| | --- |
| | license: mit |
| | task_categories: |
| | - text-classification |
| | language: |
| | - en |
| | tags: |
| | - code |
| | size_categories: |
| | - 10M<n<100M |
| | --- |
| | # README |
| |
|
| | ## Introduction |
| |
|
| | This dataset contains the introductions of all model repositories from Hugging Face. |
| | It is designed for text classification tasks and aims to provide a rich and diverse collection of model descriptions for various natural language processing (NLP) applications. |
| |
|
| | Each introduction provides a concise overview of the model's purpose, architecture, and potential use cases. |
| | The dataset covers a wide range of models, including but not limited to language models, text classifiers, and generative models. |
| |
|
| |
|
| | ## Usage |
| |
|
| | This dataset can be used for various text classification tasks, such as: |
| |
|
| | - **Model Category Classification**: Classify models into different categories based on their introductions (e.g., language models, text classifiers, etc.). |
| | - **Sentiment Analysis**: Analyze the sentiment of the introductions to understand the tone and focus of the model descriptions. |
| | - **Topic Modeling**: Identify common topics and themes across different model introductions. |
| |
|
| | ### Preprocessing |
| |
|
| | Before using the dataset, it is recommended to perform the following preprocessing steps: |
| |
|
| | 1. **Text Cleaning**: Remove any HTML tags, special characters, or irrelevant content from the introductions. |
| | 2. **Tokenization**: Split the text into individual tokens (words or subwords) for further analysis. |
| | 3. **Stop Words Removal**: Remove common stop words that do not contribute significantly to the meaning of the text. |
| | 4. **Lemmatization/Stemming**: Reduce words to their base or root form to normalize the text. |
| |
|
| | ### Model Training |
| |
|
| | You can use this dataset to train machine learning models for text classification tasks. |
| | Here is a basic example using Python and the scikit-learn library: |
| |
|
| | ```python |
| | import pandas as pd |
| | from sklearn.model_selection import train_test_split |
| | from sklearn.feature_extraction.text import TfidfVectorizer |
| | from sklearn.naive_bayes import MultinomialNB |
| | from sklearn.metrics import accuracy_score |
| | |
| | # Load the dataset |
| | data = pd.read_csv("dataset.csv") |
| | |
| | # Split the data into training and testing sets |
| | X_train, X_test, y_train, y_test = train_test_split(data["introduction"], data["category"], test_size=0.2, random_state=42) |
| | |
| | # Vectorize the text data |
| | vectorizer = TfidfVectorizer() |
| | X_train_tfidf = vectorizer.fit_transform(X_train) |
| | X_test_tfidf = vectorizer.transform(X_test) |
| | |
| | # Train a Naive Bayes classifier |
| | model = MultinomialNB() |
| | model.fit(X_train_tfidf, y_train) |
| | |
| | # Make predictions and evaluate the model |
| | y_pred = model.predict(X_test_tfidf) |
| | accuracy = accuracy_score(y_test, y_pred) |
| | print(f"Model Accuracy: {accuracy:.2f}") |
| | ``` |
| |
|
| | You can also refer to my [blog](https://blog.csdn.net/Xm041206/article/details/138907342). |
| |
|
| | ## License |
| |
|
| | This dataset is licensed under the [License Name]. You are free to use, modify, and distribute the dataset for research and educational purposes. For commercial use, please refer to the specific terms of the license. |
| |
|
| | ## Acknowledgments |
| |
|
| | We would like to thank the Hugging Face community for providing such a rich and diverse collection of models. |
| | This dataset would not have been possible without their contributions. |
| |
|
| | ## Contact |
| |
|
| | For any questions or feedback regarding this dataset, |
| | please leave a message or contact me at [https://github.com/XuMian-xm]. |
| |
|
| | --- |
