Update Readme

README.md

@@ -1,3 +1,71 @@
----
-license: cc-by-sa-4.0
----
+---
+license: cc-by-sa-4.0
+datasets:
+- Blablablab/ALOE
+---
+
+### Model Description
+
+The model classifies an *appraisal* given a sentence and is trained on [ALOE](https://huggingface.co/datasets/Blablablab/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
+
+```python
+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)
+```
+