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  1. Health/1k/en-si/cross_domain_outputs/Bible/en-si.csv +2 -0
  2. Health/1k/en-si/cross_domain_outputs/Bible/encoded/test.bpe.en +0 -0
  3. Health/1k/en-si/cross_domain_outputs/Bible/encoded/test.bpe.si +0 -0
  4. Health/1k/en-si/cross_domain_outputs/Bible/outputs/output___en-si.hyp +0 -0
  5. Health/1k/en-si/cross_domain_outputs/Bible/outputs/output___en-si.txt +0 -0
  6. Health/1k/en-si/cross_domain_outputs/Bible/preprocess_en-si/dict.en.txt +0 -0
  7. Health/1k/en-si/cross_domain_outputs/Bible/preprocess_en-si/dict.si.txt +0 -0
  8. Health/1k/en-si/cross_domain_outputs/Bible/preprocess_en-si/preprocess.log +6 -0
  9. Health/1k/en-si/cross_domain_outputs/Bible/preprocess_en-si/test.en-si.en.bin +3 -0
  10. Health/1k/en-si/cross_domain_outputs/Bible/preprocess_en-si/test.en-si.en.idx +0 -0
  11. Health/1k/en-si/cross_domain_outputs/Bible/preprocess_en-si/test.en-si.si.bin +3 -0
  12. Health/1k/en-si/cross_domain_outputs/Bible/preprocess_en-si/test.en-si.si.idx +0 -0
  13. Health/1k/en-si/cross_domain_outputs/FloresPlus/en-si.csv +2 -0
  14. Health/1k/en-si/cross_domain_outputs/FloresPlus/encoded/test.bpe.en +0 -0
  15. Health/1k/en-si/cross_domain_outputs/FloresPlus/encoded/test.bpe.si +0 -0
  16. Health/1k/en-si/cross_domain_outputs/FloresPlus/outputs/output___en-si.hyp +0 -0
  17. Health/1k/en-si/cross_domain_outputs/FloresPlus/outputs/output___en-si.txt +0 -0
  18. Health/1k/en-si/cross_domain_outputs/FloresPlus/preprocess_en-si/dict.en.txt +0 -0
  19. Health/1k/en-si/cross_domain_outputs/FloresPlus/preprocess_en-si/dict.si.txt +0 -0
  20. Health/1k/en-si/cross_domain_outputs/FloresPlus/preprocess_en-si/preprocess.log +6 -0
  21. Health/1k/en-si/cross_domain_outputs/FloresPlus/preprocess_en-si/test.en-si.en.bin +3 -0
  22. Health/1k/en-si/cross_domain_outputs/FloresPlus/preprocess_en-si/test.en-si.en.idx +0 -0
  23. Health/1k/en-si/cross_domain_outputs/FloresPlus/preprocess_en-si/test.en-si.si.bin +3 -0
  24. Health/1k/en-si/cross_domain_outputs/FloresPlus/preprocess_en-si/test.en-si.si.idx +0 -0
  25. Health/1k/en-si/cross_domain_outputs/Government/en-si.csv +2 -0
  26. Health/1k/en-si/cross_domain_outputs/Government/encoded/test.bpe.en +0 -0
  27. Health/1k/en-si/cross_domain_outputs/Government/encoded/test.bpe.si +0 -0
  28. Health/1k/en-si/cross_domain_outputs/Government/outputs/output___en-si.hyp +0 -0
  29. Health/1k/en-si/cross_domain_outputs/Government/outputs/output___en-si.txt +0 -0
  30. Health/1k/en-si/cross_domain_outputs/Government/preprocess_en-si/dict.en.txt +0 -0
  31. Health/1k/en-si/cross_domain_outputs/Government/preprocess_en-si/dict.si.txt +0 -0
  32. Health/1k/en-si/cross_domain_outputs/Government/preprocess_en-si/preprocess.log +6 -0
  33. Health/1k/en-si/cross_domain_outputs/Government/preprocess_en-si/test.en-si.en.bin +3 -0
  34. Health/1k/en-si/cross_domain_outputs/Government/preprocess_en-si/test.en-si.en.idx +0 -0
  35. Health/1k/en-si/cross_domain_outputs/Government/preprocess_en-si/test.en-si.si.bin +3 -0
  36. Health/1k/en-si/cross_domain_outputs/Government/preprocess_en-si/test.en-si.si.idx +0 -0
  37. Health/1k/en-si/cross_domain_outputs/Health/en-si.csv +2 -0
  38. Health/1k/en-si/cross_domain_outputs/Health/encoded/test.bpe.en +0 -0
  39. Health/1k/en-si/cross_domain_outputs/Health/encoded/test.bpe.si +0 -0
  40. Health/1k/en-si/cross_domain_outputs/Health/outputs/output___en-si.hyp +0 -0
  41. Health/1k/en-si/cross_domain_outputs/Health/outputs/output___en-si.txt +0 -0
  42. Health/1k/en-si/cross_domain_outputs/Health/preprocess_en-si/dict.en.txt +0 -0
  43. Health/1k/en-si/cross_domain_outputs/Health/preprocess_en-si/dict.si.txt +0 -0
  44. Health/1k/en-si/cross_domain_outputs/Health/preprocess_en-si/preprocess.log +6 -0
