Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+---
+# ModerationBERT-ML-En
+
+**ModerationBERT-ML-En** is a moderation model based on `bert-base-multilingual-cased`. This model is designed to perform text moderation tasks, specifically categorizing text into 18 different categories. It currently works only with English text.
+
+## Dataset
+
+The model was trained and fine-tuned using the [text-moderation-410K](https://huggingface.co/datasets/ifmain/text-moderation-410K) dataset. This dataset contains a wide variety of text samples labeled with different moderation categories.
+
+## Model Description
+
+ModerationBERT-ML-En uses the BERT architecture to classify text into the following categories:
+- harassment
+- harassment_threatening
+- hate
+- hate_threatening
+- self_harm
+- self_harm_instructions
+- self_harm_intent
+- sexual
+- sexual_minors
+- violence
+- violence_graphic
+- self-harm
+- sexual/minors
+- hate/threatening
+- violence/graphic
+- self-harm/intent
+- self-harm/instructions
+- harassment/threatening
+
+## Training and Fine-Tuning
+
+The model was trained using a 95% subset of the dataset for training and a 5% subset for evaluation. The training was performed in two stages:
+
+1. **Initial Training**: The classifier layer was trained with frozen BERT layers.
+2. **Fine-Tuning**: The top two layers of the BERT model were unfrozen and the entire model was fine-tuned.
+
+## Installation
+
+To use ModerationBERT-ML-En, you will need to install the `transformers` library from Hugging Face and `torch`.
+
+```bash
+pip install transformers torch
+```
+
+## Usage
+
+Here is an example of how to use ModerationBERT-ML-En to predict the moderation categories for a given text:
+
+```python
+import json
+import torch
+from transformers import BertTokenizer, BertForSequenceClassification
+
+# Load the tokenizer and model
+model_name = "ModerationBERT-ML-En"
+tokenizer = BertTokenizer.from_pretrained(model_name)
+model = BertForSequenceClassification.from_pretrained(model_name, num_labels=18)
+
+# Device configuration
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model.to(device)
+
+def predict(text, model, tokenizer):
+    encoding = tokenizer.encode_plus(
+        text,
+        add_special_tokens=True,
+        max_length=128,
+        return_token_type_ids=False,
+        padding='max_length',
+        truncation=True,
+        return_attention_mask=True,
+        return_tensors='pt'
+    )
+    input_ids = encoding['input_ids'].to(device)
+    attention_mask = encoding['attention_mask'].to(device)
+    model.eval()
+    with torch.no_grad():
+        outputs = model(input_ids, attention_mask=attention_mask)
+    predictions = torch.sigmoid(outputs.logits)  # Convert logits to probabilities
+    return predictions
+
+# Example usage
+new_text = "This isn't Twitter: try to comment on the article, and not your current activities."
+predictions = predict(new_text, model, tokenizer)
+
+# Define the categories
+categories = ['harassment', 'harassment_threatening', 'hate', 'hate_threatening', 
+              'self_harm', 'self_harm_instructions', 'self_harm_intent', 'sexual', 
+              'sexual_minors', 'violence', 'violence_graphic', 'self-harm', 
+              'sexual/minors', 'hate/threatening', 'violence/graphic', 
+              'self-harm/intent', 'self-harm/instructions', 'harassment/threatening']
+
+# Convert predictions to a dictionary
+category_scores = {categories[i]: predictions[0][i].item() for i in range(len(categories))}
+
+output = {
+    "text": new_text,
+    "category_scores": category_scores
+}
+
+# Print the result as a JSON with indentation
+print(json.dumps(output, indent=4, ensure_ascii=False))
+```
+
+## Notes
+
+- This model is currently configured to work only with English text.
+- Future updates may include support for additional languages.