Improve speed by using dense evaluation for local models

[gordonwatts]

From co-piolot:

The warning message indicates that you are using the transformers library's pipeline feature in a way that processes data sequentially on the GPU. This can be inefficient because it doesn't fully utilize the GPU's parallel processing capabilities. Instead, the library suggests using a dataset to batch process the data, which can significantly improve performance by leveraging the GPU more effectively.

Steps to Fix the Issue
Create a Dataset: Use a dataset to hold your input data.
Batch Processing: Use the dataset with the pipeline to process data in batches.
Pseudocode
Import necessary libraries.
Create a dataset from your input data.
Use the dataset with the pipeline for batch processing.
Example Code
Explanation
Import Libraries: Import the pipeline from transformers and Dataset from datasets.
Create Dataset: Convert your input data into a Dataset object.
Initialize Pipeline: Initialize the pipeline for your specific task (e.g., sentiment analysis).
Batch Processing: Use the pipeline to process the dataset in batches by specifying the batch_size parameter.
Output Results: Print or use the results as needed.
By following these steps, you can improve the efficiency of your pipeline by utilizing the GPU more effectively.