  45. Health/1k/en-si/cross_domain_outputs/Health/preprocess_en-si/test.en-si.en.bin +3 -0
  46. Health/1k/en-si/cross_domain_outputs/Health/preprocess_en-si/test.en-si.en.idx +0 -0
  47. Health/1k/en-si/cross_domain_outputs/Health/preprocess_en-si/test.en-si.si.bin +3 -0
  48. Health/1k/en-si/cross_domain_outputs/Health/preprocess_en-si/test.en-si.si.idx +0 -0
  49. Health/1k/en-si/cross_domain_outputs/Jailbreak/en-si.csv +2 -0
  50. Health/1k/en-si/cross_domain_outputs/Jailbreak/encoded/test.bpe.en +0 -0
Health/1k/en-si/cross_domain_outputs/Bible/en-si.csv ADDED
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1
+ model,chrf_sacrebleuapi,chrfpp_sacrebleuapi,bleu_sacrebleuapi,spBLEU-flores101,spBLEU-flores200,comet
2
+ _,9.484918700991344,7.565540495219289,0.007595479554013201,0.022402724873046634,0.02491530428828895,0.3845692992955446
Health/1k/en-si/cross_domain_outputs/Bible/encoded/test.bpe.en ADDED
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Health/1k/en-si/cross_domain_outputs/Bible/encoded/test.bpe.si ADDED
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Health/1k/en-si/cross_domain_outputs/Bible/outputs/output___en-si.hyp ADDED
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Health/1k/en-si/cross_domain_outputs/Bible/outputs/output___en-si.txt ADDED
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Health/1k/en-si/cross_domain_outputs/Bible/preprocess_en-si/dict.en.txt ADDED
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Health/1k/en-si/cross_domain_outputs/Bible/preprocess_en-si/dict.si.txt ADDED
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Health/1k/en-si/cross_domain_outputs/Bible/preprocess_en-si/preprocess.log ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
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+ Namespace(aim_repo=None, aim_run_hash=None, align_suffix=None, alignfile=None, all_gather_list_size=16384, amp=False, amp_batch_retries=2, amp_init_scale=128, amp_scale_window=None, azureml_logging=False, bf16=False, bpe=None, cpu=False, criterion='cross_entropy', dataset_impl='mmap', destdir='../trained_models/Fairseq/Health/1k/en-si/cross_domain_outputs/Bible/preprocess_en-si/', dict_only=False, empty_cache_freq=0, fp16=False, fp16_init_scale=128, fp16_no_flatten_grads=False, fp16_scale_tolerance=0.0, fp16_scale_window=None, joined_dictionary=False, log_file=None, log_format=None, log_interval=100, lr_scheduler='fixed', memory_efficient_bf16=False, memory_efficient_fp16=False, min_loss_scale=0.0001, model_parallel_size=1, no_progress_bar=False, nwordssrc=-1, nwordstgt=-1, on_cpu_convert_precision=False, only_source=False, optimizer=None, padding_factor=8, plasma_path='/tmp/plasma', profile=False, quantization_config_path=None, reset_logging=False, scoring='bleu', seed=1, source_lang='en', srcdict='../trained_models/Fairseq/Health/1k/en-si/preprocess_en-si/dict.en.txt', suppress_crashes=False, target_lang='si', task='translation', tensorboard_logdir=None, testpref='../trained_models/Fairseq/Health/1k/en-si/cross_domain_outputs/Bible/encoded/test.bpe', tgtdict='../trained_models/Fairseq/Health/1k/en-si/preprocess_en-si/dict.si.txt', threshold_loss_scale=None, thresholdsrc=0, thresholdtgt=0, tokenizer=None, tpu=False, trainpref=None, use_plasma_view=False, user_dir=None, validpref=None, wandb_project=None, workers=16)
2
+ [en] Dictionary: 7088 types
3
+ [en] ../trained_models/Fairseq/Health/1k/en-si/cross_domain_outputs/Bible/encoded/test.bpe.en: 1000 sents, 48875 tokens, 46.4% replaced (by <unk>)
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+ [si] Dictionary: 7088 types
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+ [si] ../trained_models/Fairseq/Health/1k/en-si/cross_domain_outputs/Bible/encoded/test.bpe.si: 1000 sents, 53868 tokens, 66.7% replaced (by <unk>)
