Update README.md

README.md

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-metadata
+---
+license: "bsd"
+annotations_creators:
+  - manual
+language:
+  - en
+task_categories:
+  - text-classification
+pretty_name: "76057 Output Dataset"
+---
 
-license: bsd
+# Dataset Card
 
-datasets:
-    - ajsbsd/76057/output.jnlp
+## Table of Contents
+- [Dataset Description](#dataset-description)
+- [Dataset Summary](#dataset-summary)
+- [Supported Tasks](#supported-tasks)
+- [Dataset Structure](#dataset-structure)
+- [Usage](#usage)
+- [Additional Information](#additional-information)
+
+## Dataset Description
+The **76057 Output Dataset** is a curated collection of textual data primarily intended for **text classification tasks**. The dataset was manually annotated and contains examples in English. It can be used for training and evaluating machine learning models, particularly those focused on understanding or categorizing natural language text.
+
+This dataset may have been derived from various sources (e.g., surveys, user inputs, logs) depending on the original use case. The exact origin and content depend on how the dataset was constructed and labeled.
+
+## Dataset Summary
+- **Creator**: Manual annotators
+- **Language**: English (`en`)
+- **License**: BSD License
+- **Task Categories**: Text Classification
+- **Number of Examples**: Not specified (will depend on actual data)
+- **Annotation Type**: Manual annotations for classification labels
+
+## Supported Tasks
+The primary task supported by this dataset is **text classification**, where each input text is associated with one or more categorical labels. This could include binary classification (e.g., spam/not-spam), multi-class classification (e.g., topic categorization), or multi-label classification (e.g., tagging multiple relevant topics per text).
+
+## Dataset Structure
+The dataset likely includes at least two fields:
+
+- `text`: A string representing the input text.
+- `label`: A categorical value (string or integer) representing the class or classes assigned to the text.
+
+Depending on the structure, it may also contain:
+- Metadata such as source IDs, timestamps, or annotator IDs
+- Confidence scores if multiple annotators were involved
+- Additional features like sentiment scores, keywords, etc.
+
+## Usage
+To use this dataset, you can load it using standard tools such as `datasets` from Hugging Face, or directly via file readers if stored in CSV, JSON, or similar formats.
+
+Example usage in Python:
+```python
+import pandas as pd
+df = pd.read_csv("76057_output_dataset.csv")
+texts = df["text"].tolist()
+labels = df["label"].tolist()