Bump @huggingface/transformers from 4.3.0 to 4.3.1

[dependabot]

Bumps @huggingface/transformers from 4.3.0 to 4.3.1.

Release notes

Sourced from @​huggingface/transformers's releases.

4.3.1

What's new?

This release adds support for EmbeddingGemma 2. EmbeddingGemma 2 is an open multimodal embedding model built by Google DeepMind which maps text (incl. code), images, video, and audio inputs—and combinations thereof—into a single, unified 768-dimensional vector space. The model has 740M total parameters, combining a 270M parameter text model with modular vision (170M) and audio (300M) encoders.

Text search

The feature-extraction pipeline returns normalized embeddings, so the dot product of two embeddings is their cosine similarity:

import { pipeline, matmul } from "@huggingface/transformers";
const extractor = await pipeline("feature-extraction", "onnx-community/embeddinggemma-2-ONNX", {
device: "webgpu", // or "wasm" (browser) / "cpu" (Node.js)
dtype: "q4", // see "Choosing a dtype" below
});
const query = "task: search result | query: Which planet is known as the Red Planet?";
const documents = [
"title: none | text: Venus is often called Earth's twin because of its similar size and proximity.",
"title: none | text: Mars, known for its reddish appearance, is often referred to as the Red Planet.",
"title: none | text: Jupiter, the largest planet in our solar system, has a prominent red spot.",
"title: none | text: Saturn, famous for its rings, is sometimes mistaken for the Red Planet.",
];
const embeddings = await extractor([query, ...documents], { pooling: "mean", normalize: true });
const query_embedding = embeddings.slice([0, 1]);
const document_embeddings = embeddings.slice([1, null]);
const scores = (await matmul(query_embedding, document_embeddings.transpose(1, 0))).tolist()[0];
const ranking = scores.map((score, i) => ({ score, document: documents[i] })).sort((a, b) => b.score - a.score);
console.log(ranking);
// [
//   { score: 0.854, document: "title: none | text: Mars, known for its reddish appearance, ..." },
//   { score: 0.783, document: "title: none | text: Saturn, famous for its rings, ..." },
//   { score: 0.752, document: "title: none | text: Jupiter, the largest planet in our solar system, ..." },
//   { score: 0.684, document: "title: none | text: Venus is often called Earth's twin, ..." },
// ]

Images, audio and video

For other modalities, use the processor and the model directly. Every input maps into the same embedding space, so any embedding can be compared with any other: here, text queries against an image, an audio clip and a video.

import { AutoModel, AutoProcessor, load_image, load_audio, load_video, cat, matmul } from "@huggingface/transformers";
const model_id = "onnx-community/embeddinggemma-2-ONNX";
const processor = await AutoProcessor.from_pretrained(model_id);
</tr></table>

... (truncated)

Commits

• 511bb61 [version] Update to 4.3.1
• e822439 new model (#1788)
• 40d3304 Bump doc-builder workflow pin (#1787)
• 836b9cb Scope GITHUB_TOKEN permissions per job (#1777)
• c5f2b97 Bump the actions group across 1 directory with 6 updates (#1703)
• See full diff in compare view

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