In the vibrant realm of linguistics, Spanglish—a beautiful blend of Spanish and English—holds a unique charm. To harness the potential of this hybrid language, we present you with the BERT Codemixed Base Model for Spanglish. This model can help you analyze sentiment in codemixed texts efficiently.

What is the BERT Codemixed Model?

This model utilizes the popular BERT framework to interpret codemixed Spanglish text. Whether you’re facing a positive, negative, or neutral sentiment, this model has you covered!

Input and Output

• Input: Codemixed Spanglish text
• Output: Sentiment classification (0 – Negative, 1 – Neutral, 2 – Positive)

Model Performance

The model metrics are as follows:

• Accuracy: 0.7186
• F1 Score: 0.71759
• Precision: 0.7193
• Recall: 0.7186

How to Use the Model

Let’s get started with utilizing this model! Whether you prefer PyTorch or TensorFlow, we have got you covered.

Using PyTorch

Here’s how you can implement this model using PyTorch:

from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("rohanrajpal/bert-base-en-es-codemix-cased")
model = AutoModelForSequenceClassification.from_pretrained("rohanrajpal/bert-base-en-es-codemix-cased")

text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)

Using TensorFlow

Alternatively, here’s how to use TensorFlow:

from transformers import BertTokenizer, TFBertModel

tokenizer = BertTokenizer.from_pretrained("rohanrajpal/bert-base-en-es-codemix-cased")
model = TFBertModel.from_pretrained("rohanrajpal/bert-base-en-es-codemix-cased")

text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)

Training Data and Procedure

The model was trained using the CS-EN-ES-CORPUS dataset, alongside the bert-base-multilingual-cased model. Remember, preprocessing your data is crucial before using this model to achieve optimal results!

Limitations and Troubleshooting

While this model serves as an introduction to sentiment analysis in codemixed texts, keep in mind the following:

• Quality Verification: The author does not speak Spanish and cannot verify the annotation quality of the dataset.
• Bias: This is a basic transfer learning application. Improvements and discussions are welcomed.

Troubleshooting Tips

If you encounter issues during implementation, consider the following troubleshooting steps:

• Ensure that you have the correct packages installed, including the Transformers library.
• Double-check the versions of PyTorch or TensorFlow you are using; compatibility can impact performance.
• Make sure to follow the preprocessing methods outlined here.

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At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.