new: BAAI/BGE-M3 support with testing script

[lucifertrj]

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New models submission:

• Have you added an explanation of why it's important to include this model?
• Have you added tests for the new model? Were canonical values for tests computed via the original model?
• Have you added the code snippet for how canonical values were computed?
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New Model: BAAI/bge-m3

MIT-Licensed:

Model Name	Dimension	Sequence Length	Introduction
BAAI/bge-m3	1024	8192	multilingual; unified fine-tuning (dense, sparse, and colbert) from bge-m3-unsupervised

🔗 Colab Notebook:

Code Snippet for Canonical Values:

docs = ["hello world", "flag embedding"]
embeddings = list(embedding_model.embed(docs))
embeddings = np.stack(embeddings, axis=0)

canonical = np.round(embeddings[0, :5], 4)
print(f"Canonical vector values: {canonical}")

Output:

Canonical vector values: [-0.0404  0.037  -0.029   0.0161 -0.0357]

Added these values in: tests/test_text_onnx_embeddings.py

[coderabbitai]

📝 Walkthrough

Walkthrough

This pull request adds support for the BAAI/bge-m3 multilingual text embedding model to the FastEmbed library. A new model entry is registered in the supported_onnx_models list with metadata including embedding dimension (1024), model size (2.27 GB), license (MIT), and file paths for the ONNX model and associated resources. A corresponding canonical embedding vector is added to the test suite for verification purposes.

Estimated code review effort

🎯 1 (Trivial) | ⏱️ ~5 minutes

🚥 Pre-merge checks | ✅ 3

✅ Passed checks (3 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title clearly and specifically describes the main change: adding support for the BAAI/BGE-M3 model with corresponding test coverage, which aligns perfectly with the changeset.
Description check	✅ Passed	The description is comprehensive and directly related to the changeset, providing model details, testing information, canonical values, and code snippets used to compute those values.
Docstring Coverage	✅ Passed	No functions found in the changed files to evaluate docstring coverage. Skipping docstring coverage check.

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✨ Finishing touches

• 📝 Generate docstrings

🧪 Generate unit tests (beta)

• Create PR with unit tests
• Post copyable unit tests in a comment

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[lucifertrj]

@joein Supported Models docs page needs to be updated

[mohamad-tohidi]

when will this merge?

[michelkluger]

I feel like the library is falling a bit behind in supporting models sadly

[mohamad-tohidi]

i agree

[JiwaniZakir]

The DenseModelDescription in onnx_embedding.py advertises "8192 input tokens truncation" in the description, but there's no corresponding additional_kwargs (e.g., {"max_length": 8192}) or similar field to actually configure the tokenizer's max sequence length. Without this, the tokenizer will fall back to its default, likely 512 tokens, silently discarding the model's long-context capability and making the description misleading. It's worth checking how other long-context models in this registry handle that setting.

The test entry in test_text_onnx_embeddings.py only checks 5 embedding dimensions, consistent with the rest of the suite, but there's no assertion that the output shape matches the declared dim=1024. Given the model is 2.27 GB and has an unusual external data file (model.onnx_data), a shape check would help catch loading issues early — for instance, if the wrong ONNX graph is loaded or the external data file is missing at inference time.