pandas as pd
-import os
-
-class IPIPFFMDataset(DatasetBuilder):
-    def _info(self):
-        return DatasetInfo(
-            description="IPIP-FFM dataset with personality traits and metadata.",
-            features=Features({
-                "EXT1": Value("int32"),
-                "EXT2": Value("int32"),
-                "EXT3": Value("int32"),
-                # Adicione todos os outros itens aqui
-                "dateload": Value("string"),
-                "screenw": Value("int32"),
-                "screenh": Value("int32"),
-                "introelapse": Value("int32"),
-                "testelapse": Value("int32"),
-                "endelapse": Value("int32"),
-                "IPC": Value("int32"),
-                "country": Value("string"),
-                "lat_appx_lots_of_err": Value("float32"),
-                "long_appx_lots_of_err": Value("float32"),
-            }),
-            homepage="https://openpsychometrics.org/_rawdata/",
-            citation="@misc{openpsychometrics, author = {OpenPsychometrics}, title = {IPIP-FFM Dataset}, year = {2018}, url = {https://openpsychometrics.org/_rawdata/}}",
-        )
-
-    def _split_generators(self, dl_manager):
-        return [
-            SplitGenerator(
-                name=Split.TRAIN,
-                gen_kwargs={"filepath": "data-final.csv"}
-            ),
-        ]
-
-    def _generate_examples(self, filepath):
-        df = pd.read_csv(filepath)
-        for i, row in df.iterrows():
-            yield i, row.to_dict()
-
-    def _prepare_split(self, split_generator, **kwargs):
-        # Preparar os dados para o split
-        filepath = split_generator.gen_kwargs["filepath"]
-        df = pd.read_csv(filepath)
-        # Aqui você pode adicionar qualquer pré-processamento necessário
-        # Por exemplo, dividir os dados em treino e validação
-        # Neste caso, estamos apenas retornando o dataframe como está
-        return df

prepare_parket_files.py

@@ -0,0 +1,38 @@
+import os
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from datasets import Dataset, DatasetDict
+import pyarrow as pa
+import pyarrow.parquet as pq
+
+# Define the directory to save Parquet files
+parquet_dir = "./dataset_parquet"
+
+# Create the directory if it doesn't exist
+os.makedirs(parquet_dir, exist_ok=True)
+
+# Load your CSV file into a pandas DataFrame
+df = pd.read_csv("data-final.csv", delimiter='\t')
+
+# Split the DataFrame into train, validation, and test sets
+train_df, temp_df = train_test_split(df, test_size=0.4, random_state=42)
+val_df, test_df = train_test_split(temp_df, test_size=0.5, random_state=42)
+
+# Convert the pandas DataFrames to Hugging Face Datasets
+train_dataset = Dataset.from_pandas(train_df)
+val_dataset = Dataset.from_pandas(val_df)
+test_dataset = Dataset.from_pandas(test_df)
+
+# Create a DatasetDict
+dataset_dict = DatasetDict({
+    "train": train_dataset,
+    "validation": val_dataset,
+    "test": test_dataset
+})
+
+# Convert each split to Parquet format and save
+for split_name, dataset in dataset_dict.items():
+    table = pa.Table.from_pandas(dataset.to_pandas())
+    pq.write_table(table, os.path.join(parquet_dir, f"{split_name}.parquet"))
+
+print("Dataset splits saved as Parquet files.")

