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| <link rel="modulepreload" href="/docs/transformers/main/en/_app/immutable/chunks/EditOnGithub.91d95064.js"><!-- HEAD_svelte-u9bgzb_START --><meta name="hf:doc:metadata" content="{"title":"BORT","local":"bort","sections":[{"title":"Overview","local":"overview","sections":[],"depth":2},{"title":"Usage tips","local":"usage-tips","sections":[],"depth":2}],"depth":1}"><!-- HEAD_svelte-u9bgzb_END --> <p></p> <h1 class="relative group"><a id="bort" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#bort"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>BORT</span></h1> <div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"><p data-svelte-h="svelte-11xtczr">This model is in maintenance mode only, we do not accept any new PRs changing its code.</p> <p data-svelte-h="svelte-4042uy">If you run into any issues running this model, please reinstall the last version that supported this model: v4.30.0. | |
| You can do so by running the following command: <code>pip install -U transformers==4.30.0</code>.</p></div> <h2 class="relative group"><a id="overview" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#overview"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Overview</span></h2> <p data-svelte-h="svelte-1ekxxrt">The BORT model was proposed in <a href="https://arxiv.org/abs/2010.10499" rel="nofollow">Optimal Subarchitecture Extraction for BERT</a> by | |
| Adrian de Wynter and Daniel J. Perry. It is an optimal subset of architectural parameters for the BERT, which the | |
| authors refer to as “Bort”.</p> <p data-svelte-h="svelte-vfdo9a">The abstract from the paper is the following:</p> <p data-svelte-h="svelte-1vfe3y"><em>We extract an optimal subset of architectural parameters for the BERT architecture from Devlin et al. (2018) by | |
| applying recent breakthroughs in algorithms for neural architecture search. This optimal subset, which we refer to as | |
| “Bort”, is demonstrably smaller, having an effective (that is, not counting the embedding layer) size of 5.5% the | |
| original BERT-large architecture, and 16% of the net size. Bort is also able to be pretrained in 288 GPU hours, which | |
| is 1.2% of the time required to pretrain the highest-performing BERT parametric architectural variant, RoBERTa-large | |
| (Liu et al., 2019), and about 33% of that of the world-record, in GPU hours, required to train BERT-large on the same | |
| hardware. It is also 7.9x faster on a CPU, as well as being better performing than other compressed variants of the | |
| architecture, and some of the non-compressed variants: it obtains performance improvements of between 0.3% and 31%, | |
| absolute, with respect to BERT-large, on multiple public natural language understanding (NLU) benchmarks.</em></p> <p data-svelte-h="svelte-n8ivge">This model was contributed by <a href="https://huggingface.co/stefan-it" rel="nofollow">stefan-it</a>. The original code can be found <a href="https://github.com/alexa/bort/" rel="nofollow">here</a>.</p> <h2 class="relative group"><a id="usage-tips" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#usage-tips"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Usage tips</span></h2> <ul data-svelte-h="svelte-1tf6va0"><li>BORT’s model architecture is based on BERT, refer to <a href="bert">BERT’s documentation page</a> for the | |
| model’s API reference as well as usage examples.</li> <li>BORT uses the RoBERTa tokenizer instead of the BERT tokenizer, refer to <a href="roberta">RoBERTa’s documentation page</a> for the tokenizer’s API reference as well as usage examples.</li> <li>BORT requires a specific fine-tuning algorithm, called <a href="https://adewynter.github.io/notes/bort_algorithms_and_applications.html#fine-tuning-with-algebraic-topology" rel="nofollow">Agora</a> , | |
| that is sadly not open-sourced yet. It would be very useful for the community, if someone tries to implement the | |
| algorithm to make BORT fine-tuning work.</li></ul> <a class="!text-gray-400 !no-underline text-sm flex items-center not-prose mt-4" href="https://github.com/huggingface/transformers/blob/main/docs/source/en/model_doc/bort.md" target="_blank"><span data-svelte-h="svelte-1kd6by1"><</span> <span data-svelte-h="svelte-x0xyl0">></span> <span data-svelte-h="svelte-1dajgef"><span class="underline ml-1.5">Update</span> on GitHub</span></a> <p></p> | |
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