davidmezzetti commited on
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Initial model

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README.md CHANGED
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  license: apache-2.0
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  ---
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+ language: en
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+ widget:
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+ - text: "translate Bash to SQL: find -name \"feel good story\" -mtime -1"
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+ example_title: Last day
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+ - text: "translate Bash to SQL: find -name \"show me sports stories\" -mtime -1 -team \"Red Sox\""
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+ example_title: Last day with filter
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+ - text: "translate Bash to SQL: find -name \"breaking news\" -summary"
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+ example_title: Summary
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+ - text: "translate Bash to SQL: find -name \"breaking news\" -translate fr"
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+ example_title: Translate to French
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+ inference:
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+ parameters:
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+ max_length: 512
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  license: apache-2.0
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  ---
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+
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+ # T5-small finedtuned to generate txtai SQL
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+
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+ [T5 small](https://huggingface.co/t5-small) fine-tuned to generate [txtai](https://github.com/neuml/txtai) SQL. This model takes [Bash](https://en.wikipedia.org/wiki/Bash_(Unix_shell)) like commands and builds txtai-compatible SQL statements.
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+
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+ ```
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+ find -name "feel good story" -mtime -1
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+ find -name "show me sports stories" -mtime -1 -team \"Red Sox\"
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+ find -name "breaking news" -summary
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+ find -name "breaking news" -translate fr
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+ ```
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+
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+ ## Custom query syntax
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+
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+ This model is an example of creating a custom query syntax that can be translated into SQL txtai can understand. Any query syntax can be created. This one supports Bash-like commands but a similar strategy can be deployed to support other languages. Natural language can be translated to functions, query clauses, column selection and more.
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+
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+ See [t5-small-txtsql](https://huggingface.co/NeuML/t5-small-txtsql) for a model that translates natural language statements into txtai SQL.
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+
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+ ## Model training
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+
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+ This model was trained using scripts that can be [found here](https://github.com/neuml/txtai/tree/master/models/bashsql).
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+
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+ Steps to train:
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+
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+ ```bash
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+ python generate.py bashsql.csv
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+ python train.py bashsql.csv t5-small-bashsql
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+ ```
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+ "_name_or_path": "t5-small",
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+ "architectures": [
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+ "T5ForConditionalGeneration"
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+ ],
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+ "d_ff": 2048,
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+ "feed_forward_proj": "relu",
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+ "model_type": "t5",
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+ "n_positions": 512,
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+ "output_past": true,
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+ "length_penalty": 2.0,
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+ "min_length": 30,
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+ "no_repeat_ngram_size": 3,
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+ "num_beams": 4,
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+ "prefix": "summarize: "
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+ },
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+ "translation_en_to_de": {
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+ "early_stopping": true,
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+ "max_length": 300,
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+ "num_beams": 4,
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+ "prefix": "translate English to German: "
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+ },
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+ "translation_en_to_fr": {
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+ "early_stopping": true,
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+ "num_beams": 4,
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+ "prefix": "translate English to French: "
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+ },
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+ "translation_en_to_ro": {
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+ "early_stopping": true,
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+ "max_length": 300,
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+ "num_beams": 4,
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+ "prefix": "translate English to Romanian: "
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+ }
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+ },
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.18.0",
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+ "use_cache": true,
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+ "vocab_size": 32128
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+ }
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