6
+ Wrote preprocessed data to ../trained_models/Fairseq/Health/1k/en-si/cross_domain_outputs/Bible/preprocess_en-si/
Health/1k/en-si/cross_domain_outputs/Bible/preprocess_en-si/test.en-si.en.bin ADDED
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Health/1k/en-si/cross_domain_outputs/Bible/preprocess_en-si/test.en-si.en.idx ADDED
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Health/1k/en-si/cross_domain_outputs/Bible/preprocess_en-si/test.en-si.si.idx ADDED
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Health/1k/en-si/cross_domain_outputs/FloresPlus/en-si.csv ADDED
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+ model,chrf_sacrebleuapi,chrfpp_sacrebleuapi,bleu_sacrebleuapi,spBLEU-flores101,spBLEU-flores200,comet
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+ _,10.810925615114762,8.857590487120092,0.05082821600925558,0.1251647931909245,0.1987741509494439,0.3695099887993967
Health/1k/en-si/cross_domain_outputs/FloresPlus/encoded/test.bpe.en ADDED
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Health/1k/en-si/cross_domain_outputs/FloresPlus/encoded/test.bpe.si ADDED
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Health/1k/en-si/cross_domain_outputs/FloresPlus/outputs/output___en-si.hyp ADDED
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Health/1k/en-si/cross_domain_outputs/FloresPlus/outputs/output___en-si.txt ADDED
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Health/1k/en-si/cross_domain_outputs/FloresPlus/preprocess_en-si/dict.en.txt ADDED
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Health/1k/en-si/cross_domain_outputs/FloresPlus/preprocess_en-si/dict.si.txt ADDED
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Health/1k/en-si/cross_domain_outputs/FloresPlus/preprocess_en-si/preprocess.log ADDED
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+ Namespace(aim_repo=None, aim_run_hash=None, align_suffix=None, alignfile=None, all_gather_list_size=16384, amp=False, amp_batch_retries=2, amp_init_scale=128, amp_scale_window=None, azureml_logging=False, bf16=False, bpe=None, cpu=False, criterion='cross_entropy', dataset_impl='mmap', destdir='../trained_models/Fairseq/Health/1k/en-si/cross_domain_outputs/FloresPlus/preprocess_en-si/', dict_only=False, empty_cache_freq=0, fp16=False, fp16_init_scale=128, fp16_no_flatten_grads=False, fp16_scale_tolerance=0.0, fp16_scale_window=None, joined_dictionary=False, log_file=None, log_format=None, log_interval=100, lr_scheduler='fixed', memory_efficient_bf16=False, memory_efficient_fp16=False, min_loss_scale=0.0001, model_parallel_size=1, no_progress_bar=False, nwordssrc=-1, nwordstgt=-1, on_cpu_convert_precision=False, only_source=False, optimizer=None, padding_factor=8, plasma_path='/tmp/plasma', profile=False, quantization_config_path=None, reset_logging=False, scoring='bleu', seed=1, source_lang='en', srcdict='../trained_models/Fairseq/Health/1k/en-si/preprocess_en-si/dict.en.txt', suppress_crashes=False, target_lang='si', task='translation', tensorboard_logdir=None, testpref='../trained_models/Fairseq/Health/1k/en-si/cross_domain_outputs/FloresPlus/encoded/test.bpe', tgtdict='../trained_models/Fairseq/Health/1k/en-si/preprocess_en-si/dict.si.txt', threshold_loss_scale=None, thresholdsrc=0, thresholdtgt=0, tokenizer=None, tpu=False, trainpref=None, use_plasma_view=False, user_dir=None, validpref=None, wandb_project=None, workers=16)
2
+ [en] Dictionary: 7088 types
3
+ [en] ../trained_models/Fairseq/Health/1k/en-si/cross_domain_outputs/FloresPlus/encoded/test.bpe.en: 1012 sents, 43089 tokens, 47.3% replaced (by <unk>)
4
+ [si] Dictionary: 7088 types
5
