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-binary-en-iiia-02c
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+This model is fine-tuned for binary text classification of Supportive Interactions in Instant Messenger dialogs of Adolescents. 
+
+## Model Description
+
+The model was fine-tuned on a dataset of English Instant Messenger dialogs of Adolescents. The classification is binary and the model outputs 'positive' or 'negative': Supportive Interactions present or not. The inputs are a target utterance and its bi-directional context; it's target label that of the target utterance.
+
+- **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
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+import torch
+
+# Target utterance
+test_texts = ['Utterance2']
+# Bi-directional context of the target utterance
+test_text_pairs = ['Utterance1;Utterance2;Utterance3']
+
+# Load the model and tokenizer
+checkpoint_path = "chi2024/mt5-base-binary-en-iiia-02c"
+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 verbalize_input(text: str, text_pair: str) -> str:
+    return "Utterance: %s\nContext: %s" % (text, text_pair)
+
+def predict_one(text, pair):
+    input_pair = verbalize_input(text, pair)
+    inputs = tokenizer(input_pair, return_tensors="pt", padding=True,
+                       truncation=True, max_length=256).to(model.device)
+    outputs = model.generate(**inputs)
+    decoded = [text.strip() for text in
+               tokenizer.batch_decode(outputs, skip_special_tokens=True)]
+    return decoded
+
+# Run the prediction
+preds_txt = [predict_one(t,p) for t,p in zip(test_texts, test_text_pairs)]
+preds_lbl = [1 if x == 'positive' else 0 for x in preds_txt]
+print(preds_lbl)
+```