Tscholak
Rank:Average Model Cost: $0.0000
Number of Runs: 4,289
Models by this creator
cxmefzzi
cxmefzzi
tscholak/cxmefzzi Fine-tuned weights for PICARD - Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models based on T5-3B. Training Data The model has been fine-tuned on the 7000 training examples in the Spider text-to-SQL dataset. The model solves Spider's zero-shot text-to-SQL translation task, and that means that it can generalize to unseen SQL databases. Training Objective This model was initialized with T5-3B and fine-tuned with the text-to-text generation objective. Questions are always grounded in a database schema, and the model is trained to predict the SQL query that would be used to answer the question. The input to the model is composed of the user's natural language question, the database identifier, and a list of tables and their columns: The model outputs the database identifier and the SQL query that will be executed on the database to answer the user's question: Performance Out of the box, this model achieves 71.5 % exact-set match accuracy and 74.4 % execution accuracy on the Spider development set. On the test set, the model achieves 68.0 % exact-set match accuracy and 70.1 % execution accuracy. Using the PICARD constrained decoding method (see the official PICARD implementation), the model's performance can be improved to 75.5 % exact-set match accuracy and 79.3 % execution accuracy on the Spider development set. On the test set and with PICARD, the model achieves 71.9 % exact-set match accuracy and 75.1 % execution accuracy. Usage Please see the official repository for scripts and docker images that support evaluation and serving of this model. References PICARD - Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models Official PICARD code Citation
$-/run
1.2K
Huggingface
t5.1.1.lm100k.base
$-/run
56
Huggingface