This model is a fine-tuned version of distilbert-base-uncased specifically designed for Question Answering (QA) tasks on movie-related information. It was trained on a custom dataset generated from the imdb_movies.csv dataset, which includes movie titles, genres, release dates, budgets, revenues, and overviews.
The model's primary purpose is to extract answers to questions about movie attributes (such as budget, revenue, genre, release year, and description) given a context string that contains this information. This makes it suitable for applications requiring information retrieval from structured movie data presented in a natural language context.
Key Features:
Base Model: distilbert-base-uncased Task: Extractive Question Answering Training Data: Custom dataset derived from IMDB movie data, formatted into a SQuAD-like structure. Capabilities: Answering direct questions about movie properties like budget, revenue, genre, release date, and summary from provided text. Intended Use: This model can be used for building conversational agents, information extraction systems, or search functionalities related to movie databases where specific factual details need to be retrieved.