Update README.md

README.md

@@ -1,3 +1,136 @@
 ---
 license: apache-2.0
+language:
+- en
+pipeline_tag: zero-shot-classification
 ---
+# Model Card for Model ID
+
+<!-- Based on  https://huggingface.co/t5-small, model generates SQL from text given table list with "CREATE TABLE" statements. 
+This is a very light weigh model and could be used in multiple analytical applications. -->
+
+Based on  [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) (MobileBERT is a thin version of BERT_LARGE, while equipped with bottleneck structures and a carefully designed balance between self-attentions and feed-forward networks). This model detects SQLInjection attacks in the input string (check How To Below). This is a very very light  model (100mb) and can be used for edge computing use cases. Used dataset from [Kaggle](www.kaggle.com) called [SQl_Injection](https://www.kaggle.com/datasets/sajid576/sql-injection-dataset).
+**Please test the model before deploying into any environment**.
+Contact us for more info: [email protected]
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** cssupport ([email protected])
+- **Model type:** Language model
+- **Language(s) (NLP):** English
+- **License:** Apache 2.0
+- **Finetuned from model :** [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased)
+
+### Model Sources 
+
+<!-- Provide the basic links for the model. -->
+
+Please refer [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) for Model Sources.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+```python
+import torch
+from transformers import MobileBertTokenizer, MobileBertForSequenceClassification
+
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+tokenizer = MobileBertTokenizer.from_pretrained('google/mobilebert-uncased')
+model = MobileBertForSequenceClassification.from_pretrained('cssupport/mobilebert-sql-injection-detect')
+model.to(device)
+model.eval()
+
+def predict(text):
+    inputs = tokenizer(text, padding=False, truncation=True, return_tensors='pt', max_length=512)
+    input_ids = inputs['input_ids'].to(device)
+    attention_mask = inputs['attention_mask'].to(device)
+
+    with torch.no_grad():
+        outputs = model(input_ids=input_ids, attention_mask=attention_mask)
+
+    logits = outputs.logits
+    probabilities = torch.softmax(logits, dim=1)
+    predicted_class = torch.argmax(probabilities, dim=1).item()
+    return predicted_class, probabilities[0][predicted_class].item()
+
+
+#text = "SELECT * FROM users WHERE username = 'admin' AND password = 'password';"
+#text = "select * from users where username = 'admin' and password = 'password';"
+#text = "SELECT * from USERS where id  =  '1' or @ @1  =  1 union select 1,version  (    )   -- 1'"
+#text = "select * from data where id  =  '1'  or @"
+text ="select * from users where id  =  1 or 1#\"?  =  1 or 1  =  1 -- 1"
+predicted_class, confidence = predict(text)
+
+if predicted_class > 0.7:
+    print("Prediction: SQL Injection Detected")
+else:
+    print("Prediction: No SQL Injection Detected")
+    
+print(f"Confidence: {confidence:.2f}")
+# OUTPUT
+# Prediction: SQL Injection Detected
+# Confidence: 1.00
+```
+
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+[More Information Needed]
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+Could used in application where natural language is to be converted into SQL queries. 
+[More Information Needed]
+
+
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+
+
+## Technical Specifications 
+
+### Model Architecture and Objective
+
+[google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased)
+
+### Compute Infrastructure
+
+
+
+#### Hardware
+
+one P6000 GPU
+
+#### Software
+
+Pytorch and HuggingFace 
+