Instructions to use bdotloh/roberta-base-empathy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bdotloh/roberta-base-empathy with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bdotloh/roberta-base-empathy")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bdotloh/roberta-base-empathy") model = AutoModelForSequenceClassification.from_pretrained("bdotloh/roberta-base-empathy") - Inference
- Notebooks
- Google Colab
- Kaggle
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- name: MSE loss
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type: MSE loss
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value: 7.07853364944458
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verified: true
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- name: Pearson's R (empathy)
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type: Pearson's R (empathy)
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value: 0.4336383660597612
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verified: true
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- name: Pearson's R (distress)
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type: Pearson's R (distress)
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value: 0.40006974689041663
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verified: true
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- name: MSE loss
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type: MSE loss
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value: 7.07853364944458
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- name: Pearson's R (empathy)
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type: Pearson's R (empathy)
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value: 0.4336383660597612
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- name: Pearson's R (distress)
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type: Pearson's R (distress)
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value: 0.40006974689041663
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