Update README.md

README.md

@@ -1,100 +1,120 @@
 ---
 library_name: transformers
-tags: []
+tags: [text2sql, sql-generation, t5, natural-language-processing]
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+# Model Card for ThotaBhanu/t5_sql_askdb
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
+This model is a **T5-based Natural Language to SQL** converter, fine-tuned on the **WikiSQL dataset**. It is designed to convert **English natural language queries** into **SQL queries** that can be executed on relational databases.
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+- **Developed by:** Bhanu Prasad Thota  
+- **Funded by [optional]:** Independent  
+- **Shared by [optional]:** Bhanu Prasad Thota  
+- **Model type:** T5-based Sequence-to-Sequence Model  
+- **Language(s):** English  
+- **License:** MIT  
+- **Finetuned from model:** `t5-large`  
 
-- **Developed by:** Bhanu Prasad Thota
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** T5-based Natural Language to SQL Model 
-- **Language(s) (NLP):** English
-- **License:** MIT
-- **Finetuned from model [optional]:** `t5-large`
+This model is particularly useful for **text-to-SQL applications**, allowing users to **query databases using plain English** instead of writing SQL.
 
-This model is trained to convert natural language queries into SQL statements using the **WikiSQL dataset**.
+---
 
-### Model Sources [optional]
+## Model Sources
 
-<!-- Provide the basic links for the model. -->
+- **Repository:** [https://huggingface.co/ThotaBhanu/t5_sql_askdb](https://huggingface.co/ThotaBhanu/t5_sql_askdb)  
+- **Paper [optional]:** N/A  
+- **Demo [optional]:** Coming soon  
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+---
 
 ## 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. -->
-
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
+- Convert **natural language questions** into **SQL queries**  
+- Assist in **database query automation**  
+- Can be used in **chatbots, data analytics tools, and enterprise database search systems**  
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+### Downstream Use
 
-[More Information Needed]
+- Can be **fine-tuned** further on **custom datasets** to improve domain-specific SQL generation  
+- Can be integrated into **business intelligence tools** for better user interaction  
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+- The model does **not infer database schema** automatically  
+- May generate incorrect SQL for **complex nested queries or multi-table joins**  
+- Not suitable for **non-relational (NoSQL) databases**  
 
-[More Information Needed]
+---
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+- The model may not **always generate valid SQL** for **custom database schemas**  
+- Assumes **consistent column naming**, which may not always be the case in enterprise databases  
+- Performance depends on **how well the input query aligns** with the training data format  
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+- Always **validate generated SQL** before executing on a live database  
+- Use **schema-aware** validation methods for production environments  
+- Consider **fine-tuning the model** on domain-specific SQL queries  
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+---
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
+Use the code below to generate SQL queries from natural language:
 
-[More Information Needed]
+```python
+from transformers import T5Tokenizer, T5ForConditionalGeneration
 
-## Training Details
+# Load model and tokenizer
+model_name = "ThotaBhanu/t5_sql_askdb"
+tokenizer = T5Tokenizer.from_pretrained(model_name)
+model = T5ForConditionalGeneration.from_pretrained(model_name)
 
-### Training Data
+# Function to convert query to SQL
+def generate_sql(query):
+    input_text = f"Convert to SQL: {query}"
+    inputs = tokenizer(input_text, return_tensors="pt")
+    output = model.generate(**inputs)
+    return tokenizer.decode(output[0], skip_special_tokens=True)
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+# Example usage
+query = "Find all employees who joined in 2020"
+sql_query = generate_sql(query)
 
-[More Information Needed]
+print(f"📝 Query: {query}")
+print(f"🛠 Generated SQL: {sql_query}")
 
-### Training Procedure
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+## Training Details
 
-#### Preprocessing [optional]
+### Training Data
+
+Dataset: WikiSQL
+Size: 80,654 pairs of natural language questions and SQL queries
+Preprocessing: Tokenization using T5Tokenizer, max length 128
 
-[More Information Needed]
 
+### Training Procedure
+
+Training framework: Hugging Face Transformers + PyTorch
+Hardware used: NVIDIA V100 GPU
+Optimizer: AdamW
+Learning rate: 5e-5
+Batch size: 8
+Epochs: 5
 
 #### Training Hyperparameters
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+Training precision: Mixed precision (fp16)
+Gradient accumulation: Yes (to handle large batch sizes)
 
 #### Speeds, Sizes, Times [optional]
 
@@ -172,7 +192,14 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 ## Citation [optional]
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+@misc{t5_sql_askdb,
+  author = {Bhanu Prasad Thota},
+  title = {T5-SQL AskDB Model},
+  year = {2025},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/ThotaBhanu/t5_sql_askdb}}
+}
+
 
 **BibTeX:**