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--- |
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library_name: transformers |
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datasets: |
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- thu-coai/augesc |
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--- |
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Test set performance |
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- Top 1 Accuracy: 0.4346 |
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- Top 3 Accuracy: 0.7677 |
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- Top 1 Macro F1: 0.2668 |
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- Top 3 Macro F1: 0.5669 |
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### Usage |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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device="cuda:0" |
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model = "heegyu/TinyLlama-augesc-context-strategy" |
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tokenizer = AutoTokenizer.from_pretrained(model) |
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model = AutoModelForSequenceClassification.from_pretrained(model).eval().to(device) |
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example = """usr: Hi |
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sys[Question]: Hello, how are you today? |
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usr: I was scolded by my parents yesterday""" |
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inputs = tokenizer(example, return_tensors="pt").to(device) |
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logits = model(**inputs).logits.softmax(-1) |
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print(logits) |
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label = logits.argmax(-1).item() |
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ESCONV_STRATEGY = [ |
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"Question", |
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"Restatement or Paraphrasing", |
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"Reflection of feelings", |
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"Self-disclosure", |
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"Affirmation and Reassurance", |
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"Providing Suggestions", |
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"Information", |
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"Others" |
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] |
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id2label = {i:k for i, k in enumerate(ESCONV_STRATEGY)} |
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print(id2label[label]) |
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``` |
