Create README.md

README.md

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+### Model Summary
+
+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.
+
+### Dataset
+
+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:
+- Positive
+- Neutral
+
+### Model Architecture
+
+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
+
+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.
+
+### Performance
+
+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.
+
+### Usage
+
+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.
+
+### License
+
+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.
+
+### Creator
+
+This fine-tuned sentiment classification model on BERTu for Maltese is the work of [Daniil Gurgurov].