Describe the bug Hello, I'm trying to use Gradio-Lite with zer

It's about Transformers.js' API spec where you have to pass the <code class="notransla

Gradio-Lite : zero-shot-classification pipeline returns only 1 score about gradio HOT 2 CLOSED

xavierbarbier commented on September 23, 2024

Gradio-Lite : zero-shot-classification pipeline returns only 1 score

from gradio.

Comments (2)

whitphx commented on September 23, 2024 2

It's about Transformers.js' API spec where you have to pass the labels at prediction, not model initialization (see https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.ZeroShotClassificationPipeline).

So your code should be modified like this:

from transformers_js import import_transformers_js
import gradio as gr

labels=['politics', 'music','police']
transformers = await import_transformers_js()
pipeline = transformers.pipeline
model_path = 'Xenova/mobilebert-uncased-mnli'
pipe = await pipeline('zero-shot-classification', model_path) # Not here.

async def classify(text):
	pred = await pipe(text, labels) # Pass `labels` here.

	return pred["scores"]


demo = gr.Interface(classify, "textbox", "textbox")
demo.launch()

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xavierbarbier commented on September 23, 2024 1

I first try to define labels within the pipe at inference (as with "classic" Transformer pipeline). Wasn't working.
Then I tried at model initialization. Wasn't working either as expected.
Seems you have to define them separately from inference call.
It's working now.
Arigato !

from transformers_js import import_transformers_js
import gradio as gr

labels=['politics', 'music','police'] # works
transformers = await import_transformers_js()
pipeline = transformers.pipeline
model_path = 'Xenova/mobilebert-uncased-mnli'
pipe = await pipeline('zero-shot-classification', model_path) # Not here.
# labels=['politics', 'music','police'] # works 
async def classify(text):
	pred = await pipe(text, labels # Pass `labels` here.
                                   # labels=['politics', 'music','police'] # doesn't work
                                     ) 

	return pred["scores"]


demo = gr.Interface(classify, "textbox", "textbox")
demo.launch()

from gradio.

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