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---
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].
--- |