---
datasets:
- spider
language:
- en
---

# T5 large LM Adapt for Text to SQL

This model is fine-tuned from the [t5-large-LM-adapt](https://huggingface.co/google/t5-large-lm-adapt) checkpoint.

## Spider and Spider-Syn dataset

The model was fine-tuned on the training splits of [Spider](https://yale-lily.github.io/spider) and [Spider-Syn](https://github.com/ygan/Spider-Syn/tree/main/Spider-Syn) datasets. Instead of using only the questions, we added the database schema to the question, as we wanted the model to generate a question over a given database

_input_:

```
Question:  What is the average, minimum, and maximum age for all French musicians?
Schema: "stadium" "Stadium_ID" int , "Location" text , "Name" text , "Capacity" int , "Highest" int , "Lowest" int , "Average" int , foreign_key:  primary key: "Stadium_ID" [SEP] "singer" "Singer_ID" int , "Name" text , "Country" text , "Song_Name" text , "Song_release_year" text , "Age" int , "Is_male" bool , foreign_key:  primary key: "Singer_ID" [SEP] "concert" "concert_ID" int , "concert_Name" text , "Theme" text , "Year" text , foreign_key: "Stadium_ID" text from "stadium" "Stadium_ID" , primary key: "concert_ID" [SEP] "singer_in_concert"  foreign_key: "concert_ID" int from "concert" "concert_ID" , "Singer_ID" text from "singer" "Singer_ID" , primary key: "concert_ID" "Singer_ID"
```

=> _target_:

```
SELECT avg(age), min(age), max(age) FROM singer WHERE country = 'France'
```

When evaluating we query the sqlite database
=> _query result_:

```
[[34.5, 25, 43]]
```

## Format of the database schema

The standardized database schema the model was trained on:

```
table_name column1_name column1_type column2_name column2_type ... foreign_key: FK_name FK_type from table_name column_name primary key: column_name [SEP]
table_name2 ...
```

## Usage

Here is how to use this model to answer the question on a given context using 🤗 Transformers in PyTorch:

```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model_path = 'gaussalgo/T5-LM-Large-text2sql-spider'
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)

question = "What is the average, minimum, and maximum age for all French musicians?"
schema = ""stadium" "Stadium_ID" int , "Location" text , "Name" text , "Capacity" int , "Highest" int , "Lowest" int , "Average" int , foreign_key:  primary key: "Stadium_ID" [SEP] "singer" "Singer_ID" int , "Name" text , "Country" text , "Song_Name" text , "Song_release_year" text , "Age" int , "Is_male" bool , foreign_key:  primary key: "Singer_ID" [SEP] "concert" "concert_ID" int , "concert_Name" text , "Theme" text , "Year" text , foreign_key: "Stadium_ID" text from "stadium" "Stadium_ID" , primary key: "concert_ID" [SEP] "singer_in_concert"  foreign_key: "concert_ID" int from "concert" "concert_ID" , "Singer_ID" text from "singer" "Singer_ID" , primary key: "concert_ID" "Singer_ID""

input_text = " ".join(["Question: ",question, "Schema:", schema])

model_inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**model_inputs, max_length=512)

output_text = tokenizer.decode(outputs, skip_special_tokens=True)

print("SQL Query:")
print(output_text)

```

## Training

The model has been trained using [Adaptor library](https://github.com/gaussalgo/adaptor) 0.2.1, on training splits of Spider and Spider-syn datasets with the following parameters:

```python
training_arguments = AdaptationArguments(output_dir="train_dir",
                                         learning_rate=5e-5,
                                         stopping_strategy=StoppingStrategy.ALL_OBJECTIVES_CONVERGED,
                                         stopping_patience=8,
                                         save_total_limit=8,
                                         do_train=True,
                                         do_eval=True,
                                         bf16=True,
                                         warmup_steps=1000,
                                         gradient_accumulation_steps=8,
                                         logging_steps=10,
                                         eval_steps=200,
                                         save_steps=1000,
                                         num_train_epochs=10,
                                         evaluation_strategy="steps")

```