Instructions to use j-hartmann/MindMiner-Binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use j-hartmann/MindMiner-Binary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="j-hartmann/MindMiner-Binary")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("j-hartmann/MindMiner-Binary") model = AutoModelForSequenceClassification.from_pretrained("j-hartmann/MindMiner-Binary") - Notebooks
- Google Colab
- Kaggle
This RoBERTa-based model ("MindMiner") can classify the degree of mind perception in English language text in 2 classes:
- high mind perception ๐ฉ
- low mind perception ๐ค
The model was fine-tuned on 997 manually annotated open-ended survey responses. The hold-out accuracy is 75.5% (vs. a balanced 50% random-chance baseline).
Hartmann, J., Bergner, A., & Hildebrand, C. (2023). MindMiner: Uncovering Linguistic Markers of Mind Perception as a New Lens to Understand Consumer-Smart Object Relationships. Journal of Consumer Psychology, Forthcoming.
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