Anchors
| Anchor text | Ref. domains ▾ | Top DR | Ref. pages | Links to target | Dofollow links |
|---|---|---|---|---|---|
| PyTorch | 548 | — | 0 | 3951 | 3672 92.9% |
| https://pytorch.org/ | 59 | — | 0 | 65 | 53 81.5% |
| Pytorch | 47 | — | 0 | 87 | 77 88.5% |
| https://pytorch.org/get-started/locally/ | 29 | — | 0 | 38 | 27 71.1% |
| here | 21 | — | 0 | 38 | 26 68.4% |
| https://pytorch.org | 18 | — | 0 | 26 | 18 69.2% |
| TorchServe | 17 | — | 0 | 1135 | 1132 99.7% |
| 17 | — | 0 | 3371 | 3358 99.6% | |
| pytorch.org | 14 | — | 0 | 16 | 11 68.8% |
| PyTorch documentation | 13 | — | 0 | 14 | 11 78.6% |
| pytorch | 13 | — | 0 | 34 | 7 20.6% |
| TorchScript | 12 | — | 0 | 16 | 14 87.5% |
| torchvision | 11 | — | 0 | 1822 | 1817 99.7% |
| PyTorch Tutorials | 10 | — | 0 | 10 | 9 90% |
| https://pytorch.org/tutorials/ | 10 | — | 0 | 10 | 8 80% |
| PyTorch Hub | 9 | — | 0 | 13 | 12 92.3% |
| TorchVision | 8 | — | 0 | 26 | 23 88.5% |
| torch.Tensor | 8 | — | 0 | 78 | 77 98.7% |
| Fully Sharded Data Parallel (FSDP) | 7 | — | 0 | 7 | 7 100% |
| torch | 7 | — | 0 | 13 | 12 92.3% |
| DistributedDataParallel | 7 | — | 0 | 9 | 8 88.9% |
| official documentation | 7 | — | 0 | 18 | 17 94.4% |
| PyTorch Documentation | 7 | — | 0 | 11 | 9 81.8% |
| PyTorch website | 6 | — | 0 | 7 | 6 85.7% |
| torch.compile | 6 | — | 0 | 12 | 8 66.7% |
| official PyTorch documentation | 6 | — | 0 | 8 | 7 87.5% |
| http://pytorch.org | 6 | — | 0 | 12 | 12 100% |
| PyTorch 2.0 | 6 | — | 0 | 7 | 7 100% |
| documentation | 6 | — | 0 | 8 | 5 62.5% |
| https://pytorch.org/get-started/previous-versions/ | 5 | — | 0 | 5 | 2 40% |
| link | 5 | — | 0 | 10 | 9 90% |
| PyTorch tutorials | 5 | — | 0 | 6 | 5 83.3% |
| PyTorch Foundation | 5 | — | 0 | 1085 | 1083 99.8% |
| torchtune | 4 | — | 0 | 5 | 3 60% |
| Documentation | 4 | — | 0 | 16 | 9 56.2% |
| tutorial | 4 | — | 0 | 6 | 5 83.3% |
| FlexAttention | 4 | — | 0 | 6 | 5 83.3% |
| Docs | 4 | — | 0 | 26 | 26 100% |
| torch.nn.Module | 4 | — | 0 | 107 | 3 2.8% |
| PyTorch Mobile | 4 | — | 0 | 4 | 4 100% |
| PyTorch Blog | 4 | — | 0 | 4 | 2 50% |
| Torchaudio | 4 | — | 0 | 20 | 20 100% |
| ExecuTorch | 4 | — | 0 | 243 | 242 99.6% |
| PyTorch FSDP | 4 | — | 0 | 4 | 4 100% |
| https://pytorch.org/docs/stable/ | 4 | — | 0 | 4 | 2 50% |
| PyTorch Profiler | 4 | — | 0 | 4 | 4 100% |
| Source | 4 | — | 0 | 4 | 4 100% |
| PyTorch.org | 4 | — | 0 | 7 | 3 42.9% |
| (Beta) PyTorch Inference Performance Tuning on AWS Graviton Processors | 3 | — | 0 | 3 | 3 100% |
| CrossEntropyLoss | 3 | — | 0 | 3 | 3 100% |
Frequently Asked Questions
What anchor texts are used to link to pytorch.org?
This page shows all anchor texts found in backlinks pointing to pytorch.org, sorted by the number of referring domains using each anchor. Anchor texts range from branded terms (like the domain name itself) to keyword-rich phrases that describe the linked content. The distribution of anchor texts reveals how other websites perceive and describe pytorch.org.
What is anchor text?
Anchor text is the visible, clickable text in a hyperlink. Search engines use anchor text as a signal to understand what the linked page is about. For example, if many sites link to a page using the anchor text "best running shoes," search engines infer that the page is relevant to that topic. Anchor text appears in several forms: exact-match (contains target keywords), branded (uses the company or domain name), generic (like "click here"), and naked URLs.
Why is anchor text analysis important for SEO?
Anchor text analysis helps identify potential SEO risks and opportunities. A natural backlink profile has diverse anchor texts including branded terms, generic phrases, and topic-relevant keywords. Over-optimization, where too many backlinks use the same exact-match keyword anchor, can trigger search engine penalties. Conversely, understanding which anchors drive the most authority (measured by referring domain count and DR) helps prioritize link building efforts.
How many unique anchor texts does pytorch.org have?
The anchor text report for pytorch.org displays all distinct anchor texts grouped by their hash. Each row shows how many unique referring domains use that anchor, the total number of links, and the dofollow percentage. A high number of unique anchors generally indicates a healthy, natural backlink profile with diverse link sources.