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