Transformers
Safetensors
English
bart
text2text-generation
natural language
SQL
text2sql
nl2sql
Eval Results (legacy)
Instructions to use SwastikM/bart-large-nl2sql with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SwastikM/bart-large-nl2sql with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("SwastikM/bart-large-nl2sql") model = AutoModelForSeq2SeqLM.from_pretrained("SwastikM/bart-large-nl2sql") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -103,7 +103,7 @@ the 'diversification' table have a higher budget than the average budget for all
|
|
| 103 |
sql_context: CREATE TABLE diversification (id INT, effort VARCHAR(50), budget FLOAT); CREATE TABLE
|
| 104 |
budget (diversification_id INT, diversification_effort VARCHAR(50), amount FLOAT);"""
|
| 105 |
|
| 106 |
-
sql = sql_generator(
|
| 107 |
|
| 108 |
print(sql)
|
| 109 |
```
|
|
|
|
| 103 |
sql_context: CREATE TABLE diversification (id INT, effort VARCHAR(50), budget FLOAT); CREATE TABLE
|
| 104 |
budget (diversification_id INT, diversification_effort VARCHAR(50), amount FLOAT);"""
|
| 105 |
|
| 106 |
+
sql = sql_generator(query_question_with_context)[0]['generated_text']
|
| 107 |
|
| 108 |
print(sql)
|
| 109 |
```
|