Preeti
The content provides a guide on how to perform multilabel classification using the Mistral-7B model, a variant of the GPT-3.5 architecture. The steps include setting up the environment, loading CSV data, preparing data for the model, fine-tuning the model, and making predictions.
To start, ensure Python is installed and install necessary libraries like transformers, torch, pandas, and scikit-learn. Then, load CSV data, split labels into lists, extract texts and labels, and binarize the labels using MultiLabelBinarizer.
Next, prepare data for the model by loading the tokenizer, tokenizing texts, and setting up the dataset for training. Fine-tune the model by loading the model, defining the optimizer and loss function, and running a training loop for a specified number of epochs.
Finally, make predictions by putting the model in evaluation mode, tokenizing example texts, predicting labels, and converting predictions to label format. It is important to adjust hyperparameters like learning rate, batch size, and number of epochs based on the specific needs and dataset.
By following these steps, users can effectively perform multilabel classification using the Mistral-7B model or a similar transformer model with data from a CSV file.
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Source link: https://medium.com/@preeti.rana.ai/multilabel-classification-using-mistral-7b-5f8b2857e4ad?source=rss——hugging_face-5
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