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