# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("valurank/distilroberta-bias")
model = AutoModelForSequenceClassification.from_pretrained("valurank/distilroberta-bias", device_map="auto")Quick Links
DistilROBERTA fine-tuned for bias detection
This model is based on distilroberta-base pretrained weights, with a classification head fine-tuned to classify text into 2 categories (neutral, biased).
Training data
The dataset used to fine-tune the model is wikirev-bias, extracted from English wikipedia revisions, see https://github.com/rpryzant/neutralizing-bias for details on the WNC wiki edits corpus.
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/distilroberta-bias")