srb2703/DistilBERT-NER-Finetuned

Model Card for Model ID

DistilBERT NER - MIT Movie Trivia Dataset

This model is a fine-tuned version of DistilBERT (distilbert-base-uncased) on the MIT Movie Trivia Dataset for Named Entity Recognition (NER). It is designed to tag movie-related named entities such as actors, directors, characters, genres, and more.

Model Details

Base Model: DistilBERT (distilbert-base-uncased) Dataset: MIT Movie Trivia NER Dataset Fine-tuning Task: Named Entity Recognition (NER) Framework: Hugging Face Transformers Tokenizer: distilbert-base-uncased Total Parameters: ~66M Training Time: Approx. 8 epochs Intended Use This model is intended for Named Entity Recognition (NER) on movie-related text, such as:

Identifying actors, directors, characters, genres, release years, etc. Extracting structured information from movie-related questions or reviews. NER Labels (BIO Format) The model uses the BIO (Beginning-Inside-Outside) format for labeling:

Tag Description B-Actor Beginning of an actor's name I-Actor Inside an actor's name B-Director Beginning of a director's name I-Director Inside a director's name B-Character_Name Beginning of a character's name I-Character_Name Inside a character's name B-Genre Beginning of a genre I-Genre Inside a genre B-Year Movie release year B-Plot Beginning of a plot description I-Plot Inside a plot description B-Quote Beginning of a movie quote I-Quote Inside a movie quote O Outside any named entity

Model Description

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Training Details

Training Details Optimizer: AdamW Learning Rate: 2e-5 Batch Size: 8 Evaluation Strategy: Epoch-based Loss Function: CrossEntropyLoss

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Results

Which movies did Quentin Tarantino make?? Ans: Entity: quentin tarantino, Label: Director, Score: 1.00

Leonardo DiCaprio starred in which movies? Ans: Entity: leonardo dicaprio, Label: Actor, Score: 1.00

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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