Instructions to use LACAI/roberta-large-adapted-PFG-progression with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LACAI/roberta-large-adapted-PFG-progression with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LACAI/roberta-large-adapted-PFG-progression")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LACAI/roberta-large-adapted-PFG-progression") model = AutoModelForSequenceClassification.from_pretrained("LACAI/roberta-large-adapted-PFG-progression") - Notebooks
- Google Colab
- Kaggle
Base model: lacai/roberta-large-dialog-narrative
Fine tuned as a progression model (to predict the acceptability of a dialogue) on the Persuasion For Good Dataset (Wang et al., 2019):
Given a complete dialogue from (or in the style of) Persuasion For Good, the task is to predict a numeric score typically in the range (-3, 3) where a higher score means a more acceptable dialogue in context of the donation solicitation task.
This model inherits a special dialogue token <d> from its base model, which indicates the start of a dialogue utterance.
Example input: <d>How are you?</s><d>Good! how about yourself?</s><d>Great. Would you like to donate today to help the children?</s>
For more context and usage information see https://github.rpi.edu/LACAI/dialogue-progression.
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