This is a text classification model based on DistilBERT. It has been fine-tuned on the ecommerce_reviews_with_language_drift dataset.
The model is used for classifying product reviews in text format. The probable outputs are 'positive', 'negative' and 'neutral'.
The arize-ai/ecommerce_reviews_with_language_drift dataset was used for training. Only the 'text' and 'label' columns were used. The training dataset contains 8k rows out of which 34.1% are labeled 'positive', 33.4 % are labeled 'negative' and 32.5% are labeled 'neutral'. So it is a balanced dataset.
The model was fine tuned based on the F1 score for 50 epochs. The best score obtained was 0.67.
1from transformers import pipeline
2
3classifier = pipeline("text-classification", model="your-model-identifier")
4
5result = classifier("Your example text here")
6print(result)
