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:
+- cs
+---
+# Model Card for small-e-czech-2stage-supportive-interactions-cs
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+This model is fine-tuned for 2nd stage multi-label text classification of Supportive Interactions in Instant Messenger dialogs of Adolescents - it expects inputs where at least one of the classes appears. 
+
+## Model Description
+
+The model was fine-tuned on a dataset of Instant Messenger dialogs of Adolescents. The classification is 2stage and the model outputs probablities for labels {0,1,2,3,4}: 
+
+0. Informational Support
+1. Emotional Support
+2. Social Companionship
+3. Appraisal
+4. Instrumental Support
+
+- **Developed by:** Anonymous
+- **Language(s):** cs
+- **Finetuned from:** small-e-czech
+
+## Model Sources
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** https://github.com/justtherightsize/supportive-interactions-and-risks
+- **Paper:** Stay tuned!
+
+## Usage
+Here is how to use this model to classify a context-window of a dialogue:
+
+```python
+import numpy as np
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+# Prepare input texts. This model is pretrained on multi-lingual data 
+# and fine-tuned on English
+test_texts = ['Utterance1;Utterance2;Utterance3']
+
+# Load the model and tokenizer
+model = AutoModelForSequenceClassification.from_pretrained(
+    'justtherightsize/small-e-czech-2stage-supportive-interactions-cs', num_labels=5).to("cuda")
+
+tokenizer = AutoTokenizer.from_pretrained(
+    'justtherightsize/small-e-czech-2stage-supportive-interactions-cs',
+    use_fast=False, truncation_side='left')
+assert tokenizer.truncation_side == 'left'
+
+# Define helper functions
+def predict_one(text: str, tok, mod, threshold=0.5):
+    encoding = tok(text, return_tensors="pt", truncation=True, padding=True,
+                   max_length=256)
+    encoding = {k: v.to(mod.device) for k, v in encoding.items()}
+    outputs = mod(**encoding)
+    logits = outputs.logits
+    sigmoid = torch.nn.Sigmoid()
+    probs = sigmoid(logits.squeeze().cpu())
+    predictions = np.zeros(probs.shape)
+    predictions[np.where(probs >= threshold)] = 1
+    return predictions, probs
+
+def print_predictions(texts):
+    preds = [predict_one(tt, tokenizer, model) for tt in texts]
+    for c, p in preds:
+        print(f'{c}: {p.tolist():.4f}')
+
+# Run the prediction
+print_predictions(test_texts)
+```