kienhoang123/PhoBERT_Poem_Analysis_Seq2Seq

---

    language: vi
    license: apache-2.0
    tags:
    - vietnamese
    - poem-analysis
    - phobert
    - sequence-classification
    datasets:
    - kienhoang123/Vietnamese_Poem_Analysis_VN
    ---

    # PhoBERT Model for Vietnamese Poem Analysis

    This model was fine-tuned on kienhoang123/Vietnamese_Poem_Analysis_VN to analyze Vietnamese poetry using a sequence classification approach.

    ## Model Details

    - **Base Model**: vinai/phobert-base
    - **Training Data**: Vietnamese poem analysis dataset
    - **Tasks**: Predict presence of emotion, metaphor, setting, motion, and prompt in Vietnamese poems

    ## Usage

    ```python
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    import torch

    # Load model and tokenizer
    tokenizer = AutoTokenizer.from_pretrained("kienhoang123/PhoBERT_Poem_Analysis_Seq2Seq")
    model = AutoModelForSequenceClassification.from_pretrained("kienhoang123/PhoBERT_Poem_Analysis_Seq2Seq")

    # Prepare your input
    poem = "Your Vietnamese poem here"
    inputs = tokenizer(poem, return_tensors="pt", padding=True, truncation=True, max_length=256)

    # Get predictions
    with torch.no_grad():
        outputs = model(**inputs)
        
    logits = outputs.logits
    predictions = torch.sigmoid(logits) > 0.5  # Convert to binary predictions

    # Interpret results
    fields = ["emotion", "metaphor", "setting", "motion", "prompt"]
    for i, field in enumerate(fields):
        present = "present" if predictions[0][i].item() else "absent"
        print(f"{field}: {present}")