Lightweight Explainable Multilingual Movie Script Certi
Collection
Official research resources accompanying the paper "Lightweight and Explainable Neural Models for Multilingual Movie Script Certification". • 6 items • Updated
Text Classification
Transformers
Safetensors
English
distilbert
movie-certification
content-rating
movie-script
nlp
multilingual
knowledge-distillation
long-document
explainable-ai
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("pratikkalamkar/moviecert-teacher-en")
model = AutoModelForSequenceClassification.from_pretrained("pratikkalamkar/moviecert-teacher-en", device_map="auto")Quick Links
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
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
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
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
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.
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.
Recommendations
The model should be used as an assistive tool rather than a replacement for human reviewers.
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.
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
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)
Evaluation
Testing Data
Held-out English movie script test split.
Metrics
The following metrics were used:
- Accuracy
- Precision
- Recall
- Macro F1-score
- Confusion Matrix
Results
The complete experimental results are reported in the associated journal paper.
Technical Specifications
Model Architecture
- DistilBERT multilingual encoder
- Sequence Classification Head
- Fine-tuned on English movie scripts
Software
- Python
- PyTorch
- Hugging Face Transformers
- Scikit-learn
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}
}
More Information
This model is one component of the MovieCert multilingual movie certification framework comprising:
- English Teacher Model
- Hindi Teacher Model
- Marathi Teacher Model
Model Card Author
Pratik N. Kalamkar
Contact
For questions regarding the model or the associated research, please contact:
Pratik N. Kalamkar
GitHub: https://github.com/pratik1986
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pratikkalamkar/moviecert-teacher-en")