
This RoBERTa-based model ("MindMiner") can classify the degree of mind perception in English language text in 2 classes:

- high mind perception 👩
- low mind perception 🤖

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.

{"language": "en", "tags": ["roberta"], "widget": [{"text": "Alexa is part of our family. She is simply amazing!"}, {"text": "I use my smart assistant for may things. It's incredibly useful."}]}
transformers pytorch roberta text-classification en autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #roberta #text-classification #en #autotrain_compatible #endpoints_compatible #has_space #region-us 


This RoBERTa-based model ("MindMiner") can classify the degree of mind perception in English language text in 2 classes:

- high mind perception 
- low mind perception 

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.

TAGS
#transformers #pytorch #roberta #text-classification #en #autotrain_compatible #endpoints_compatible #has_space #region-us 

passage: TAGS
#transformers #pytorch #roberta #text-classification #en #autotrain_compatible #endpoints_compatible #has_space #region-us 
