# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("valurank/distilbert-quality")
model = AutoModelForSequenceClassification.from_pretrained("valurank/distilbert-quality")Quick Links
DistilBERT fine-tuned for news classification
This model is based on distilbert-base-uncased pretrained weights, with a classification head fine-tuned to classify news articles into 3 categories (bad, medium, good).
Training data
The dataset used to fine-tune the model is news-small, the 300 article news dataset manually annotated by Alex.
Inputs
Similar to its base model, this model accepts inputs with a maximum length of 512 tokens.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="valurank/distilbert-quality")