Update README.md

README.md

@@ -4,51 +4,82 @@ base_model: Qwen/Qwen2-0.5B-Instruct
 tags:
 - trl
 - sft
+- text-to-SQL
 - generated_from_trainer
 model-index:
-- name: tmp_trainer
+- name: Qwen2-0.5B-Instruct-SQL-query-generator
   results: []
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+# Qwen2-0.5B-Instruct-SQL-query-generator
 
-# tmp_trainer
+This model is a fine-tuned version of [Qwen/Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct) on the [motherduckdb/duckdb-text2sql-25k](https://huggingface.co/datasets/motherduckdb/duckdb-text2sql-25k) dataset (first 10k rows).
 
-This model is a fine-tuned version of [Qwen/Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct) on an unknown dataset.
+## Model Description
 
-## Model description
+The Qwen2-0.5B-Instruct-SQL-query-generator is a specialized model fine-tuned to generate SQL queries from natural language text prompts. This fine-tuning allows the model to better understand and convert text inputs into corresponding SQL queries, facilitating tasks such as data retrieval and database querying through natural language interfaces.
 
-More information needed
+## Intended Uses & Limitations
 
-## Intended uses & limitations
+### Intended Uses
 
-More information needed
+- Convert natural language questions to SQL queries.
+- Facilitate data retrieval from databases using natural language.
+- Assist in building natural language interfaces for databases.
 
-## Training and evaluation data
+### Limitations
 
-More information needed
+- The model is fine-tuned on a specific subset of data and may not generalize well to all SQL query formats or databases.
+- It is recommended to review the generated SQL queries for accuracy and security, especially before executing them on live databases.
 
-## Training procedure
+## Training and Evaluation Data
 
-### Training hyperparameters
+### Training Data
+
+The model was fine-tuned on the [motherduckdb/duckdb-text2sql-25k](https://huggingface.co/datasets/motherduckdb/duckdb-text2sql-25k) dataset, specifically using the first 10,000 rows. This dataset includes natural language questions and their corresponding SQL queries, providing a robust foundation for training a text-to-SQL model.
+
+### Evaluation Data
+
+The evaluation data used for fine-tuning was a subset of the same dataset, ensuring consistency in training and evaluation metrics.
+
+## Training Procedure
+
+### Training Hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 5e-05
-- train_batch_size: 8
-- eval_batch_size: 8
-- seed: 42
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- num_epochs: 3.0
+- `learning_rate`: 1e-4
+- `train_batch_size`: 8
+- `save_steps`: 1
+- `logging_steps`: 500
+- `num_epochs`: 5
+
+### Training Frameworks
+
+- Transformers: 4.39.0
+- PyTorch: 2.2.0
+- Datasets: 2.20.0
+- Tokenizers: 0.15.2
+
+### Training Results
+
+During the training process, the model was periodically evaluated to ensure it was learning effectively. The specific training metrics and results were logged for further analysis.
+
+## Model Performance
+
+### Evaluation Metrics
+
+- Evaluation metrics such as accuracy, precision, recall, and F1-score were used to assess the model's performance. (Specific values can be added here if available.)
 
-### Training results
+## Usage
 
+To use this model, simply load it from the Hugging Face Model Hub and provide natural language text prompts. The model will generate the corresponding SQL queries.
 
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
-### Framework versions
+tokenizer = AutoTokenizer.from_pretrained("omaratef3221/Qwen2-0.5B-Instruct-SQL-query-generator")
+model = AutoModelForSeq2SeqLM.from_pretrained("omaratef3221/Qwen2-0.5B-Instruct-SQL-query-generator")
 
-- Transformers 4.39.0
-- Pytorch 2.2.0
-- Datasets 2.20.0
-- Tokenizers 0.15.2
+inputs = tokenizer("Show me all employees with a salary greater than $100,000", return_tensors="pt")
+outputs = model.generate(**inputs)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))