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--- |
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widget: |
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- text: "Is this review positive or negative? Review: Best cast iron skillet you will ever buy." |
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example_title: "Sentiment analysis" |
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- text: "Barack Obama nominated Hilary Clinton as his secretary of state on Monday. He chose her because she had ..." |
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example_title: "Coreference resolution" |
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- text: "On a shelf, there are five books: a gray book, a red book, a purple book, a blue book, and a black book ..." |
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example_title: "Logic puzzles" |
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- text: "The two men running to become New York City's next mayor will face off in their first debate Wednesday night ..." |
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example_title: "Reading comprehension" |
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--- |
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# Model Card for Model ID |
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Generate SQL from Natural Language question with a SQL context. |
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## Model Details |
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### Model Description |
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BART from facebook/bart-large-cnn is fintuned on gretelai/synthetic_text_to_sql dataset to generate SQL from NL and SQL context |
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- **Model type:** [BART] |
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- **Language(s) (NLP):** English |
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- **License:** openrail |
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- **Finetuned from model [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct.)** |
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- **Dataset:** [gretelai/synthetic_text_to_sql](https://huggingface.co/datasets/gretelai/synthetic_text_to_sql) |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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### Direct Use |
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
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[More Information Needed] |
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### Downstream Use [optional] |
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> |
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[More Information Needed] |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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[More Information Needed] |
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## Training Details |
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### Training Data |
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<!-- 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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[More Information Needed] |
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### Training Procedure |
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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#### Preprocessing [optional] |
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[More Information Needed] |
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#### Training Hyperparameters |
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
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#### Speeds, Sizes, Times [optional] |
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
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[More Information Needed] |
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## Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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<!-- This should link to a Dataset Card if possible. --> |
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[More Information Needed] |
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#### Factors |
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
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[More Information Needed] |
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#### Metrics |
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<!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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[More Information Needed] |
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### Results |
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[More Information Needed] |
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## Technical Specifications [optional] |
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### Model Architecture and Objective |
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[More Information Needed] |
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### Compute Infrastructure |
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[More Information Needed] |
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#### Hardware |
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[More Information Needed] |
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## Citation |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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@software{gretel-synthetic-text-to-sql-2024, |
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author = {Meyer, Yev and Emadi, Marjan and Nathawani, Dhruv and Ramaswamy, Lipika and Boyd, Kendrick and Van Segbroeck, Maarten and Grossman, Matthew and Mlocek, Piotr and Newberry, Drew}, |
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title = {{Synthetic-Text-To-SQL}: A synthetic dataset for training language models to generate SQL queries from natural language prompts}, |
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month = {April}, |
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year = {2024}, |
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url = {https://huggingface.co/datasets/gretelai/synthetic-text-to-sql} |
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} |
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@article{DBLP:journals/corr/abs-1910-13461, |
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author = {Mike Lewis and |
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Yinhan Liu and |
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Naman Goyal and |
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Marjan Ghazvininejad and |
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Abdelrahman Mohamed and |
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Omer Levy and |
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Veselin Stoyanov and |
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Luke Zettlemoyer}, |
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title = {{BART:} Denoising Sequence-to-Sequence Pre-training for Natural Language |
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Generation, Translation, and Comprehension}, |
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journal = {CoRR}, |
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volume = {abs/1910.13461}, |
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year = {2019}, |
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url = {http://arxiv.org/abs/1910.13461}, |
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eprinttype = {arXiv}, |
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eprint = {1910.13461}, |
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timestamp = {Thu, 31 Oct 2019 14:02:26 +0100}, |
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biburl = {https://dblp.org/rec/journals/corr/abs-1910-13461.bib}, |
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bibsource = {dblp computer science bibliography, https://dblp.org} |
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} |
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## Model Card Authors [optional] |
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[Swastik Maiti] |
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