license: apache-2.0
datasets:
- Lowerated/imdb-reviews-rated
language:
- en
metrics:
- mse
library_name: transformers
pipeline_tag: text-classification
tags:
- movies
- rating
- lowerated
Lowerated/deberta-v3-lm6
Model Details
Model Name: Lowerated/deberta-v3-lm6
Model Type: Text Classification (Aspect-Based Sentiment Analysis)
Language: English
Framework: PyTorch
License: Apache 2.0
Model Description
Lowerated/deberta-v3-lm6 is a DeBERTa-v3-based model fine-tuned for aspect-based sentiment analysis on IMDb movie reviews. The model is designed to classify sentiments across seven key aspects of filmmaking: Cinematography, Direction, Story, Characters, Production Design, Unique Concept, and Emotions.
Dataset
Dataset Name: Lowerated/imdb-reviews-rated
Dataset URL: IMDb Reviews Rated
Dataset Description: The dataset contains IMDb movie reviews with sentiment scores for seven aspects of filmmaking. Each review is labeled with sentiment scores for Cinematography, Direction, Story, Characters, Production Design, Unique Concept, and Emotions.
Performance
Evaluation Metric: Mean Squared Error (MSE)
MSE: 0.08594679832458496
Detailed Results
Cinematography:
- Precision: 0.96
- Recall: 0.97
- F1-score: 0.96
- Accuracy: 0.95
Confusion Matrix: [ \begin{bmatrix} 68 & 0 & 0 \ 1 & 377 & 37 \ 0 & 0 & 310 \ \end{bmatrix} ]
Direction:
- Precision: 0.93
- Recall: 0.97
- F1-score: 0.94
- Accuracy: 0.95
Story:
- Precision: 0.85
- Recall: 0.88
- F1-score: 0.85
- Accuracy: 0.85
Characters:
- Precision: 0.89
- Recall: 0.89
- F1-score: 0.89
- Accuracy: 0.90
Production Design:
- Precision: 0.95
- Recall: 0.98
- F1-score: 0.96
- Accuracy: 0.96
Unique Concept:
- Precision: 0.83
- Recall: 1.00
- F1-score: 0.89
- Accuracy: 1.00
Emotions:
- Precision: 0.76
- Recall: 0.87
- F1-score: 0.78
- Accuracy: 0.82
Test Results:
- Eval Loss: 0.08594681322574615
- Eval Model Preparation Time: 0.0011
- Eval MSE: 0.08594679832458496
- Eval Runtime: 23.1411
- Eval Samples per Second: 34.268
- Eval Steps per Second: 8.599
Intended Use
This model is intended for rating of movies across seven aspects of filmmaking. It can be used to provide a more nuanced understanding of viewer opinions and improve movie rating systems.
Limitations
While the model performs well on the evaluation dataset, its performance may vary on different datasets. Continuous monitoring and retraining with diverse data are recommended to maintain and improve its accuracy.
Future Work
Future improvements could focus on exploring alternative methods for handling neutral values, investigating advanced techniques for addressing missing ratings, enhancing sentiment analysis methods, and expanding the range of aspects analyzed.
Citation
If you use this model in your research, please cite it as follows:
@model{lowerated_deberta-v3-lm6,
author = {LOWERATED},
title = {deberta-v3-lm6},
year = {2024},
url = {https://huggingface.co/Lowerated/deberta-v3-lm6},
}