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LarkAI
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codet5p-770m_nl2sql_oig

Text Generation
Transformers
PyTorch
English
t5
text2text-generation
nl2sql
text-generation-inference
Model card Files
xet
Community
2
YAML Metadata Warning: The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

How to Use 

import torch
from transformers import T5ForConditionalGeneration, AutoTokenizer

device = torch.device("cuda:0")

tokenizer = AutoTokenizer.from_pretrained("LarkAI/codet5p-770m_nl2sql_oig")
model = T5ForConditionalGeneration.from_pretrained("LarkAI/codet5p-770m_nl2sql_oig").to(device)

text = "Given the following schema:\ntrack (Track_ID, Name, Location, Seating, Year_Opened)\nrace (Race_ID, Name, Class, Date, Track_ID)\nWrite a SQL query to count the number of tracks."
inputs = tokenizer.encode(text, return_tensors="pt").to(device)
output_ids = model.generate(inputs, max_length=512)
response_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
# SELECT COUNT( * ) FROM track

How to Train

Dataset: 

{
    "text":"<human>: Given the following schema:\nlocation (restaurant_id, house_number, street_name, city_name)\nrestaurant (id, name, food_type, city_name, rating)\ngeographic (city_name, county, region)\nWrite a SQL query to give me some good arabic -s on buchanan in san francisco ?\n<bot>: SELECT location.house_number , restaurant.name FROM location , restaurant WHERE location.city_name = \"san francisco\" AND location.street_name = \"buchanan\" AND restaurant.food_type = \"arabic\" AND restaurant.id = location.restaurant_id AND restaurant.rating > 2.5 ;",
    "metadata":{
        "source":"unified_sqlv1"
    }
}
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Dataset used to train LarkAI/codet5p-770m_nl2sql_oig