moviecert-teacher-en

MovieCert English Teacher Model

Model Details

Model Description

This repository contains the English Teacher model developed for the research paper "Lightweight and Explainable Neural Models for Multilingual Movie Script Certification."

The model is fine-tuned from the multilingual DistilBERT architecture to classify full English movie scripts into age-rating categories. It serves as the teacher model in a knowledge distillation framework used to develop lightweight student models for efficient deployment.

• Developed by: Pratik N. Kalamkar
• Funded by: Self-funded academic research
• Shared by: Pratik N. Kalamkar
• Model type: Transformer-based sequence classification model (DistilBERT)
• Language(s): English
• License: Apache-2.0
• Finetuned from model: distilbert-base-multilingual-cased

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Model Sources

• Repository: https://github.com/pratik1986/Lightweight-and-Explainable-Neural-Models-for-Multilingual-Movie-Script-Certification
• Paper: https://doi.org/10.5815/ijitcs.2026.02.09
• Dataset: https://huggingface.co/datasets/pratikkalamkar/Movie_Scripts_with_Age_Ratings_by_Pratik_Kalamkar-EN

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Uses

Direct Use

This model is intended for automated classification of English movie scripts into age-rating categories.

Potential applications include:

• Automated movie certification research
• Movie script analysis
• Content moderation
• Academic NLP research
• Benchmarking multilingual transformer models

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Downstream Use

The model may be used as:

• Teacher model for knowledge distillation
• Base model for further fine-tuning
• Feature extractor
• Long-document text classification research

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Out-of-Scope Use

This model is not intended for:

• Legal or official movie certification decisions
• Safety-critical systems
• Hate speech detection
• Sentiment analysis
• Medical or legal decision making

Predictions should not replace human review.

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Bias, Risks and Limitations

The model was trained using a curated dataset of English movie scripts and inherits limitations of the dataset, including:

• Limited dataset size
• Possible class imbalance
• Genre imbalance
• Cultural bias
• Language-specific ambiguity

Performance may decrease on scripts that differ substantially from the training distribution.

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Recommendations

The model should be used as an assistive tool rather than a replacement for human reviewers.

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Training Details

Training Data

The model was trained using the English Movie Scripts with Verified Age Ratings https://huggingface.co/datasets/pratikkalamkar/Movie_Scripts_with_Age_Ratings_by_Pratik_Kalamkar-EN dataset curated by the author.

Training data consists of English movie scripts labelled with their corresponding age ratings.

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Training Procedure

The model was fine-tuned using the Hugging Face Transformers library.

Preprocessing

• UTF-8 normalization
• Tokenization using DistilBERT tokenizer
• Sliding-window chunking for long movie scripts
• Chunk-level prediction aggregation

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Training Hyperparameters

• Base model: distilbert-base-multilingual-cased
• Epochs: 6
• Batch size (train): 8
• Batch size (evaluation): 16
• Learning rate: 2e-5
• Weight decay: 0.01
• Warmup steps: 50
• Optimizer: AdamW
• Maximum gradient norm: 1.0
• Early stopping patience: 2
• Mixed precision: Disabled (FP32)

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Evaluation

Testing Data

Held-out English movie script test split.

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Metrics

The following metrics were used:

• Accuracy
• Precision
• Recall
• Macro F1-score
• Confusion Matrix

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Results

The complete experimental results are reported in the associated journal paper.

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Technical Specifications

Model Architecture

• DistilBERT multilingual encoder
• Sequence Classification Head
• Fine-tuned on English movie scripts

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Software

• Python
• PyTorch
• Hugging Face Transformers
• Scikit-learn

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Citation

If you use this model in your research, please cite:

BibTeX

@article{kalamkar2026moviecertification,
  author = {Pratik N. Kalamkar and Prasadu Peddi and Yogesh K. Sharma},
  title = {Lightweight and Explainable Neural Models for Multilingual Movie Script Certification},
  journal = {International Journal of Information Technology and Computer Science},
  volume = {18},
  number = {2},
  pages = {146--160},
  year = {2026},
  doi = {10.5815/ijitcs.2026.02.09}
}

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More Information

This model is one component of the MovieCert multilingual movie certification framework comprising:

• English Teacher Model
• Hindi Teacher Model
• Marathi Teacher Model

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Model Card Author

Pratik N. Kalamkar

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Contact

For questions regarding the model or the associated research, please contact:

Pratik N. Kalamkar

GitHub: https://github.com/pratik1986