dependabot[bot] opened a new pull request, #39329: URL: https://github.com/apache/beam/pull/39329
Bumps [transformers](https://github.com/huggingface/transformers) from 4.55.4 to 5.5.0. <details> <summary>Release notes</summary> <p><em>Sourced from <a href="https://github.com/huggingface/transformers/releases";>transformers's releases</a>.</em></p> <blockquote> <h1>Release v5.5.0</h1> <!-- raw HTML omitted --> <h2>New Model additions</h2> <h3>Gemma4</h3> <p><a href="https://github.com/huggingface/transformers/blob/HEAD/INSET_PAPER_LINK";>Gemma 4</a> is a multimodal model with pretrained and instruction-tuned variants, available in 1B, 13B, and 27B parameters. The architecture is mostly the same as the previous Gemma versions. The key differences are a vision processor that can output images of fixed token budget and a spatial 2D RoPE to encode vision-specific information across height and width axis.</p> <!-- raw HTML omitted --> <p>You can find all the original Gemma 4 checkpoints under the <a href="https://huggingface.co/collections/google/gemma-4-release-67c6c6f89c4f76621268bb6d";>Gemma 4</a> release.</p> <p>The key difference from previous Gemma releases is the new design to process <strong>images of different sizes</strong> using a <strong>fixed-budget number of tokens</strong>. Unlike many models that squash every image into a fixed square (like 224×224), Gemma 4 keeps the image's natural aspect ratio while making it the right size. There a a couple constraints to follow:</p> <ul> <li>The total number of pixels must fit within a patch budget</li> <li>Both height and width must be divisible by <strong>48</strong> (= patch size 16 × pooling kernel 3)</li> </ul> <blockquote> <p>[!IMPORTANT] Gemma 4 does <strong>not</strong> apply the standard ImageNet mean/std normalization that many other vision models use. The model's own patch embedding layer handles the final scaling internally (shifting values to the [-1, 1] range).</p> </blockquote> <p>The number of "soft tokens" (aka vision tokens) an image processor can produce is configurable. The supported options are outlined below and the default is <strong>280 soft tokens</strong> per image.</p> <table> <thead> <tr> <th align="center">Soft Tokens</th> <th align="center">Patches (before pooling)</th> <th align="center">Approx. Image Area</th> </tr> </thead> <tbody> <tr> <td align="center">70</td> <td align="center">630</td> <td align="center">~161K pixels</td> </tr> <tr> <td align="center">140</td> <td align="center">1,260</td> <td align="center">~323K pixels</td> </tr> <tr> <td align="center"><strong>280</strong></td> <td align="center"><strong>2,520</strong></td> <td align="center"><strong>~645K pixels</strong></td> </tr> <tr> <td align="center">560</td> <td align="center">5,040</td> <td align="center">~1.3M pixels</td> </tr> <tr> <td align="center">1,120</td> <td align="center">10,080</td> <td align="center">~2.6M pixels</td> </tr> </tbody> </table> <p>To encode positional information for each patch in the image, Gemma 4 uses a learned 2D position embedding table. The position table stores up to 10,240 positions per axis, which allows the model to handle very large images. Each position is a learned vector of the same dimensions as the patch embedding. The 2D RoPE which Gemma 4 uses independently rotate half the attention head dimensions for the x-axis and the other half for the y-axis. This allows the model to understand spatial relationships like "above," "below," "left of," and "right of."</p> <h3>NomicBERT</h3> <p>NomicBERT is a BERT-inspired encoder model that applies Rotary Position Embeddings (RoPE) to create reproducible long context text embeddings. It is the first fully reproducible, open-source text embedding model with 8192 context length that outperforms both OpenAI Ada-002 and OpenAI text-embedding-3-small on short-context MTEB and long context LoCo benchmarks. The model generates dense vector embeddings for various tasks including search, clustering, and classification using specific instruction prefixes.