{"text": "RetroMAE can provide a strong initialization of dense retriever; after fine-tuned with in-domain data, it gives rise to a high-quality supervised retrieval performance in the corresponding scenario. Besides, It substantially improves the pre-trained model's transferability, which helps to result in superior zero-shot performances on out-of-domain datasets."}
{"text": "RetroMAE can provide a strong initialization of dense retriever; after fine-tuned with in-domain data, it gives rise to a high-quality supervised retrieval performance in the corresponding scenario. Besides, It substantially improves the pre-trained model's transferability, which helps to result in superior zero-shot performances on out-of-domain datasets."}
{"text": "RetroMAE can provide a strong initialization of dense retriever; after fine-tuned with in-domain data, it gives rise to a high-quality supervised retrieval performance in the corresponding scenario. Besides, It substantially improves the pre-trained model's transferability, which helps to result in superior zero-shot performances on out-of-domain datasets."}
{"text": "RetroMAE can provide a strong initialization of dense retriever; after fine-tuned with in-domain data, it gives rise to a high-quality supervised retrieval performance in the corresponding scenario. Besides, It substantially improves the pre-trained model's transferability, which helps to result in superior zero-shot performances on out-of-domain datasets."}
{"text": "RetroMAE can provide a strong initialization of dense retriever; after fine-tuned with in-domain data, it gives rise to a high-quality supervised retrieval performance in the corresponding scenario. Besides, It substantially improves the pre-trained model's transferability, which helps to result in superior zero-shot performances on out-of-domain datasets."}
{"text": "RetroMAE can provide a strong initialization of dense retriever; after fine-tuned with in-domain data, it gives rise to a high-quality supervised retrieval performance in the corresponding scenario. Besides, It substantially improves the pre-trained model's transferability, which helps to result in superior zero-shot performances on out-of-domain datasets."}
{"text": "RetroMAE can provide a strong initialization of dense retriever; after fine-tuned with in-domain data, it gives rise to a high-quality supervised retrieval performance in the corresponding scenario. Besides, It substantially improves the pre-trained model's transferability, which helps to result in superior zero-shot performances on out-of-domain datasets."}
{"text": "RetroMAE can provide a strong initialization of dense retriever; after fine-tuned with in-domain data, it gives rise to a high-quality supervised retrieval performance in the corresponding scenario. Besides, It substantially improves the pre-trained model's transferability, which helps to result in superior zero-shot performances on out-of-domain datasets."}
{"text": "RetroMAE can provide a strong initialization of dense retriever; after fine-tuned with in-domain data, it gives rise to a high-quality supervised retrieval performance in the corresponding scenario. Besides, It substantially improves the pre-trained model's transferability, which helps to result in superior zero-shot performances on out-of-domain datasets."}
{"text": "RetroMAE can provide a strong initialization of dense retriever; after fine-tuned with in-domain data, it gives rise to a high-quality supervised retrieval performance in the corresponding scenario. Besides, It substantially improves the pre-trained model's transferability, which helps to result in superior zero-shot performances on out-of-domain datasets."}