Add Medium article, research paper, and sentiment datasets

data/datasets/medium_articles_posts/README.md

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+# Medium Articles Posts Dataset
+
+## Description
+
+The Medium Articles Posts dataset contains a collection of articles published on
+the Medium platform. Each article entry includes information such as the
+article's title, main content or text, associated URL or link, authors' names,
+timestamps, and tags or categories.
+
+## Dataset Info
+
+The dataset consists of the following features:
+
+- **title**: _(string)_ The title of the Medium article.
+- **text**: _(string)_ The main content or text of the Medium article.
+- **url**: _(string)_ The URL or link to the Medium article.
+- **authors**: _(string)_ The authors or contributors of the Medium article.
+- **timestamp**: _(string)_ The timestamp or date when the Medium article was
+  published.
+- **tags**: _(string)_ Tags or categories associated with the Medium article.
+
+## Dataset Size
+
+- **Total Dataset Size**: 1,044,746,687 bytes (approximately 1000 MB)
+
+## Splits
+
+The dataset is split into the following part:
+
+- **Train**:
+  - Number of examples: 192,368
+  - Size: 1,044,746,687 bytes (approximately 1000 MB)
+
+## Download Size
+
+- **Compressed Download Size**: 601,519,297 bytes (approximately 600 MB)
+
+### Usage example
+
+```python
+from datasets import load_dataset
+#Load the dataset
+dataset = load_dataset("Falah/medium_articles_posts")
+
+```

data/datasets/medium_articles_posts/load_dataset.py

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+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("Falah/medium_articles_posts")

data/datasets/medium_articles_posts/requirements.txt

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+datasets==2.9.0

data/datasets/research_papers_dataset/ReadME.md

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+---
+dataset_info:
+  features:
+    - name: title
+      dtype: string
+    - name: abstract
+      dtype: string
+  splits:
+    - name: train
+      num_bytes: 2363569633
+      num_examples: 2311491
+  download_size: 1423881564
+  dataset_size: 2363569633
+---
+
+<!-- markdownlint-disable -->
+
+## Research Paper Dataset 2023
+
+[Check out this website](https://huggingface.co/datasets/Falah/research_paper2023)
+
+### Dataset Information
+
+The "Research Paper Dataset 2023" contains information related to research
+papers. It includes the following features:
+
+- Title (dtype: string): The title of the research paper.
+- Abstract (dtype: string): The abstract of the research paper.
+
+### Dataset Splits
+
+The dataset is divided into one split:
+
+- Train Split:
+  - Name: train
+  - Number of Bytes: 2,363,569,633
+  - Number of Examples: 2,311,491
+
+### Download Information
+
+- Download Size: 1,423,881,564 bytes
+- Dataset Size: 2,363,569,633 bytes
+
+### Dataset Citation
+
+If you use this dataset in your research or project, please cite it as follows:
+
+```
+@dataset{Research Paper Dataset 2023,
+  author = {Falah.G.Salieh},
+  title = {Research Paper Dataset 2023,},
+  year = {2023},
+  publisher = {Hugging Face},
+  version = {1.0},
+  location = {Online},
+  url = {Falah/research_paper2023}
+}
+
+
+```
+
+### Apache License
+
+The "Research Paper Dataset 2023" is distributed under the Apache License 2.0.
+You can find a copy of the license in the LICENSE file of the dataset
+repository.
+
+The specific licensing and usage terms for this dataset can be found in the
+dataset repository or documentation. Please make sure to review and comply with
+the applicable license and usage terms before downloading and using the dataset.
+
+### Example Usage
+
+To load the "Research Paper Dataset 2023" using the Hugging Face Datasets
+Library in Python, you can use the following code:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("Falah/research_paper2023")
+```
+
+### Application of "Research Paper Dataset 2023" for NLP Text Classification and Chatbot Models
+
+The "Research Paper Dataset 2023" can be a valuable resource for various Natural
+Language Processing (NLP) tasks, including text classification and generating
+titles for books in the context of chatbot models. Here are some ways this
+dataset can be utilized for these applications:
+
+1. **Text Classification**: The dataset's features, such as the title and
+   abstract of research papers, can be used to train a text classification
+   model. By assigning appropriate labels to the research papers based on their
+   topics or fields of study, the model can learn to classify new research
+   papers into different categories. For example, the model can predict whether
+   a research paper is related to computer science, biology, physics, etc. This
+   text classification model can then be adapted for other applications that
+   require categorizing text.
+
+2. **Book Title Generation for Chatbot Models**: By utilizing the research paper
+   titles in the dataset, a natural language generation model, such as a
+   sequence-to-sequence model or a transformer-based model, can be trained to
+   generate book titles. The model can be fine-tuned on the research paper
+   titles to learn patterns and structures in generating relevant and meaningful
+   book titles. This can be a useful feature for chatbot models that recommend
+   books based on specific research topics or areas of interest.
+
+### Potential Benefits
+
+- Improved Chatbot Recommendations: With the ability to generate book titles
+  related to specific research topics, chatbot models can provide more relevant
+  and personalized book recommendations to users.
+- Enhanced User Engagement: By incorporating the text classification model, the
+  chatbot can better understand user queries and respond more accurately,
+  leading to a more engaging user experience.
+- Knowledge Discovery: Researchers and students can use the text classification
+  model to efficiently categorize large collections of research papers, enabling
+  quicker access to relevant information in specific domains.
+
+### Considerations
+
+- Data Preprocessing: Before training the NLP models, appropriate data
+  preprocessing steps may be required, such as text cleaning, tokenization, and
+  encoding, to prepare the dataset for model input.
+- Model Selection and Fine-Tuning: Choosing the right NLP model architecture and
+  hyperparameters, and fine-tuning the model on the specific tasks, can
+  significantly impact the model's performance and generalization ability.
+- Ethical Use: Ensure that the generated book titles and text classification
+  predictions are used responsibly and ethically, respecting copyright and
+  intellectual property rights.
+
+### Conclusion
+
+The "Research Paper Dataset 2023" holds great potential for enhancing NLP text
+classification models and chatbot systems. By leveraging the dataset's features
+and information, NLP applications can be developed to aid researchers, students,
+and readers in finding relevant research papers and generating meaningful book
+titles for their specific interests. Proper utilization of this dataset can lead
+to more efficient information retrieval and improved user experiences in the
+domain of research and academic literature exploration.

data/datasets/research_papers_dataset/load_dataset.py

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+from datasets import load_dataset
+
+dataset = load_dataset("Falah/research_paper2023")

data/datasets/research_papers_dataset/requirements.txt

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+datasets==2.9.0