README.md

metadata

license: cc-by-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)