Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- en
+pipeline_tag: text-classification
+---
+
+## Model Description
+This model is IBM's 12-layer toxicity binary classifier for English, intended to be used as a guardrail for any large language model. It has been trained on several benchmark datasets in English, specifically for detecting hateful, abusive, profane and other toxic content in plain text. 
+
+
+## Model Usage
+```python
+# Example of how to use the model
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+model_name_or_path = 'ibm-granite/granite-guardian-hap-125m'
+model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path)
+tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
+model.to(device)
+
+# Sample text
+text = ["This is the 1st test", "This is the 2nd test"]
+input = tokenizer(text, padding=True, truncation=True, return_tensors="pt").to(device)
+
+with torch.no_grad():
+    logits = model(**input).logits
+    prediction = torch.argmax(logits, dim=1).cpu().detach().numpy().tolist() # Binary prediction where label 1 indicates toxicity.
+    probability = torch.softmax(logits, dim=1).cpu().detach().numpy()[:,1].tolist() #  Probability of toxicity.
+   
+```
+
+## Performance Comparison with Other Models
+This model demonstrates superior average performance in comparison with other models on eight mainstream toxicity benchmarks. If a very fast model is required, please refer to the lightweight 4-layer IBM model, granite-guardian-hap-38m.
+
+![Description of Image](125m_comparison_a.png)
+![Description of Image](125m_comparison_b.png)