Update README.md

README.md

@@ -1,54 +1,218 @@
+# 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
+
 ---
-license: apache-2.0
-datasets:
-- pratikkalamkar/Movie_Scripts_with_Age_Ratings_by_Pratik_Kalamkar-EN
-language:
-- en
-metrics:
-- accuracy
-- precision
-- recall
-- f1
-base_model:
-- distilbert/distilbert-base-multilingual-cased
-pipeline_tag: text-classification
-library_name: transformers
-tags:
-- movie-certification
-- movie-script
-- content-rating
-- text-classification
-- nlp
-- multilingual
-- distilbert
-- knowledge-distillation
-- transformers
-- english
----
-This repository contains the English Teacher model developed for the paper
-
-"Lightweight and Explainable Neural Models for Multilingual Movie Script Certification"
-
-The model classifies full-length movie scripts into age-rating categories.
-
-Citation
-If you use this model, please cite:
-
-Kalamkar, P. N., Peddi, P., & Sharma, Y. K. (2026).
-
-"Lightweight and Explainable Neural Models for Multilingual Movie Script Certification."
-
-International Journal of Information Technology and Computer Science (IJITCS), Volume 18, Issue 2, Pages 146–160.
-
-DOI: 10.5815/ijitcs.2026.02.09
 
+# Citation
+
+If you use this model in your research, please cite:
+
+**BibTeX**
+
+```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},
-year = {2026},
-volume = {18},
-number = {2},
-pages = {146--160},
-doi = {10.5815/ijitcs.2026.02.09}
-}
+  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