mteb/WebLINXCandidatesReranking
WebLINXCandidatesReranking An MTEB dataset Massive Text Embedding Benchmark WebLINX is a large-scale benchmark of 100K interactions across 2300 expert demonstrations of conversational web navigation. The reranking task focuses on finding relevant elements at every given step in the trajectory. Task category t2t Domains Academic, Web, Written Reference https://mcgill-nlp.github.io/weblinx How to evaluate on this task You can evaluate an embedding… See the full description on the dataset page: https://huggingface.co/datasets/mteb/WebLINXCandidatesReranking.
| Author / organization | mteb |
|---|---|
| License (as declared) | cc-by-nc-sa-4.0 |
| Downloads | 416 (as reported by Hugging Face) |
| Likes | 0 |
| Created | 18 February 2025 |
| Last updated at source | 24 February 2026 |
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- 17 Oct 2026 Description: WebLINXCandidatesReranking An MTEB dataset Massive Text Embedding Benchmark WebLINX is a large-scale benchmark of 100K interactions across 2300 expert demonstrations of conversational web navigation. The reranking task focuses on finding re → WebLINXCandidatesReranking An MTEB dataset Massive Text Embedding Benchmark WebLINX is a large-scale benchmark of 100K interactions across 2300 expert demonstrations of conversational web navigation. The reranking task focuses on finding re
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