Upload README.md

README.md

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+---
+# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
+# Doc / guide: https://huggingface.co/docs/hub/model-cards
+license: mit
+language:
+- multilingual
+---
+# Model Card for mt5-base-multi-label-all-cs-iv
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+This model is fine-tuned for multi-label seq2seq text classification of Supportive Interactions in Instant Messenger dialogs of Adolescents. 
+
+## Model Description
+
+The model was fine-tuned on a dataset of Czech Instant Messenger dialogs of Adolescents. The classification is multi-label. For each of the utterances in the input, 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' in each of the utterances of the input. The inputs of the model is a sequence of utterances joined with ';'. The outputs are a sequence of per-utterance labels such as: 'NO TAG; Informační podpora, Uznání; NO TAG'
+
+- **Developed by:** Anonymous
+- **Language(s):** multilingual
+- **Finetuned from:** mt5-base
+
+## Model Sources
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** https://github.com/chi2024submission
+- **Paper:** Stay tuned!
+
+## Usage
+Here is how to use this model to classify a context-window of a dialogue:
+
+```python
+import itertools
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+import torch
+
+# Target dialog context window
+test_texts = ['Utterance1;Utterance2;Utterance3']
+
+# Load the model and tokenizer
+checkpoint_path = "chi2024/mt5-base-multi-label-all-cs-iv"
+model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint_path)\
+    .to("cuda" if torch.cuda.is_available() else "cpu")
+tokenizer = AutoTokenizer.from_pretrained(checkpoint_path)
+
+# Define helper functions
+def predict_one(text):
+    inputs = tokenizer(text, return_tensors="pt", padding=True,
+                       truncation=True, max_length=256).to(model.device)
+    outputs = model.generate(**inputs)
+    decoded = [text.split(",")[0].strip() for text in
+               tokenizer.batch_decode(outputs, skip_special_tokens=True)]
+    predicted_sequence = list(
+        itertools.chain(*(pred_one.split("; ") for pred_one in decoded)))
+    return predicted_sequence
+
+# Run the prediction
+dec = predict_one(test_texts[0])
+print(dec)
+```