Create README.md

README.md

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+---
+language: fa
+license: mit
+pipeline_tag: text-classification
+---
+
+
+# SentenceFormalityClassifier
+
+This model is fine-tuned to classify text based on formality. It has been fine-tuned on [Mohavere Dataset] (Takalli vahideh, Kalantari, Fateme, Shamsfard, Mehrnoush, Developing an Informal-Formal Persian Corpus, 2022.) using the pretrained model [persian-t5-formality-transfer](https://huggingface.co/HooshvareLab/bert-base-parsbert-uncased).
+
+
+## Evaluation Metrics
+
+**INFORMAL**:
+
+Precision: 0.99
+
+Recall: 0.99
+
+F1-Score: 0.99
+
+
+**FORMAL**:
+
+Precision: 0.99
+
+Recall: 1.0
+
+F1-Score: 0.99
+
+**Accuracy**: 0.99
+
+
+**Macro Avg**:
+
+Precision: 0.99
+
+Recall: 0.99
+
+F1-Score: 0.99
+
+
+**Weighted Avg**:
+
+Precision: 0.99
+
+Recall: 0.99
+
+F1-Score: 0.99
+
+
+## Usage
+
+```python
+
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+
+labels = ["INFORMAL", "FORMAL"]
+
+model = AutoModelForSequenceClassification.from_pretrained('parsi-ai-nlpclass/sentence_formality_classifier')
+tokenizer = AutoTokenizer.from_pretrained('parsi-ai-nlpclass/sentence_formality_classifier')
+
+def test_model(text):
+    inputs = tokenizer(text, return_tensors='pt')
+    outputs = model(**inputs)
+    predicted_label = labels[int(torch.argmax(outputs.logits))]
+    return predicted_label
+
+# Test the model
+text1 = "من فقط می‌خواستم بگویم که چقدر قدردان هستم."
+print("Original:", text1)
+print("Predicted Label:", test_model(text1))
+
+# output: FORMAL
+
+text2 = "آرزویش است او را یک رستوران ببرم."
+print("\nOriginal:", text2)
+print("Predicted Label:", test_model(text2))
+
+# output: FORMAL
+
+text3 = "گل منو اذیت نکنید"
+print("\nOriginal:", text2)
+print("Predicted Label:", test_model(text3))
+
+# output: INFORMAL
+
+text4 = "من این دوربین رو خالم برام کادو خرید"
+print("\nOriginal:", text2)
+print("Predicted Label:", test_model(text3))
+
+# output: INFORMAL
+
+
+
+```