transformers-js-playground / public /workers /text-classification.js
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Enhance text classification pipeline: add device support, improve error handling, and refine message processing logic
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/* eslint-disable no-restricted-globals */
import { pipeline } from 'https://cdn.jsdelivr.net/npm/@huggingface/transformers@3.6.3'
class MyTextClassificationPipeline {
static task = 'text-classification'
static instance = null
static async getInstance(model, dtype = 'fp32', progress_callback = null) {
this.instance = pipeline(
this.task,
model,
{ dtype, device: "webgpu", progress_callback },
)
return this.instance
}
}
// Listen for messages from the main thread
self.addEventListener('message', async (event) => {
try {
const { type, model, dtype, text } = event.data
if (!model) {
self.postMessage({
status: 'error',
output: 'No model provided'
})
return
}
// Retrieve the pipeline. This will download the model if not already cached.
const classifier = await MyTextClassificationPipeline.getInstance(
model,
dtype,
(x) => {
self.postMessage({ status: 'loading', output: x })
}
)
if (type === 'load') {
self.postMessage({
status: 'ready',
output: `Model ${model}, dtype ${dtype} loaded`
})
return
}
if (type === 'classify') {
if (!text) {
self.postMessage({ status: 'ready' }) // Nothing to process
return
}
const split = text.split('\n')
for (const line of split) {
if (line.trim()) {
const output = await classifier(line)
self.postMessage({
status: 'output',
output: {
sequence: line,
labels: [output[0].label],
scores: [output[0].score]
}
})
}
}
self.postMessage({ status: 'ready' })
}
} catch (error) {
self.postMessage({
status: 'error',
output: error.message || 'An error occurred during processing'
})
}
})