DGurgurov/

finetuned-bertu-sentiment

This model is a sentiment classification model fine-tuned on top of BERTu, a state-of-the-art Maltese language model. It is designed to analyze the sentiment of text in the Maltese language and classify it into different sentiment categories.

The model was fine-tuned on a dataset containing Maltese text with sentiment labels. The dataset consists of text samples in the Maltese language, each labeled with one of the following sentiment categories:

The model utilizes the BERTu architecture, which is a variant of BERT (Bidirectional Encoder Representations from Transformers) specifically optimized for the Maltese language. BERTu is known for its ability to capture contextual information from text and is pre-trained on a large corpus of Maltese text.

Fine-tuning is the process of adapting a pre-trained model to a specific task, in this case, sentiment classification. The model was fine-tuned on the sentiment-labeled Maltese text dataset using transfer learning. The fine-tuning process involves updating the model's weights and parameters to make it proficient at sentiment analysis.

The model's performance can be assessed through various evaluation metrics, including accuracy, precision, recall, and F1-score. It has been fine-tuned to achieve high accuracy in classifying text into the sentiment categories.

You can use this model for sentiment analysis of Maltese text. Given a text input, the model can predict whether the sentiment is positive, negative, or neutral. It can be integrated into applications, chatbots, or services to automatically assess the sentiment of user-generated content.

The model is made available under a specific license, and it's important to refer to the terms and conditions of use provided by the model's creator.

This fine-tuned sentiment classification model on BERTu for Maltese is the work of [Daniil Gurgurov].