Skip to content

export-transformersjs.md

Latest commit

 

History

History
98 lines (73 loc) · 2.88 KB

export-transformersjs.md

File metadata and controls

98 lines (73 loc) · 2.88 KB
Raw

Transformers.js Export

OpenMed can package an ONNX token-classification export for browser inference through Transformers.js. The bundle is built from the ONNX artifact created by openmed.onnx.convert and contains the file layout expected by browser pipelines:

transformersjs/
  config.json
  tokenizer.json
  tokenizer_config.json
  quantize_config.json
  transformersjs-contract.json
  onnx/
    model.onnx
    model_quantized.onnx

The converter validates that the ONNX graph exposes the token-classification contract used by browser pipelines:

  • inputs: input_ids and attention_mask, plus optional token_type_ids
  • output: logits
  • dynamic axes: [batch, sequence] for inputs and [batch, sequence, labels] for logits

From a fresh ONNX conversion

Use --include-transformersjs when creating ONNX artifacts:

.venv/bin/python -m openmed.onnx.convert \
  --model dslim/bert-base-NER \
  --output dist/example-onnx \
  --include-transformersjs

The OpenMed ONNX manifest then records all emitted runtime formats, including transformersjs, so later publish steps can carry the format list forward.

From an existing ONNX export

If model.onnx, config.json, tokenizer.json, and tokenizer_config.json already exist in an ONNX export directory, build only the browser bundle:

.venv/bin/python -m openmed.onnx.transformersjs \
  --onnx-export-dir dist/example-onnx

By default this writes dist/example-onnx/transformersjs and updates dist/example-onnx/openmed-onnx.json if that manifest exists. Pass --no-manifest-update to leave the source manifest unchanged.

Validation

The Python validator checks required files, config.json label metadata, and the ONNX graph contract:

from openmed.onnx import validate_transformersjs_bundle

validate_transformersjs_bundle("dist/example-onnx/transformersjs")

The repository also includes a headless Node smoke fixture at tests/fixtures/onnx/transformersjs_smoke.mjs. It verifies the same file layout and tensor contract from the generated transformersjs-contract.json.

Browser usage

Once the transformersjs/ directory is served by your application or copied into a static model asset path, load it with Transformers.js:

import { pipeline } from "@huggingface/transformers";

const detector = await pipeline(
  "token-classification",
  "/models/openmed-pii/transformersjs",
  { device: "webgpu" },
);

const entities = await detector("Patient Casey Example called 212-555-0198.");

Use the browser bundle for local-first token classification where PHI should stay inside the user's browser session. The export step only packages model artifacts; it does not change Hugging Face repository visibility.

For an offline synthetic walkthrough that prints the expected bundle files and browser load snippet, run:

uv run python examples/v17_multimodal_browser_interop.py