Comments (1)
Comparison with CLS embedding for test purpose:
Input:
sh similarity_XLM-R_batch.sh 40 cls True
Output:
Confusion matrix:
langs cs de en es fr avg
cs 0.00% 97.60% 98.77% 99.53% 98.43% 98.58%
de 97.10% 0.00% 95.64% 97.44% 97.74% 96.98%
en 97.24% 95.94% 0.00% 94.14% 95.90% 95.80%
es 97.14% 97.44% 89.81% 0.00% 95.80% 95.05%
fr 97.20% 96.67% 91.18% 92.07% 0.00% 94.28%
avg 97.17% 96.91% 93.85% 95.80% 96.97% 96.14%
Input:
sh similarity_XLM-R_batch.sh 100 cls True
Output:
Confusion matrix:
langs cs de en es fr avg
cs 0.00% 97.54% 98.83% 99.40% 98.57% 98.58%
de 97.04% 0.00% 95.50% 97.20% 97.44% 96.79%
en 97.20% 95.30% 0.00% 93.94% 94.34% 95.20%
es 96.97% 95.94% 90.71% 0.00% 94.81% 94.61%
fr 97.10% 97.10% 92.77% 93.64% 0.00% 95.15%
avg 97.08% 96.47% 94.46% 96.05% 96.29% 96.07%
from multilingual_similarity_compare.
Related Issues (10)
- What metric is tracked in the table? HOT 1
- Why does XLM-R have low performance on this task?
- Add batch-size as a parameter in parser.args
- Add distilBERT with batches and test it HOT 3
- 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
- Arabic and Urdu HOT 3
- Benchmarks for LaBSE HOT 4
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