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Title | Blogs | |
Description | A Systematic Evaluation of Transfer Learning and Pseudo-labeling with BERT-based Ranking log in | about Blog Directory A Systematic Evaluation of Transfer Learning and Pseudo-labeling with BERT-based Ranking Models Submitted by srchvrs on Thu, |
Keywords | N/A |
WebSite | searchivarius.org |
Host IP | 69.60.127.165 |
Location | United States |
Site | Rank |
US$276,661
Last updated: 2023-05-09 11:10:16
searchivarius.org has Semrush global rank of 38,257,314. searchivarius.org has an estimated worth of US$ 276,661, based on its estimated Ads revenue. searchivarius.org receives approximately 31,923 unique visitors each day. Its web server is located in United States, with IP address 69.60.127.165. According to SiteAdvisor, searchivarius.org is safe to visit. |
Purchase/Sale Value | US$276,661 |
Daily Ads Revenue | US$256 |
Monthly Ads Revenue | US$7,662 |
Yearly Ads Revenue | US$91,937 |
Daily Unique Visitors | 2,129 |
Note: All traffic and earnings values are estimates. |
Host | Type | TTL | Data |
searchivarius.org. | A | 1799 | IP: 69.60.127.165 |
searchivarius.org. | NS | 3600 | NS Record: ns57.domaincontrol.com. |
searchivarius.org. | NS | 3600 | NS Record: ns58.domaincontrol.com. |
log in | about Blog Directory A Systematic Evaluation of Transfer Learning and Pseudo-labeling with BERT-based Ranking Models Submitted by srchvrs on Thu, 04/15/2021 - 13:17 Due to high annotation costs making the best use of existing human-created training data is an important research direction. We, therefore, carried out a systematic evaluation of transferability of BERT-based neural ranking models across five English datasets . Previous studies focused primarily on zero-shot and few-shot transfer from a large dataset to a dataset with a small number of queries. In contrast, each of our collections has a substantial number of queries, which enables a full-shot evaluation mode and improves reliability of our results. Furthermore, since source datasets licences often prohibit commercial use, we compare transfer learning to training on pseudo-labels generated by a BM25 scorer. We find that training on pseudo-labels—possibly with subsequent fine-tuning using a modest number of |
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