Initial model

README.md

@@ -1,3 +1,44 @@
 ---
+language: en
+widget:
+- text: "translate Bash to SQL: find -name \"feel good story\" -mtime -1"
+  example_title: Last day
+- text: "translate Bash to SQL: find -name \"show me sports stories\" -mtime -1 -team \"Red Sox\""
+  example_title: Last day with filter
+- text: "translate Bash to SQL: find -name \"breaking news\" -summary"
+  example_title: Summary
+- text: "translate Bash to SQL: find -name \"breaking news\" -translate fr"
+  example_title: Translate to French
+inference:
+  parameters:
+    max_length: 512
 license: apache-2.0
 ---
+
+# T5-small finedtuned to generate txtai SQL
+
+[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.
+
+```
+find -name "feel good story" -mtime -1
+find -name "show me sports stories" -mtime -1 -team \"Red Sox\"
+find -name "breaking news" -summary
+find -name "breaking news" -translate fr
+```
+
+## Custom query syntax
+
+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.
+
+See [t5-small-txtsql](https://huggingface.co/NeuML/t5-small-txtsql) for a model that translates natural language statements into txtai SQL.
+
+## Model training
+
+This model was trained using scripts that can be [found here](https://github.com/neuml/txtai/tree/master/models/bashsql).
+
+Steps to train:
+
+```bash
+python generate.py bashsql.csv
+python train.py bashsql.csv t5-small-bashsql
+```

config.json

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+{
+  "_name_or_path": "t5-small",
+  "architectures": [
+    "T5ForConditionalGeneration"
+  ],
+  "d_ff": 2048,
+  "d_kv": 64,
+  "d_model": 512,
+  "decoder_start_token_id": 0,
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "feed_forward_proj": "relu",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "n_positions": 512,
+  "num_decoder_layers": 6,
+  "num_heads": 8,
+  "num_layers": 6,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_max_distance": 128,
+  "relative_attention_num_buckets": 32,
+  "task_specific_params": {
+    "summarization": {
+      "early_stopping": true,
+      "length_penalty": 2.0,
+      "max_length": 200,
+      "min_length": 30,
+      "no_repeat_ngram_size": 3,
+      "num_beams": 4,
+      "prefix": "summarize: "
+    },
+    "translation_en_to_de": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to German: "
+    },
+    "translation_en_to_fr": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to French: "
+    },
+    "translation_en_to_ro": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to Romanian: "
+    }
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.18.0",
+  "use_cache": true,
+  "vocab_size": 32128
+}

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special_tokens_map.json

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tokenizer_config.json

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trainer_state.json

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+}

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