Update README.md

README.md

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----
-license: mit
----
+---
+library_name: sklearn
+tags:
+- text-classification
+- persian
+- offensive-language-detection
+- onnx
+language: fa
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+---
+
+# Offensive Text Classifier for Persian Language
+
+This repository contains an Offensive Text Classifier model trained to detect offensive language in Persian text. The model is built using `scikit-learn` and exported to ONNX format for efficient inference.
+
+## Model Description
+
+This model classifies Persian text into two categories: `Offensive` (label 1) and `Neutral` (label 0). It uses a TfidfVectorizer for text feature extraction combined with a Support Vector Machine (SVM) classifier.
+
+## Dataset
+
+The model was trained on the [ParsOffensive dataset](https://github.com/golnaz76gh/pars-offensive-dataset). This dataset consists of Persian comments labeled as either 'Offensive' or 'Neutral'.
+
+## Preprocessing
+
+The text data underwent the following preprocessing steps:
+- **Normalization**: Using `hazm.Normalizer`.
+- **Lemmatization**: Using `hazm.Lemmatizer`.
+- **Stop-word Removal**: Common Persian stop words were removed.
+- **Label Encoding**: 'Neutral' and 'Offensive' labels were converted to numerical `0` and `1` respectively.
+- **Imbalance Handling**: The ADASYN technique was applied to address class imbalance during training.
+
+## Model Architecture
+
+The final pipeline consists of:
+1.  **`TfidfVectorizer`**: Converts raw text into a matrix of TF-IDF features.
+2.  **`SVC` (Support Vector Classifier)**: A Support Vector Machine classifier with a radial basis function (RBF) kernel.
+
+## Performance
+
+Below are the performance metrics on the test set:
+
+```
+              precision    recall  f1-score   support
+
+           0       0.80      0.96      0.88      1043
+           1       0.91      0.62      0.74       644
+
+    accuracy                           0.83      1687
+   macro avg       0.86      0.79      0.81      1687
+weighted avg       0.84      0.83      0.82      1687
+```
+
+Detailed Metrics:
+-   **Accuracy**: 0.830
+-   **Precision (Offensive)**: 0.909
+-   **Recall (Offensive)**: 0.618
+-   **F1-score (Offensive)**: 0.736
+
+## How to Use
+
+### Load the model
+
+You can load the ONNX model and use it for inference. You will need to apply the same preprocessing steps as during training.
+
+```python
+from hazm import Lemmatizer, Normalizer, stopwords_list
+
+# recreate the preprocessing components (or load them if saved)
+stopwords = stopwords_list()
+lemmatizer = Lemmatizer()
+normalizer = Normalizer()
+
+def clean_sentences(sentence: str) -> str:
+    return " ".join(lemmatizer.lemmatize(word) for word in normalizer.normalize(sentence).split(" ") if word not in stopwords)
+```
+
+## Dependencies
+
+-   `pandas`
+-   `numpy`
+-   `hazm`
+-   `scikit-learn`
+-   `imblearn`
+-   `skl2onnx`
+-   `onnxruntime`
+-   `huggingface_hub`