45 lines
1.2 KiB
Plaintext
45 lines
1.2 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 '@xenova/transformers';
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// Allocate a pipeline for sentiment-analysis
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let pipe = await pipeline('sentiment-analysis');
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let 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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let pipe = await pipeline('sentiment-analysis', 'Xenova/bert-base-multilingual-uncased-sentiment');
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```
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