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--- |
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license: mit |
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language: |
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- en |
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metrics: |
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- accuracy |
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pipeline_tag: text-generation |
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widget: |
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- text: "<schema>CREATE TABLE radio(age VARCHAR, radio_id VARCHAR, frequency VARCHAR, wavelength VARCHAR); CREATE TABLE radio_faults(radio_id VARCHAR, fault_description VARCHAR)</schema><question>Get the radio id and defect descriptions of radios that have wavelength greater than 30 ?</question><sql>" |
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example_title: "example1" |
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- text: "<schema>CREATE TABLE system(JobID: String,GID: String, UID: String, Start:Time(yyyy/mm/dd), End: Time,ElapsedRaw: Time, CPUTimeRAW: Time,NCPUS: Number,NNodes: Number, NodeList: List, State:String, Timelimit: Time);</schema><question>Get UID and job id for Jobs that started on Jan 20 , 2023</question><sql>" |
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example_title: "example2" |
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- text: "<schema>CREATE TABLE department (Department_ID number, Name text, Creation text, Ranking number, Budget_in_Billions number, Num_Employees number) which has Department_ID as primary key abd CREATE TABLE head (head_ID number, name text, born_state text, age number) which has head_ID as primary key and CREATE TABLE management (department_ID number, head_ID number, temporary_acting text) which has department_ID as primary key</schema><question>" |
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example_title: "example3" |
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tags: |
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- code |
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- sql |
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- text2sql |
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- instruction_tuned |
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- jax |
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- pytorch |
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- 1b |
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- expert |
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datasets: |
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- PipableAI/spider-bird |
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--- |
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# Pipable’s pipSQL |
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Please refer to https://huggingface.co./PipableAI/pipSQL-1.3b for our state of the art model, that gives better performance than chatgpt and claude on sql tasks on a lot of benchmarks. |
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Pipable’s pipSQL is a model distilled from llama 1b to generate sql queries given prompt and schema. |
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We used a unique pipeline which involved the model working on two objectives alternatively ---- |
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1. Maximizing the log prob of all tokens in the sequence (including the prompt tokens) |
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2. Minimizng the difference between the true value and the predicted maximum value of the output tokens i.e generated tokens for the sql query slice of the entire sequence. |
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## License |
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The model's new weights along with all other assets involved with it are open sourced under mit license. |
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## How to Use |
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```python |
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text = """<schema>{schema}</schema> |
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<question>{question}</question> |
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<sql>""" |
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``` |
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pytorch |
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```python |
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from transformers import AutoModelForCasualLM, AutoTokenizer |
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device = "cuda" |
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model = AutoModelForCausalLM.from_pretrained("PipableAI/pipSQL1b") |
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tokenizer = AutoTokenizer.from_pretrained("PipableAI/pipSQL1b") |
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inputs = tokenizer(text, return_tensors="pt") |
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outputs = model.generate(**inputs, max_new_tokens=200) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True).split('<sql>')[1].split('</sql>')[0]) |
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``` |
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flax |
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```python |
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from transformers import FlaxAutoModelForCasualLM, AutoTokenizer |
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model = FlaxAutoModelForCausalLM.from_pretrained("PipableAI/pipSQL1b" , from_pt=True) |
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tokenizer = AutoTokenizer.from_pretrained("PipableAI/pipSQL1b") |
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``` |
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## The PipableAI team |
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Avi Kothari, Pratham Gupta, Ritvik Aryan Kalra, Rohan Bhatial, Soham Acharya |