+ [si] ../trained_models/Fairseq/Health/1k/en-si/cross_domain_outputs/FloresPlus/encoded/test.bpe.si: 1012 sents, 43735 tokens, 51.2% replaced (by <unk>)
6
+ Wrote preprocessed data to ../trained_models/Fairseq/Health/1k/en-si/cross_domain_outputs/FloresPlus/preprocess_en-si/
Health/1k/en-si/cross_domain_outputs/FloresPlus/preprocess_en-si/test.en-si.en.bin ADDED
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Health/1k/en-si/cross_domain_outputs/FloresPlus/preprocess_en-si/test.en-si.en.idx ADDED
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Health/1k/en-si/cross_domain_outputs/FloresPlus/preprocess_en-si/test.en-si.si.idx ADDED
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Health/1k/en-si/cross_domain_outputs/Government/en-si.csv ADDED
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+ model,chrf_sacrebleuapi,chrfpp_sacrebleuapi,bleu_sacrebleuapi,spBLEU-flores101,spBLEU-flores200,comet
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+ _,12.843001729444929,10.533315521502317,0.06860353228669117,0.17991274575352503,0.5679371062836159,0.41095161241292955
Health/1k/en-si/cross_domain_outputs/Government/encoded/test.bpe.en ADDED
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Health/1k/en-si/cross_domain_outputs/Government/encoded/test.bpe.si ADDED
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Health/1k/en-si/cross_domain_outputs/Government/outputs/output___en-si.hyp ADDED
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Health/1k/en-si/cross_domain_outputs/Government/outputs/output___en-si.txt ADDED
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Health/1k/en-si/cross_domain_outputs/Government/preprocess_en-si/dict.en.txt ADDED
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Health/1k/en-si/cross_domain_outputs/Government/preprocess_en-si/dict.si.txt ADDED
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Health/1k/en-si/cross_domain_outputs/Government/preprocess_en-si/preprocess.log ADDED
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+ Namespace(aim_repo=None, aim_run_hash=None, align_suffix=None, alignfile=None, all_gather_list_size=16384, amp=False, amp_batch_retries=2, amp_init_scale=128, amp_scale_window=None, azureml_logging=False, bf16=False, bpe=None, cpu=False, criterion='cross_entropy', dataset_impl='mmap', destdir='../trained_models/Fairseq/Health/1k/en-si/cross_domain_outputs/Government/preprocess_en-si/', dict_only=False, empty_cache_freq=0, fp16=False, fp16_init_scale=128, fp16_no_flatten_grads=False, fp16_scale_tolerance=0.0, fp16_scale_window=None, joined_dictionary=False, log_file=None, log_format=None, log_interval=100, lr_scheduler='fixed', memory_efficient_bf16=False, memory_efficient_fp16=False, min_loss_scale=0.0001, model_parallel_size=1, no_progress_bar=False, nwordssrc=-1, nwordstgt=-1, on_cpu_convert_precision=False, only_source=False, optimizer=None, padding_factor=8, plasma_path='/tmp/plasma', profile=False, quantization_config_path=None, reset_logging=False, scoring='bleu', seed=1, source_lang='en', srcdict='../trained_models/Fairseq/Health/1k/en-si/preprocess_en-si/dict.en.txt', suppress_crashes=False, target_lang='si', task='translation', tensorboard_logdir=None, testpref='../trained_models/Fairseq/Health/1k/en-si/cross_domain_outputs/Government/encoded/test.bpe', tgtdict='../trained_models/Fairseq/Health/1k/en-si/preprocess_en-si/dict.si.txt', threshold_loss_scale=None, thresholdsrc=0, thresholdtgt=0, tokenizer=None, tpu=False, trainpref=None, use_plasma_view=False, user_dir=None, validpref=None, wandb_project=None, workers=16)
2
+ [en] Dictionary: 7088 types
3
+ [en] ../trained_models/Fairseq/Health/1k/en-si/cross_domain_outputs/Government/encoded/test.bpe.en: 1000 sents, 35198 tokens, 41.4% replaced (by <unk>)
4
+ [si] Dictionary: 7088 types
5
