wehub-resource-sync
75 lines
3.0 KiB
Plaintext
75 lines
3.0 KiB
Plaintext
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It's super simple to translate from existing code! Just like the python library, we support the `pipeline` API. Pipelines group together a pretrained model with preprocessing of inputs and postprocessing of outputs, making it the easiest way to run models with the library.
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<table>
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<tr>
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<th width="440px" align="center"><b>Python (original)</b></th>
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<th width="440px" align="center"><b>Javascript (ours)</b></th>
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</tr>
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<tr>
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<td>
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```python
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from transformers import pipeline
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# Allocate a pipeline for sentiment-analysis
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pipe = pipeline('sentiment-analysis')
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out = pipe('I love transformers!')
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# [{'label': 'POSITIVE', 'score': 0.999806941}]
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```
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</td>
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<td>
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```javascript
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import { pipeline } from '@huggingface/transformers';
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// Allocate a pipeline for sentiment-analysis
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const pipe = await pipeline('sentiment-analysis');
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const out = await pipe('I love transformers!');
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// [{'label': 'POSITIVE', 'score': 0.999817686}]
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```
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</td>
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</tr>
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</table>
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You can also use a different model by specifying the model id or path as the second argument to the `pipeline` function. For example:
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```javascript
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// Use a different model for sentiment-analysis
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const pipe = await pipeline('sentiment-analysis', 'Xenova/bert-base-multilingual-uncased-sentiment');
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```
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By default, when running in the browser, the model will be run on your CPU (via WASM). If you would like
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to run the model on your GPU (via WebGPU), you can do this by setting `device: 'webgpu'`, for example:
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```javascript
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// Run the model on WebGPU
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const pipe = await pipeline('sentiment-analysis', 'Xenova/distilbert-base-uncased-finetuned-sst-2-english', {
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device: 'webgpu',
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});
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```
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For more information, check out the [WebGPU guide](./guides/webgpu).
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> [!WARNING]
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> The WebGPU API is still experimental in many browsers, so if you run into any issues,
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> please file a [bug report](https://github.com/huggingface/transformers.js/issues/new?title=%5BWebGPU%5D%20Error%20running%20MODEL_ID_GOES_HERE&assignees=&labels=bug,webgpu&projects=&template=1_bug-report.yml).
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In resource-constrained environments, such as web browsers, it is advisable to use a quantized version of
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the model to lower bandwidth and optimize performance. This can be achieved by adjusting the `dtype` option,
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which allows you to select the appropriate data type for your model. While the available options may vary
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depending on the specific model, typical choices include `"fp32"` (default for WebGPU), `"fp16"`, `"q8"`
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(default for WASM), and `"q4"`. For more information, check out the [quantization guide](./guides/dtypes).
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```javascript
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// Run the model at 4-bit quantization
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const pipe = await pipeline('sentiment-analysis', 'Xenova/distilbert-base-uncased-finetuned-sst-2-english', {
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dtype: 'q4',
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});
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```
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Ready to dive in? Explore our wide variety of demo applications and templates [here](https://github.com/huggingface/transformers.js-examples). You can also launch your own project instantly using the official Transformers.js [template](https://huggingface.co/new-space?template=static-templates%2Ftransformers.js) on Hugging Face!
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