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Fixed examples test error to adapt to neural_compressor v2.3 #420

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Fixed examples test error to adapt to neural_compressor v2.3 #420

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PenghuiCheng
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What does this PR do?

Fixed examples test error to adapt to neural_compressor v2.3

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  • This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
  • Did you make sure to update the documentation with your changes?
  • Did you write any new necessary tests?

echarlaix
echarlaix reviewed Sep 6, 2023
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Thanks a lot for adding this in anticipation to the neural-compressor release. Is there any additional modification that needs to be added so that everything stays compatible ?

@PenghuiCheng
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Thanks a lot for adding this in anticipation to the neural-compressor release. Is there any additional modification that needs to be added so that everything stays compatible ?

No more changes for neural compressor 2.3 version

@PenghuiCheng PenghuiCheng force-pushed the penghuic/fixed_examples_error branch from 20cd567 to c6926ac Compare September 8, 2023 07:29
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HuggingFaceDocBuilderDev commented Sep 8, 2023 •
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The documentation is not available anymore as the PR was closed or merged.

@PenghuiCheng PenghuiCheng force-pushed the penghuic/fixed_examples_error branch from c6926ac to 489a9be Compare September 11, 2023 06:45
@PenghuiCheng PenghuiCheng force-pushed the penghuic/fixed_examples_error branch from 489a9be to 588599c Compare September 11, 2023 09:22
echarlaix
echarlaix reviewed Sep 11, 2023
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examples/neural_compressor/test_examples.py
self.assertGreaterEqual(results["eval_f1"], 70)
self.assertGreaterEqual(results["eval_exact_match"], 70)
self.assertGreaterEqual(results["eval_f1"], 60)
self.assertGreaterEqual(results["eval_exact_match"], 45)
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why this modification ?

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@PenghuiCheng any news on this ?

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hi, Echarlaix, Sorry for the delayed response. We are implementing the pre-check-in test on our server, and we found the accuracy does not match the value. So we want to change it to pass the test. However I found that the accuracy is not stable, for the token-classification example, I will get "f1" < 0.6 sometimes. I cannot explain this phenomenon, is it related to training arguments?

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@echarlaix echarlaix echarlaix approved these changes

@changwangss changwangss Awaiting requested review from changwangss

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@PenghuiCheng @HuggingFaceDocBuilderDev @echarlaix