# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("jorgeortizv/reviewUsefulness-binaryClassification")
model = AutoModelForSequenceClassification.from_pretrained("jorgeortizv/reviewUsefulness-binaryClassification")Quick Links
This model is a fine-tunning of the bert-base-german-cased for predicting the usefulness of a review using the Bestande binary dataset (see jorgeortizv/Bestande). The purpose is to predict if a review found online for university courses would be considered useful for other users or not. Similar as what you can find in Stackoverflow, but removing the necesity of human anotators to evaluate reviews.
Details on training as well as a detailed explanation on the project can be found on: https://github.com/liamti5/UZH-Essentials-in-Text-and-Speech-Processing
Interpreting results:
- Label 0: neutral / not useful review
- Label 1: Useful review.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jorgeortizv/reviewUsefulness-binaryClassification")