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rts_t5-small_sql

@@ -33,149 +33,77 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 ## 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]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-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.
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-## Training Details
-### Training Data
-<!-- 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. -->
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-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
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-#### 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 -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
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-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-### Results
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-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
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-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
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-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
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-### Compute Infrastructure
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-#### Hardware
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-#### Software
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-**BibTeX:**
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-## More Information [optional]
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-## Model Card Authors [optional]
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 ## Uses
+Example prompt and response:
+```
+INPUT PROMPT:
+Tables:
+CREATE TABLE employees (
+    EMPLOYEE_ID decimal(6,0),
+    FIRST_NAME varchar(20),
+    LAST_NAME varchar(25),
+    EMAIL varchar(25),
+    PHONE_NUMBER varchar(20),
+    HIRE_DATE date,
+    JOB_ID varchar(10),
+    SALARY decimal(8,2),
+    COMMISSION_PCT decimal(2,2),
+    MANAGER_ID decimal(6,0),
+    DEPARTMENT_ID decimal(4,0)
+)
+CREATE TABLE jobs (
+    JOB_ID varchar(10),
+    JOB_TITLE varchar(35),
+    MIN_SALARY decimal(6,0),
+    MAX_SALARY decimal(6,0)
+)
+CREATE TABLE locations (
+    LOCATION_ID decimal(4,0),
+    STREET_ADDRESS varchar(40),
+    POSTAL_CODE varchar(12),
+    CITY varchar(30),
+    STATE_PROVINCE varchar(25),
+    COUNTRY_ID varchar(2)
+)
+CREATE TABLE countries (
+    COUNTRY_ID varchar(2),
+    COUNTRY_NAME varchar(40),
+    REGION_ID decimal(10,0)
+)
+CREATE TABLE job_history (
+    EMPLOYEE_ID decimal(6,0),
+    START_DATE date,
+    END_DATE date,
+    JOB_ID varchar(10),
+    DEPARTMENT_ID decimal(4,0)
+)
+CREATE TABLE regions (
+    REGION_ID decimal(5,0),
+    REGION_NAME varchar(25)
+)
+CREATE TABLE departments (
+    DEPARTMENT_ID decimal(4,0),
+    DEPARTMENT_NAME varchar(30),
+    MANAGER_ID decimal(6,0),
+    LOCATION_ID decimal(4,0)
+)
+Question:
+For those employees who did not have any job in the past, give me the comparison about the amount of job_id over the job_id , and group by attribute job_id, and list from low to high by the JOB_ID please.
+Answer:
+---------------------------------------------------------------------------------------------------
+BASELINE ANSWER:
+SELECT JOB_ID, COUNT(JOB_ID) FROM employees WHERE NOT EMPLOYEE_ID IN (SELECT EMPLOYEE_ID FROM job_history) GROUP BY JOB_ID ORDER BY JOB_ID
+---------------------------------------------------------------------------------------------------
+MODEL RESPONSE:
+SELECT JOB_ID, COUNT(JOB_ID) FROM employees WHERE NOT EMPLOYEE_ID IN (SELECT EMPLOYEE_ID FROM job_history) ORDER BY JOB_ID DESC
+```