</p> <p><strong>Links:</strong> <a href="https://huggingface.co/docs/transformers/main/en/model_doc/nomic_bert";>Documentation</a> | <a href="https://arxiv.org/abs/2402.01613";>Paper</a></p> <ul> <li>Internalise the NomicBERT model (<a href="https://redirect.github.com/huggingface/transformers/issues/43067";>#43067</a>) by <a href="https://github.com/ed22699";><code>@ed22699</code></a> in <a href="https://redirect.github.com/huggingface/transformers/pull/43067";>#43067</a></li> </ul> <h3>MusicFlamingo</h3> <p>Music Flamingo is a fully open large audio–language model designed for robust understanding and reasoning over music. It builds upon the Audio Flamingo 3 architecture by including Rotary Time Embeddings (RoTE), which injects temporal position information to enable the model to handle audio sequences up to 20 minutes. The model features a unified audio encoder across speech, sound, and music with special sound boundary tokens for improved audio sequence modeling.</p> <p><strong>Links:</strong> <a href="https://huggingface.co/docs/transformers/main/en/model_doc/musicflamingo";>Documentation</a> | <a href="https://huggingface.co/papers/2511.10289";>Paper</a></p> <ul> <li>Add Music Flamingo (<a href="https://redirect.github.com/huggingface/transformers/issues/43538";>#43538</a>) by <a href="https://github.com/lashahub";><code>@lashahub</code></a> in <a href="https://redirect.github.com/huggingface/transformers/pull/43538";>#43538</a></li> </ul> <!-- raw HTML omitted --> </blockquote> <p>... (truncated)</p> </details> <details> <summary>Commits</summary> <ul> <li><a href="https://github.com/huggingface/transformers/commit/c1c34249fa27deefbd4a377dfbf883a39baf5c6d";><code>c1c3424</code></a> update</li> <li><a href="https://github.com/huggingface/transformers/commit/20bff6865a756a074f5b893b57f0ae438b25ec46";><code>20bff68</code></a> update release workflow</li> <li><a href="https://github.com/huggingface/transformers/commit/89564412a56ae6581f8aa48a533a835860dc9f43";><code>8956441</code></a> v5.5.0</li> <li><a href="https://github.com/huggingface/transformers/commit/5135e5efa7203cd23aac0866de12dfeef038422d";><code>5135e5e</code></a> casually dropping the most capable open weights on the planet (<a href="https://redirect.github.com/huggingface/transformers/issues/45192";>#45192</a>)</li> <li><a href="https://github.com/huggingface/transformers/commit/a594e09e3924120f1f5508e7d81946bf3504df2b";><code>a594e09</code></a> Internalise the NomicBERT model (<a href="https://redirect.github.com/huggingface/transformers/issues/43067";>#43067</a>)</li> <li><a href="https://github.com/huggingface/transformers/commit/4932e9721e230bea915341e7f04db32885b6c6af";><code>4932e97</code></a> Fix resized LM head weights being overwritten by post_init (<a href="https://redirect.github.com/huggingface/transformers/issues/45079";>#45079</a>)</li> <li><a href="https://github.com/huggingface/transformers/commit/57e84139542c8c297873f35fcd25f66ffcf132ae";><code>57e8413</code></a> [Qwen3.5 MoE] Add _tp_plan to ForConditionalGeneration (<a href="https://redirect.github.com/huggingface/transformers/issues/45124";>#45124</a>)</li> <li><a href="https://github.com/huggingface/transformers/commit/b10552e99dc4974b30126995baea455df43f8476";><code>b10552e</code></a> Fix TypeError: 'NoneType' object is not iterable in GenerationMixin.generate ...</li> <li><a href="https://github.com/huggingface/transformers/commit/423f2a31d2bd05bdc1dc30dd938389edaa998fde";><code>423f2a3</code></a> fix(models): Fix dtype mismatch in SwitchTransformers and TimmWrapperModel (#...</li> <li><a href="https://github.com/huggingface/transformers/commit/ade7a05a42bf53b183bb78c181743be063c5ff14";><code>ade7a05</code></a> Generalize gemma vision mask to videos (<a href="https://redirect.github.com/huggingface/transformers/issues/45185";>#45185</a>)</li> <li>Additional commits viewable in <a href="https://github.com/huggingface/transformers/compare/v4.55.4...v5.5.0";>compare view</a></li> </ul> </details> <br /> [](https://docs.github.com/en/github/managing-security-vulnerabilities/about-dependabot-security-updates#about-compatibility-scores) Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. 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