Comments (2)
Solved! Issue in the sum() when multiplying the attention_mask with the input_embedding and averaging over the true tokens only (not the paddings)
from multilingual_similarity_compare.
Output now that issue is solved with same input:
Confusion matrix:
langs cs de en es fr avg
cs 0.00% 84.18% 85.98% 82.65% 89.08% 85.47%
de 74.79% 0.00% 61.51% 65.23% 67.27% 67.20%
en 75.72% 55.04% 0.00% 26.71% 31.60% 47.27%
es 78.39% 81.75% 37.40% 0.00% 55.54% 63.27%
fr 75.36% 63.20% 35.16% 34.33% 0.00% 52.01%
avg 76.07% 71.05% 55.01% 52.23% 60.87% 63.05%
Input:
sh similarity_distilBERT_batch.sh 100 mean True
Output:
Confusion matrix:
langs cs de en es fr avg
cs 0.00% 84.85% 87.71% 86.85% 88.71% 87.03%
de 75.06% 0.00% 64.20% 70.36% 69.13% 69.69%
en 74.16% 54.28% 0.00% 24.84% 29.14% 45.60%
es 75.99% 81.62% 40.86% 0.00% 50.95% 62.35%
fr 90.08% 78.16% 37.53% 56.88% 0.00% 65.66%
avg 78.82% 74.73% 57.58% 59.73% 59.48% 66.07%
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
- Add distilBERT with batches and test it HOT 3
- Time comparison between GPU and CPU HOT 1
- 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.