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--- |
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license: mit |
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language: |
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- cs |
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--- |
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# Model Card for mt5-base-multi-label-cs-iiib |
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<!-- Provide a quick summary of what the model is/does. --> |
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This model is fine-tuned for multi-label text classification of Supportive Interactions in Instant Messenger dialogs of Adolescents in Czech. |
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## Model Description |
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The model was fine-tuned on a dataset of Czech Instant Messenger dialogs of Adolescents. The classification is multi-label and the model outputs any combination of the tags:'NO TAG', 'Informační podpora', 'Emocionální podpora', 'Začlenění do skupiny', 'Uznání', 'Nabídka pomoci': as a string joined with ', ' (ordered alphabetically). Each label indicates the presence of that category of Supportive Interactions: 'no tag', 'informational support', 'emocional support', 'social companionship', 'appraisal', 'instrumental support'. The inputs of the model are: a target utterance and its bi-directional context; the label of the example is determined by the label of the target utterance. |
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- **Developed by:** Anonymous |
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- **Language(s):** multilingual |
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- **Finetuned from:** mt5-base |
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## Model Sources |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** https://github.com/chi2024submission |
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- **Paper:** Stay tuned! |
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## Usage |
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Here is how to use this model to classify a context-window of a dialogue: |
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```python |
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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer |
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import torch |
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test_texts = ['Utterance2'] |
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test_text_pairs = ['Utterance1;Utterance2;Utterance3'] |
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checkpoint_path = "chi2024/mt5-base-multi-label-cs-iiib" |
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model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint_path)\ |
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.to("cuda" if torch.cuda.is_available() else "cpu") |
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tokenizer = AutoTokenizer.from_pretrained(checkpoint_path) |
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def verbalize_input(text: str, text_pair: str) -> str: |
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return "Utterance: %s\nContext: %s" % (text, text_pair) |
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def predict_one(text, pair): |
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input_pair = verbalize_input(text, pair) |
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inputs = tokenizer(input_pair, return_tensors="pt", padding=True, |
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truncation=True, max_length=256).to(model.device) |
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outputs = model.generate(**inputs) |
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decoded = [text.split(",")[0].strip() for text in |
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tokenizer.batch_decode(outputs, skip_special_tokens=True)] |
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return decoded |
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dec = predict_one(test_texts[0], test_text_pairs[0]) |
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print(dec) |
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``` |
