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README.md ADDED
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+ ---
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+ license: bsd-3-clause
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+ inference:
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+ parameters:
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+ do_sample: false
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+ max_length: 200
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+ widget:
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+ - text: "CREATE TABLE stadium (\n stadium_id number,\n location text,\n name text,\n capacity number,\n)\n\n-- Using valid SQLite, answer the following questions for the tables provided above.\n\n-- how many stadiums in total?\n\nSELECT"
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+ example_title: "Number stadiums"
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+ - text: "CREATE TABLE work_orders ( ID NUMBER, CREATED_AT TEXT, COST FLOAT, INVOICE_AMOUNT FLOAT, IS_DUE BOOLEAN, IS_OPEN BOOLEAN, IS_OVERDUE BOOLEAN, COUNTRY_NAME TEXT, )\n\n-- Using valid SQLite, answer the following questions for the tables provided above.\n\n-- how many work orders are open?\n\nSELECT"
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+ example_title: "Open work orders"
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+ - text: "CREATE TABLE stadium ( stadium_id number, location text, name text, capacity number, highest number, lowest number, average number )\n\nCREATE TABLE singer ( singer_id number, name text, country text, song_name text, song_release_year text, age number, is_male others )\n\nCREATE TABLE concert ( concert_id number, concert_name text, theme text, stadium_id text, year text )\n\nCREATE TABLE singer_in_concert ( concert_id number, singer_id text )\n\n-- Using valid SQLite, answer the following questions for the tables provided above.\n\n-- What is the maximum, the average, and the minimum capacity of stadiums ?\n\nSELECT"
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+ example_title: "Stadium capacity"
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+ ---
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+
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+ # NSQL (NSQL-6B)
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+
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+ ## Model Description
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+
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+ NSQL is a family of autoregressive open-source large foundation models (FMs) designed specifically for SQL generation tasks.
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+
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+ The checkpoint included in this repository is based on [CodeGen-Multi 6B](https://huggingface.co/Salesforce/codegen-6B-multi) from Salesforce and further pre-trained on a dataset of general SQL queries and then fine-tuned on a dataset composed of text-to-SQL pairs.
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+
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+ ## Training Data
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+
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+ The general SQL queries are the SQL subset from [The Stack](https://huggingface.co/datasets/bigcode/the-stack), containing 1M training samples. The labeled text-to-SQL pairs come from more than 20 public sources across the web from standard datasets. We hold out Spider and GeoQuery datasets for use in evaluation.
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+
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+ ## Evaluation Data
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+
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+ We evaluate our models on two text-to-SQL benchmarks: Spider and GeoQuery.
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+
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+ ## Training Procedure
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+
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+ NSQL was trained using cross-entropy loss to maximize the likelihood of sequential inputs. For finetuning on text-to-SQL pairs, we only compute the loss over the SQL portion of the pair. The family of models is trained using 80GB A100s, leveraging data and model parallelism. We pre-trained for 3 epochs and fine-tuned for 10 epochs.
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+
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+ ## Intended Use and Limitations
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+
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+ The model was designed for text-to-SQL generation tasks from given table schema and natural language prompts. The model works best with the prompt format defined below and outputting `SELECT` queries.
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+
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+ ## How to Use
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+
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+ Example 1:
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+
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+ ```python
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+ from transformers import AutoTokenizer, AutoModelForCausalLM
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+ tokenizer = AutoTokenizer.from_pretrained("NumbersStation/nsql-6B")
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+ model = AutoModelForCausalLM.from_pretrained("NumbersStation/nsql-6B")
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+
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+ text = """CREATE TABLE stadium (
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+ stadium_id number,
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+ location text,
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+ name text,
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+ capacity number,
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+ highest number,
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+ lowest number,
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+ average number
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+ )
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+
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+ CREATE TABLE singer (
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+ singer_id number,
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+ name text,
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+ country text,
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+ song_name text,
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+ song_release_year text,
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+ age number,
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+ is_male others
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+ )
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+
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+ CREATE TABLE concert (
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+ concert_id number,
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+ concert_name text,
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+ theme text,
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+ stadium_id text,
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+ year text
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+ )
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+
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+ CREATE TABLE singer_in_concert (
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+ concert_id number,
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+ singer_id text
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+ )
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+
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+ -- Using valid SQLite, answer the following questions for the tables provided above.
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+
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+ -- What is the maximum, the average, and the minimum capacity of stadiums ?
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+
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+ SELECT"""
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+
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+ input_ids = tokenizer(text, return_tensors="pt").input_ids
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+
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+ generated_ids = model.generate(input_ids, max_length=500)
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+ print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
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+ ```
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+
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+ Example 2:
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+
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+ ```python
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+ from transformers import AutoTokenizer, AutoModelForCausalLM
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+ tokenizer = AutoTokenizer.from_pretrained("NumbersStation/nsql-6B")
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+ model = AutoModelForCausalLM.from_pretrained("NumbersStation/nsql-6B")
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+
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+ text = """CREATE TABLE stadium (
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+ stadium_id number,
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+ location text,
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+ name text,
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+ capacity number,
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+ )
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+
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+ -- Using valid SQLite, answer the following questions for the tables provided above.
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+
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+ -- how many stadiums in total?
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+
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+ SELECT"""
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+
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+ input_ids = tokenizer(text, return_tensors="pt").input_ids
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+
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+ generated_ids = model.generate(input_ids, max_length=500)
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+ print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
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+ ```
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+
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+ Example 3:
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+
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+ ```python
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+ from transformers import AutoTokenizer, AutoModelForCausalLM
124
+ tokenizer = AutoTokenizer.from_pretrained("NumbersStation/nsql-6B")
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+ model = AutoModelForCausalLM.from_pretrained("NumbersStation/nsql-6B")
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+
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+ text = """CREATE TABLE work_orders (
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+ ID NUMBER,
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+ CREATED_AT TEXT,
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+ COST FLOAT,
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+ INVOICE_AMOUNT FLOAT,
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+ IS_DUE BOOLEAN,
133
+ IS_OPEN BOOLEAN,
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+ IS_OVERDUE BOOLEAN,
135
+ COUNTRY_NAME TEXT,
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+ )
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+
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+ -- Using valid SQLite, answer the following questions for the tables provided above.
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+
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+ -- how many work orders are open?
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+
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+ SELECT"""
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+
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+ input_ids = tokenizer(text, return_tensors="pt").input_ids
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+
146
+ generated_ids = model.generate(input_ids, max_length=500)
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+ print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
148
+ ```
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+
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+ For more information (e.g., run with your local database), please find examples in [this repository](https://github.com/NumbersStationAI/NSQL).
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tokenizer.json ADDED
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tokenizer_config.json ADDED
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vocab.json ADDED
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