tests.ipynb

@@ -0,0 +1,117 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install -U datasets huggingface_hub fsspec"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "OSWN0xQn6z8u",
+        "outputId": "bb05c540-e196-49c3-cf7a-d2f4942abe52"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: datasets in /usr/local/lib/python3.11/dist-packages (2.14.4)\n",
+            "Collecting datasets\n",
+            "  Downloading datasets-3.6.0-py3-none-any.whl.metadata (19 kB)\n",
+            "Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.11/dist-packages (0.32.4)\n",
+            "Collecting huggingface_hub\n",
+            "  Downloading huggingface_hub-0.33.0-py3-none-any.whl.metadata (14 kB)\n",
+            "Requirement already satisfied: fsspec in /usr/local/lib/python3.11/dist-packages (2025.3.2)\n",
+            "Collecting fsspec\n",
+            "  Downloading fsspec-2025.5.1-py3-none-any.whl.metadata (11 kB)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from datasets) (3.18.0)\n",
+            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.11/dist-packages (from datasets) (2.0.2)\n",
+            "Requirement already satisfied: pyarrow>=15.0.0 in /usr/local/lib/python3.11/dist-packages (from datasets) (18.1.0)\n",
+            "Requirement already satisfied: dill<0.3.9,>=0.3.0 in /usr/local/lib/python3.11/dist-packages (from datasets) (0.3.7)\n",
+            "Requirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (from datasets) (2.2.2)\n",
+            "Requirement already satisfied: requests>=2.32.2 in /usr/local/lib/python3.11/dist-packages (from datasets) (2.32.3)\n",
+            "Requirement already satisfied: tqdm>=4.66.3 in /usr/local/lib/python3.11/dist-packages (from datasets) (4.67.1)\n",
+            "Requirement already satisfied: xxhash in /usr/local/lib/python3.11/dist-packages (from datasets) (3.5.0)\n",
+            "Requirement already satisfied: multiprocess<0.70.17 in /usr/local/lib/python3.11/dist-packages (from datasets) (0.70.15)\n",
+            "  Downloading fsspec-2025.3.0-py3-none-any.whl.metadata (11 kB)\n",
+            "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from datasets) (24.2)\n",
+            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.11/dist-packages (from datasets) (6.0.2)\n",
+            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.11/dist-packages (from huggingface_hub) (4.14.0)\n",
+            "Requirement already satisfied: hf-xet<2.0.0,>=1.1.2 in /usr/local/lib/python3.11/dist-packages (from huggingface_hub) (1.1.2)\n",
+            "Requirement already satisfied: aiohttp!=4.0.0a0,!=4.0.0a1 in /usr/local/lib/python3.11/dist-packages (from fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (3.11.15)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests>=2.32.2->datasets) (3.4.2)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests>=2.32.2->datasets) (3.10)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests>=2.32.2->datasets) (2.4.0)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests>=2.32.2->datasets) (2025.4.26)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas->datasets) (2.9.0.post0)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas->datasets) (2025.2)\n",
+            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas->datasets) (2025.2)\n",
+            "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (2.6.1)\n",
+            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.11/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (1.3.2)\n",
+            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (25.3.0)\n",
+            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.11/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (1.6.0)\n",
+            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.11/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (6.4.4)\n",
+            "Requirement already satisfied: propcache>=0.2.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (0.3.1)\n",
+            "Requirement already satisfied: yarl<2.0,>=1.17.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (1.20.0)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.17.0)\n",
+            "Downloading datasets-3.6.0-py3-none-any.whl (491 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m491.5/491.5 kB\u001b[0m \u001b[31m6.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading huggingface_hub-0.33.0-py3-none-any.whl (514 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m514.8/514.8 kB\u001b[0m \u001b[31m14.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading fsspec-2025.3.0-py3-none-any.whl (193 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m193.6/193.6 kB\u001b[0m \u001b[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 📥 Step 1: Preprocess the Data\n",
+        "\n",
+        "Since the dataset is hosted on Hugging Face, you can load it directly using the `load_dataset` function."
+      ],
+      "metadata": {
+        "id": "edF1DuNE6nSg"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "from datasets import load_dataset\n",
+        "\n",
+        "# Load the dataset\n",
+        "dataset = load_dataset(\"Tetratics/2018-11-08-OpenPsychometrics-IPIP-FFM\")\n",
+        "\n",
+        "# Convert to a pandas DataFrame for easier manipulation\n",
+        "df = pd.DataFrame(dataset[\"train\"])\n",
+        "df"
+      ],
+      "metadata": {
+        "id": "HmHDx6cU7WRG"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}

upload_dataset.py

@@ -0,0 +1,27 @@
+
+from huggingface_hub import HfApi
+import os
+
+# Define the repository name
+repo_name = "Tetratics/2018-11-08-OpenPsychometrics-IPIP-FFM"
+
+# Initialize the Hugging Face API
+api = HfApi()
+
+# Create the repository on Hugging Face
+api.create_repo(repo_id=repo_name, repo_type="dataset")
+
+# Upload each Parquet file to the repository
+for split_name in ["train", "validation", "test"]:
+    file_path = f"{parquet_dir}/{split_name}.parquet"
+    if os.path.exists(file_path):
+        api.upload_file(
+            path_or_fileobj=file_path,
+            path_in_repo=f"{split_name}.parquet",
+            repo_id=repo_name,
+            repo_type="dataset"
+        )
+        print(f"Uploaded {split_name}.parquet to {repo_name}")
+    else:
+        print(f"{split_name}.parquet not found. Skipping upload.")
+