This model performs Named Entity Recognition specialized for analyzing product reviews of board games, particularly Cards Against Humanity. It identifies five custom entity types corresponding to different components of perceived value: PRODUCT (functional value), ATTRIBUTE (emotional value), PEOPLE (social value), EVENT (contextual value), and OTHER_GAME (comparative value).
Model type: Named Entity Recognition (Token Classification)
Language(s) (NLP): English
Finetuned from model: roberta-large
Uses
This model is designed for extracting and categorizing value components from product reviews to:
Analyze perceived value propositions
Identify key product attributes that resonate with consumers
Segment customers based on value perception profiles
Develop targeted marketing strategies
Bias, Risks, and Limitations
Entity recognition performs better for some categories (OTHER_GAME, PRODUCT) than others (ATTRIBUTE, PEOPLE)
The model was trained on a relatively small dataset (500 reviews), which may limit its generalizability
Domain-specific training means performance will be best for board/card game reviews
How to Get Started with the Model
Use the code below to get started with the model.
from transformers import pipeline
ner_pipe = pipeline(
"token-classification",
model="qxswy/roberta-card-ner",
tokenizer="qxswy/roberta-card-ner",
aggregation_strategy="simple"
)
text = "This fun game is perfect for parties with friends!"
entities = ner_pipe(text)
print(entities)
Training Details
Training Data
The model was trained on 500 manually annotated Amazon reviews with domain-specific entity labels. The data was labeled using Label Studio, with stratified sampling to ensure representation across review ratings and lengths. Inter-annotator agreement (Cohen's kappa) for the annotation process was 0.78.
Training Procedure
Preprocessing: BIO (Begin-Inside-Outside) tagging scheme
Training Hyperparameters:
Learning rate: 3e-5
Batch size: 4
Epochs: 8
Max sequence length: 256 tokens
Special techniques: R-Drop for consistency, oversampling for rare categories
