# Cross-task Fine-tuning

A text embedding model fine-tuned based on BAAI/bge-small-en-v1.5, trained with the MEDI dataset and MTEB classification task datasets, optimized for query encoding in retrieval tasks.

T5-Efficient-SMALL is a variant of Google's original T5, adopting a deep narrow architecture that outperforms other architectures in downstream tasks with similar parameter counts.

T5-Efficient-LARGE is a variant based on Google's T5, optimized for downstream task performance with a deep narrow architecture, featuring 737.7 million parameters.

T5-Efficient-SMALL-DM768 is a variant of Google's original T5, adopting a deep narrow architecture that prioritizes increasing model depth to enhance downstream performance.

T5-Efficient-SMALL-KV32 is a variant of Google's original T5, adopting a deep narrow architecture focused on improving downstream task performance.

T5-Efficient-BASE-FF9000 is a variant of Google's original T5, adopting a deep narrow architecture that delivers superior performance on downstream tasks with similar parameter scales.

T5-Efficient-SMALL-KV256 is a variant of Google's T5, optimized for downstream task performance using a deep narrow architecture, with 117 million parameters, requiring fine-tuning for use.

T5-Efficient-MINI is a variant of Google's original T5, adopting a deep narrow architecture that demonstrates superior downstream task performance among models with similar parameter counts.

T5-Efficient-BASE-FF6000 is a variant of Google's original T5, adopting a deep narrow architecture that demonstrates superior downstream task performance among models with similar parameter counts.

T5-Efficient-SMALL-EL2 is a variant of Google's original T5, adopting a deep narrow architecture that prioritizes increasing model depth to enhance downstream performance.