metadata
license: cc-by-nc-sa-4.0
datasets:
- Blablablab/ALOE
Model Description
The model classifies an appraisal given a sentence and is trained on ALOE dataset.
Input: a sentence
Labels: No Label, Pleasantness, Anticipated Effort, Certainty, Objective Experience, Self-Other Agency, Situational Control, Advice, Trope
Output: logits (in order of labels)
Model architecture: OpenPrompt+RoBERTa
Developed by: Jiamin Yang
Model Performance
Overall performance
| Macro-F1 | Recall | Precision |
|---|---|---|
| 0.56 | 0.57 | 0.58 |
Per-label performance
| Label | Recall | Precision |
|---|---|---|
| No Label | 0.34 | 0.64 |
| Pleasantness | 0.69 | 0.54 |
| Anticipated Effort | 0.46 | 0.46 |
| Certainty | 0.58 | 0.47 |
| Objective Experience | 0.58 | 0.69 |
| Self-Other Agency | 0.62 | 0.55 |
| Situational Control | 0.31 | 0.55 |
| Advice | 0.72 | 0.66 |
| Trope | 0.80 | 0.67 |
Getting Started
import torch
from openprompt.plms import load_plm
from openprompt.prompts import ManualTemplate
from openprompt.prompts import ManualVerbalizer
from openprompt import PromptForClassification
checkpoint_file = 'your_path_to/empathy-appraisal-span.pt'
plm, tokenizer, model_config, WrapperClass = load_plm('roberta', 'roberta-large')
template_text = 'The sentence {"placeholder":"text_a"} has the label {"mask"}.'
template = ManualTemplate(tokenizer=tokenizer, text=template_text)
num_classes = 9
label_words = [['No Label'], ['Pleasantness'], ['Anticipated Effort'], ['Certainty'], ['Objective Experience'], ['Self-Other Agency'], ['Situational Control'], ['Advice'], ['Trope']]
verbalizer = ManualVerbalizer(tokenizer, num_classes=num_classes, label_words=label_words)
prompt_model = PromptForClassification(plm=plm,template=template, verbalizer=verbalizer, freeze_plm=False).to('cuda')
checkpoint = torch.load(checkpoint_file)
state_dict = checkpoint['model_state_dict']
# depend on the version of torch
del state_dict['prompt_model.plm.roberta.embeddings.position_ids']
prompt_model.load_state_dict(state_dict)