BETO Es Binary Classification

🚀 BETO(cased)

This is a model built with PyTorch for sentiment analysis of Spanish text, determining whether it's related to technology.

🚀 Quick Start

This model was built using PyTorch. The input can be any Spanish text, and the output is sentiment (0 for Negative, 1 for Positive, i.e., technology-related).

✨ Features

• Input: Any Spanish text.
• Output: Sentiment (0 - Negative, 1 - Positive, i.e., technology-related).

📦 Installation

No specific installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

# You can include sample code which will be formatted
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("hiiamsid/BETO_es_binary_classification")
model = AutoModelForSequenceClassification.from_pretrained("hiiamsid/BETO_es_binary_classification")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)

📚 Documentation

Model description

The input for the model is any Spanish text, and the output is sentiment. (0 - Negative, 1 - Positive(i.e. technology relate)).

Training procedure

The model was trained on the dataset dccuchile/bert-base-spanish-wwm-cased.

📄 License

The model is licensed under the "apache-2.0" license.

📋 Information Table