📢 DistilBERT Fine-Tuned for Sentiment Analysis 🎭

This model is a fine-tuned DistilBERT for sentiment analysis on movie review texts. It classifies texts as positive or negative with high accuracy.

📖 Dataset

The model was trained on the IMDb dataset, which contains 50,000 movie reviews balanced between positive and negative labels.

📊 Evaluation

The model's performance was evaluated on a test set of 25,000 reviews, achieving the following results:

Metric	Value
Accuracy	93.2%
Precision	93%
Recall	93%
F1-score	93%

These metrics indicate that the model correctly predicts movie reviews in most cases.

📊 Confusion Matrix

The confusion matrix below shows the model's performance on predictions:

🚀 How to Use

To use the model, simply load it with transformers and use the text classification pipeline:

from transformers import pipeline

model_name = "gabrielnkl/fine-tuned-bert"

classifier = pipeline("text-classification", model=model_name)

text = "This movie was amazing! I really liked the acting and the storyline."
result = classifier(text)

print(result)
# [{'label': 'POSITIVE', 'score': 0.98}]


O notebook usado para treinar este modelo está disponível no GitHub:

(https://github.com/Gabrielnkl/fine-tuned-bert-model/blob/main/finetuning.py)