+ [si] ../trained_models/Fairseq/Health/1k/en-si/cross_domain_outputs/Government/encoded/test.bpe.si: 1000 sents, 33284 tokens, 42.0% replaced (by <unk>)
6
+ Wrote preprocessed data to ../trained_models/Fairseq/Health/1k/en-si/cross_domain_outputs/Government/preprocess_en-si/
Health/1k/en-si/cross_domain_outputs/Government/preprocess_en-si/test.en-si.en.bin ADDED
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Health/1k/en-si/cross_domain_outputs/Health/en-si.csv ADDED
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+ model,chrf_sacrebleuapi,chrfpp_sacrebleuapi,bleu_sacrebleuapi,spBLEU-flores101,spBLEU-flores200,comet
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+ _,13.289531689068928,11.164648713002292,0.28538859573878894,0.7001542185103758,1.0721436004557474,0.3988866438524107
Health/1k/en-si/cross_domain_outputs/Health/encoded/test.bpe.en ADDED
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Health/1k/en-si/cross_domain_outputs/Health/encoded/test.bpe.si ADDED
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Health/1k/en-si/cross_domain_outputs/Health/outputs/output___en-si.hyp ADDED
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Health/1k/en-si/cross_domain_outputs/Health/outputs/output___en-si.txt ADDED
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Health/1k/en-si/cross_domain_outputs/Health/preprocess_en-si/dict.en.txt ADDED
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Health/1k/en-si/cross_domain_outputs/Health/preprocess_en-si/dict.si.txt ADDED
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Health/1k/en-si/cross_domain_outputs/Health/preprocess_en-si/preprocess.log ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ Namespace(aim_repo=None, aim_run_hash=None, align_suffix=None, alignfile=None, all_gather_list_size=16384, amp=False, amp_batch_retries=2, amp_init_scale=128, amp_scale_window=None, azureml_logging=False, bf16=False, bpe=None, cpu=False, criterion='cross_entropy', dataset_impl='mmap', destdir='../trained_models/Fairseq/Health/1k/en-si/cross_domain_outputs/Health/preprocess_en-si/', dict_only=False, empty_cache_freq=0, fp16=False, fp16_init_scale=128, fp16_no_flatten_grads=False, fp16_scale_tolerance=0.0, fp16_scale_window=None, joined_dictionary=False, log_file=None, log_format=None, log_interval=100, lr_scheduler='fixed', memory_efficient_bf16=False, memory_efficient_fp16=False, min_loss_scale=0.0001, model_parallel_size=1, no_progress_bar=False, nwordssrc=-1, nwordstgt=-1, on_cpu_convert_precision=False, only_source=False, optimizer=None, padding_factor=8, plasma_path='/tmp/plasma', profile=False, quantization_config_path=None, reset_logging=False, scoring='bleu', seed=1, source_lang='en', srcdict='../trained_models/Fairseq/Health/1k/en-si/preprocess_en-si/dict.en.txt', suppress_crashes=False, target_lang='si', task='translation', tensorboard_logdir=None, testpref='../trained_models/Fairseq/Health/1k/en-si/cross_domain_outputs/Health/encoded/test.bpe', tgtdict='../trained_models/Fairseq/Health/1k/en-si/preprocess_en-si/dict.si.txt', threshold_loss_scale=None, thresholdsrc=0, thresholdtgt=0, tokenizer=None, tpu=False, trainpref=None, use_plasma_view=False, user_dir=None, validpref=None, wandb_project=None, workers=16)
2
+ [en] Dictionary: 7088 types
3
+ [en] ../trained_models/Fairseq/Health/1k/en-si/cross_domain_outputs/Health/encoded/test.bpe.en: 890 sents, 39630 tokens, 42.5% replaced (by <unk>)
4
+ [si] Dictionary: 7088 types
5
+ [si] ../trained_models/Fairseq/Health/1k/en-si/cross_domain_outputs/Health/encoded/test.bpe.si: 890 sents, 44407 tokens, 39.6% replaced (by <unk>)
6
+ Wrote preprocessed data to ../trained_models/Fairseq/Health/1k/en-si/cross_domain_outputs/Health/preprocess_en-si/
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