This model is a fine-tuned version of distilroberta-base model to predict 3 categories of stance (negative, positive, neutral) towards some entity mentioned in the text.
Fine-tuned on a larger and more balanced data sample compared with the previous version eevvgg/Stance-Tw.
Developed by: Ewelina Gajewska
Model type: RoBERTa for stance classification
Language(s) (NLP): English social media data from Twitter and Reddit
from transformers import pipeline
model_path = "eevvgg/StanceBERTa"
cls_task = pipeline(task = "text-classification", model = model_path, tokenizer = model_path)#, device=0
sequence = ["user The fact is that she still doesn’t change her ways and still stays non environmental friendly"
"user The criteria for these awards dont seem to be very high."]
result = cls_task(sequence)
Model suited for classification of stance in short text. Fine-tuned on a balanced corpus of size 5.6k, partially semi-annotated.
*Suitable for fine-tuning on hate/offensive language detection.
Model Sources
Repository: training procedure available in Colab notebook
Paper : tba
Training Details
Preprocessing
Normalization of user mentions and hyperlinks to "@user" and "http" tokens, respectively.
