PolitBERT Open-Source Model - Free Analysis of Speeches, Interviews, and News Content of Political Figures in English-Speaking Countries

Politbert

Developed by maurice

PolitBERT is a BERT model specifically designed for analyzing speeches, interviews, and press conference content of political figures in English-speaking countries.

Large Language Model #Political Figure Text Analysis #English Political Corpus #Masked Language Modeling

Downloads 28

Release Time : 3/2/2022

Model Overview

This model is based on the BERT architecture and optimized for political texts, capable of predicting missing words in political contexts or analyzing features of political language.

Model Features

Political Text Optimization

Trained specifically on speeches, interviews, and press conference content of political figures, enabling better understanding of political contexts.

Based on BERT Architecture

Leverages BERT's powerful language understanding capabilities for superior performance on political texts.

Easy to Use

Provides a standard Hugging Face interface for easy integration into existing NLP workflows.

Model Capabilities

Political Text Analysis

Masked Word Prediction

Political Language Feature Extraction

Use Cases

Political Text Analysis

Political Figure Speech Analysis

Analyze speeches or interviews of political figures to predict vocabulary or expressions they might use.

Political Bias Identification

Assist in identifying political biases or stances in texts through the model's processing of political content.

🚀 PolitBERT

A specialized model for political speeches, interviews, and press briefings of English - speaking politicians.

🚀 Quick Start

To predict a missing word of a sentence, you can use the following pipeline:

from transformers import pipeline, BertTokenizer, AutoModel

fill_mask = pipeline("fill - mask", 
                     model=AutoModel.from_pretrained('maurice/PolitBERT'), 
                     tokenizer=BertTokenizer.from_pretrained('bert - base - uncased'))

print(fill_mask('Donald Trump is a [MASK].'))

✨ Features

This model is specialized in processing political speeches, interviews, and press briefings of English - speaking politicians, leveraging the power of pre - trained BERT technology.

📦 Installation

No specific installation steps are provided in the original README.

💻 Usage Examples

Basic Usage

# To predict a missing word of a sentence
from transformers import pipeline, BertTokenizer, AutoModel

fill_mask = pipeline("fill - mask", 
                     model=AutoModel.from_pretrained('maurice/PolitBERT'), 
                     tokenizer=BertTokenizer.from_pretrained('bert - base - uncased'))

print(fill_mask('Donald Trump is a [MASK].'))

📚 Documentation

Background

This model was created to specialize on political speeches, interviews and press briefings of English - speaking politicians.

Training

The model was initialized using the pre - trained weights of BERT_BASE and trained for 20 epochs on the standard MLM task with default parameters. The used learning rate was 5e - 5 with a linearly decreasing schedule and AdamW. The used batch size is 8 per GPU while being trained on two Nvidia GTX TITAN X. The rest of the used configuration is the same as in AutoConfig.from_pretrained('bert - base - uncased'). As a tokenizer the default tokenizer of BERT was used (BertTokenizer.from_pretrained('bert - base - uncased'))

Dataset

PolitBERT was trained on the following dataset, which has been split up into single sentences: <https://www.kaggle.com/mauricerupp/englishspeaking - politicians>

Training Results

Evaluation Loss:
Training Loss:
Learning Rate Schedule:

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