Comments (3)
Test of distilBERT with batch and GPU support:
Input:
! sh similarity_distilBERT_batch.sh 40 cls True
Output:
Confusion matrix:
langs cs de en es fr avg
cs 0.00% 92.64% 98.43% 92.57% 94.44% 94.52%
de 77.66% 0.00% 91.08% 76.99% 80.25% 81.49%
en 83.82% 71.46% 0.00% 54.08% 61.44% 67.70%
es 79.65% 82.88% 88.68% 0.00% 69.70% 80.23%
fr 79.39% 82.32% 87.01% 60.81% 0.00% 77.38%
avg 80.13% 82.33% 91.30% 71.11% 76.46% 80.26%
Previously, we had:
Input:
! sh similarity_distilBERT.sh 40 cls
Output:
langs de en es fr ru avg
de 0.00% 91.08% 76.99% 80.25% 84.75% 83.27%
en 71.46% 0.00% 54.08% 61.44% 67.83% 63.70%
es 82.88% 88.68% 0.00% 69.70% 78.75% 80.00%
fr 82.32% 87.01% 60.81% 0.00% 78.95% 77.27%
ru 86.15% 91.91% 76.26% 80.82% 0.00% 83.78%
avg 80.70% 89.67% 67.03% 73.05% 77.57% 77.61%
from multilingual_similarity_compare.
Looking at pairs like ('en', 'es'), we can see that we yield the same results when using batch and GPU than when using one sentence at a time with CPU.
from multilingual_similarity_compare.
Input
! sh similarity_distilBERT_batch.sh 100 cls True
Output:
Confusion matrix:
langs cs de en es fr avg
cs 0.00% 89.84% 98.07% 89.88% 91.94% 92.43%
de 72.86% 0.00% 88.68% 71.33% 73.63% 76.62%
en 81.55% 64.47% 0.00% 42.99% 51.85% 60.21%
es 77.46% 78.82% 85.55% 0.00% 60.11% 75.48%
fr 75.42% 76.76% 83.82% 50.18% 0.00% 71.55%
avg 76.82% 77.47% 89.03% 63.59% 69.38% 75.26%
Input:
! sh similarity_distilBERT_batch.sh 40 cls True
Output:
Confusion matrix:
langs cs de en es fr avg
cs 0.00% 92.64% 98.43% 92.57% 94.44% 94.52%
de 77.66% 0.00% 91.08% 76.99% 80.25% 81.49%
en 83.82% 71.46% 0.00% 54.08% 61.44% 67.70%
es 79.65% 82.88% 88.68% 0.00% 69.70% 80.23%
fr 79.39% 82.32% 87.01% 60.81% 0.00% 77.38%
avg 80.13% 82.33% 91.30% 71.11% 76.46% 80.26%
from multilingual_similarity_compare.
Related Issues (10)
- What metric is tracked in the table? HOT 1
- Benchmarks for LaBSE HOT 4
- Why does XLM-R have low performance on this task?
- Add batch-size as a parameter in parser.args
- Time comparison between GPU and CPU HOT 1
- Why is mean pooling giving such bad performance? HOT 2
- Shift everything to batch version with GPU support for faster results after testing HOT 2
- Test XLM-R with mean pooling after solving issue on mean pooling HOT 1
- Arabic and Urdu HOT 3
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from multilingual_similarity_compare.