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--- |
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license: other |
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library_name: transformers |
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tags: |
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- trl |
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- sft |
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- generated_from_trainer |
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base_model: google/gemma-2b |
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model-index: |
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- name: gemma-2b_text_to_sql |
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results: [] |
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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: >- |
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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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-- Using valid SQLite, answer the following questions for the tables |
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provided above. |
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-- how many stadiums in total? |
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SELECT |
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example_title: Number stadiums |
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- text: >- |
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CREATE TABLE work_orders ( ID NUMBER, CREATED_AT TEXT, COST FLOAT, |
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INVOICE_AMOUNT FLOAT, IS_DUE BOOLEAN, IS_OPEN BOOLEAN, IS_OVERDUE BOOLEAN, |
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COUNTRY_NAME TEXT, ) |
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-- Using valid SQLite, answer the following questions for the tables |
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provided above. |
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-- how many work orders are open? |
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SELECT |
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example_title: Open work orders |
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- text: >- |
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CREATE TABLE stadium ( stadium_id number, location text, name text, capacity |
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number, highest number, lowest number, average number ) |
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CREATE TABLE singer ( singer_id number, name text, country text, song_name |
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text, song_release_year text, age number, is_male others ) |
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CREATE TABLE concert ( concert_id number, concert_name text, theme text, |
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stadium_id text, year text ) |
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CREATE TABLE singer_in_concert ( concert_id number, singer_id text ) |
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-- Using valid SQLite, answer the following questions for the tables |
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provided above. |
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-- What is the maximum, the average, and the minimum capacity of stadiums ? |
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SELECT |
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example_title: Stadium capacity |
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pipeline_tag: text2text-generation |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# text_to_sql |
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This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on an unknown dataset. |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0002 |
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- train_batch_size: 4 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 16 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 2 |
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- training_steps: 25 |
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- mixed_precision_training: Native AMP |
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### Training results |
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### Framework versions |
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- PEFT 0.8.2 |
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- Transformers 4.38.0 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.17.0 |
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- Tokenizers 0.15.2 |
