---
library_name: transformers
datasets:
- tner/mit_movie_trivia
base_model:
- distilbert/distilbert-base-uncased
---

# Model Card for Model ID

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# 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


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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](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

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