Update README.md

README.md

@@ -1,103 +1,103 @@
----
-library_name: transformers
-tags:
-- text-to-SQL
-- SQL
-- code-generation
-- NLQ-to-SQL
-- text2SQL
-- Security
-- Vulnerability detection
-datasets:
-- salmane11/SQLShield
-language:
-- en
-base_model:
-- microsoft/codebert-base
----
-
-# SQLQueryShield
-
-## Model Description
-
-SQLQueryShield is a vulnerable SQL query detection model. It classifies SQL queries as either vulnerable (e.g., prone to SQL injection or unsafe execution) or benign (safe to execute).
-
-The checkpoint included in this repository is based on [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) and further finetuned on [SQLShield](https://huggingface.co/datasets/salmane11/SQLShield), a dataset dedicated to text-to-SQL vulnerability detection composed of vulnerable and safe NLQs and their related SQL queries.
-
-
-## Finetuning Procedure
-The model was fine-tuned using the Hugging Face Transformers library. The following steps were used:
-
-1. Dataset: SSQLShield, only the SQL queries from the (NLQ, SQL) pairs were used for training.
-
-2. Preprocessing:
-
-    - Input Format: Raw SQL query strings.
-
-    - Tokenization: Tokenized using microsoft/codebert-base.
-
-    - Max Length: 128 tokens.
-
-    - Padding and truncation applied.
-
-## Intended Use and Limitations
-
-SQLQueryShield is intended for use as a post-generation filter or analysis tool in any system that executes or generates SQL queries. Its main role is to detect whether a SQL query is potentially harmful due to vulnerability patterns such as SQL injection, improper string concatenation, or unsafe expressions.
-
-Ideal use cases:
-
-    - Filtering SQL queries in Text-to-SQL applications
-
-    - Post-processing or validating user-generated SQL before execution
-
-
-## How to Use
-
-Example 1: Malicious
-
-```python
-from transformers import pipeline
-
-sql_query_shield = pipeline("text-classification", model="salmane11/SQLQueryShield")
-
-# For the following Table schema
-# CREATE TABLE campuses
-#   (
-#      campus   VARCHAR,
-#      location VARCHAR
-#   )
-
-query = "SELECT campus FROM campuses WHERE location = '' UNION SELECT database() --"
-
-prediction = sql_query_shield(query)
-print(prediction)
-#{label:"MALICIOUS", probaility:0.9}
-```
-
-
-Example 2: Safe
-
-```python
-from transformers import pipeline
-
-sql_query_shield = pipeline("text-classification", model="salmane11/SQLQueryShield")
-
-# For the following Table schema
-# CREATE TABLE tv_channel
-#   (
-#      package_option VARCHAR,
-#      series_name    VARCHAR
-#   ) 
-
-query = "SELECT package_option FROM tv_channel WHERE series_name = 'Sky Radio'"
-
-
-prediction = sql_query_shield(query)
-print(prediction)
-#{label:"SAFE", probaility:0.99}
-```
-
-
-## Cite our work
-
+---
+library_name: transformers
+tags:
+- text-to-SQL
+- SQL
+- code-generation
+- NLQ-to-SQL
+- text2SQL
+- Security
+- Vulnerability detection
+datasets:
+- salmane11/SQLShield
+language:
+- en
+base_model:
+- microsoft/codebert-base
+---
+
+# SQLQueryShield
+
+## Model Description
+
+SQLQueryShield is a vulnerable SQL query detection model. It classifies SQL queries as either vulnerable (e.g., prone to SQL injection or unsafe execution) or benign (safe to execute).
+
+The checkpoint included in this repository is based on [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) and further finetuned on [SQLShield](https://huggingface.co/datasets/salmane11/SQLShield), a dataset dedicated to text-to-SQL vulnerability detection composed of vulnerable and safe NLQs and their related SQL queries.
+
+
+## Finetuning Procedure
+The model was fine-tuned using the Hugging Face Transformers library. The following steps were used:
+
+1. Dataset: SSQLShield, only the SQL queries from the (NLQ, SQL) pairs were used for training.
+
+2. Preprocessing:
+
+    - Input Format: Raw SQL query strings.
+
+    - Tokenization: Tokenized using microsoft/codebert-base.
+
+    - Max Length: 128 tokens.
+
+    - Padding and truncation applied.
+
+## Intended Use and Limitations
+
+SQLQueryShield is intended for use as a post-generation filter or analysis tool in any system that executes or generates SQL queries. Its main role is to detect whether a SQL query is potentially harmful due to vulnerability patterns such as SQL injection, improper string concatenation, or unsafe expressions.
+
+Ideal use cases:
+
+    - Filtering SQL queries in Text-to-SQL applications
+
+    - Post-processing or validating user-generated SQL before execution
+
+
+## How to Use
+
+Example 1: Malicious
+
+```python
+from transformers import pipeline
+
+sql_query_shield = pipeline("text-classification", model="salmane11/SQLQueryShield")
+
+# For the following Table schema
+# CREATE TABLE campuses
+#   (
+#      campus   VARCHAR,
+#      location VARCHAR
+#   )
+
+query = "SELECT campus FROM campuses WHERE location = '' UNION SELECT database() --"
+
+prediction = sql_query_shield(query)
+print(prediction)
+#[{'label': 'MALICIOUS', 'score': 0.9995294809341431}]
+```
+
+
+Example 2: Safe
+
+```python
+from transformers import pipeline
+
+sql_query_shield = pipeline("text-classification", model="salmane11/SQLQueryShield")
+
+# For the following Table schema
+# CREATE TABLE tv_channel
+#   (
+#      package_option VARCHAR,
+#      series_name    VARCHAR
+#   ) 
+
+query = "SELECT package_option FROM tv_channel WHERE series_name = 'Sky Radio'"
+
+
+prediction = sql_query_shield(query)
+print(prediction)
+#[{'label': 'SAFE', 'score': 0.999503493309021}]
+```
+
+
+## Cite our work
+
 Citation