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lang-uk
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flair-uk-backward

Flair
Ukrainian
generic
text2text-generation
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Dmitry Chaplinsky commited on
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1 Parent(s): 2abbc46

First release

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Files changed (6) hide show
  1. README.md +33 -0
  2. best-lm.pt +3 -0
  3. flair_dictionary.pkl +3 -0
  4. loss.txt +336 -0
  5. pipeline.py +22 -0
  6. requirements.txt +1 -0
README.md CHANGED
@@ -1,3 +1,36 @@
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  ---
 
 
 
 
 
 
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  license: mit
 
 
 
 
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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+ language:
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+ - uk
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+ tags:
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+ - text2text-generation
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+ - flair
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+ library_name: generic
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  license: mit
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+ metrics:
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+ - perplexity
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+ datasets:
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+ - ubertext2.0
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+ widget:
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+ - text: "підсумував він."
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+ - text: "Україна переможе!"
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  ---
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+
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+ # Ukrainian flair embeddings (backward)
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+
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+ Trained for 12+ epochs on the texts from ubertext2.0 (WIP).
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+ The characters dictionary used for training is in `flair_dictionary.pkl` file
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+
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+ For more information on flair embeddings see [the article](https://github.com/flairNLP/flair/blob/master/resources/docs/embeddings/FLAIR_EMBEDDINGS.md) or the paper below:
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+
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+
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+ ```bibtex
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+ @inproceedings{akbik2018coling,
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+ title={Contextual String Embeddings for Sequence Labeling},
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+ author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland},
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+ booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics},
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+ pages = {1638--1649},
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+ year = {2018}
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+ }
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+ ```
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+
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+ Copyright: Dmytro Chaplynskyi, [lang-uk](https://lang.org.ua) project, 2022
best-lm.pt ADDED
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+ oid sha256:5a810aa30566d93280cdb89385000d44cd6320d559710784072ade264200620a
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+ size 22791455
flair_dictionary.pkl ADDED
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+ oid sha256:2125c32d2db5fb79676a8a6f087b19e9c3b788cb19b87073423e31e176d1fe24
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+ size 11900
loss.txt ADDED
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155
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157
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+ | end of split 27 / 28 | epoch 6 | time: 3802.33s | valid loss 1.0645 | valid ppl 2.8994 | learning rate 20.0000
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+ | end of split 28 / 28 | epoch 6 | time: 3797.75s | valid loss 1.0645 | valid ppl 2.8994 | learning rate 20.0000
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+ | end of split 16 / 28 | epoch 8 | time: 3792.33s | valid loss 1.0549 | valid ppl 2.8717 | learning rate 5.0000
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+ | end of split 23 / 28 | epoch 8 | time: 3797.48s | valid loss 1.0545 | valid ppl 2.8705 | learning rate 5.0000
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+ | end of split 24 / 28 | epoch 8 | time: 3826.24s | valid loss 1.0545 | valid ppl 2.8705 | learning rate 5.0000
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+ | end of split 26 / 28 | epoch 8 | time: 3803.96s | valid loss 1.0545 | valid ppl 2.8705 | learning rate 5.0000
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+ | end of split 27 / 28 | epoch 8 | time: 3802.34s | valid loss 1.0543 | valid ppl 2.8700 | learning rate 5.0000
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+ | end of split 1 / 28 | epoch 9 | time: 3798.65s | valid loss 1.0542 | valid ppl 2.8697 | learning rate 5.0000
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+ | end of split 10 / 28 | epoch 9 | time: 3798.30s | valid loss 1.0537 | valid ppl 2.8683 | learning rate 5.0000
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+ | end of split 11 / 28 | epoch 9 | time: 3794.81s | valid loss 1.0537 | valid ppl 2.8682 | learning rate 5.0000
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+ | end of split 12 / 28 | epoch 9 | time: 3794.04s | valid loss 1.0537 | valid ppl 2.8682 | learning rate 5.0000
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+ | end of split 13 / 28 | epoch 9 | time: 3798.63s | valid loss 1.0537 | valid ppl 2.8683 | learning rate 5.0000
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+ | end of split 14 / 28 | epoch 9 | time: 3797.90s | valid loss 1.0535 | valid ppl 2.8678 | learning rate 5.0000
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+ | end of split 15 / 28 | epoch 9 | time: 3796.44s | valid loss 1.0536 | valid ppl 2.8680 | learning rate 5.0000
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+ | end of split 16 / 28 | epoch 9 | time: 3798.41s | valid loss 1.0536 | valid ppl 2.8678 | learning rate 5.0000
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+ | end of split 17 / 28 | epoch 9 | time: 3799.93s | valid loss 1.0535 | valid ppl 2.8676 | learning rate 5.0000
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+ | end of split 18 / 28 | epoch 9 | time: 3803.40s | valid loss 1.0534 | valid ppl 2.8673 | learning rate 5.0000
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+ | end of split 19 / 28 | epoch 9 | time: 3807.52s | valid loss 1.0537 | valid ppl 2.8683 | learning rate 5.0000
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+ | end of split 20 / 28 | epoch 9 | time: 3807.58s | valid loss 1.0534 | valid ppl 2.8673 | learning rate 5.0000
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+ | end of split 21 / 28 | epoch 9 | time: 3799.18s | valid loss 1.0533 | valid ppl 2.8672 | learning rate 5.0000
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+ | end of split 22 / 28 | epoch 9 | time: 3800.62s | valid loss 1.0532 | valid ppl 2.8668 | learning rate 5.0000
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+ | end of split 23 / 28 | epoch 9 | time: 3796.79s | valid loss 1.0532 | valid ppl 2.8667 | learning rate 5.0000
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+ | end of split 24 / 28 | epoch 9 | time: 1097.06s | valid loss 1.0532 | valid ppl 2.8669 | learning rate 5.0000
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+ | end of split 25 / 28 | epoch 9 | time: 3795.86s | valid loss 1.0532 | valid ppl 2.8669 | learning rate 5.0000
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+ | end of split 26 / 28 | epoch 9 | time: 3803.14s | valid loss 1.0531 | valid ppl 2.8665 | learning rate 5.0000
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+ | end of split 27 / 28 | epoch 9 | time: 3798.92s | valid loss 1.0530 | valid ppl 2.8663 | learning rate 5.0000
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+ | end of split 28 / 28 | epoch 9 | time: 3799.90s | valid loss 1.0530 | valid ppl 2.8663 | learning rate 5.0000
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+ | end of split 1 / 28 | epoch 10 | time: 3798.57s | valid loss 1.0530 | valid ppl 2.8662 | learning rate 5.0000
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+ | end of split 2 / 28 | epoch 10 | time: 3798.13s | valid loss 1.0529 | valid ppl 2.8661 | learning rate 5.0000
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+ | end of split 3 / 28 | epoch 10 | time: 3799.82s | valid loss 1.0530 | valid ppl 2.8662 | learning rate 5.0000
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+ | end of split 6 / 28 | epoch 10 | time: 3798.08s | valid loss 1.0528 | valid ppl 2.8656 | learning rate 5.0000
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+ | end of split 8 / 28 | epoch 10 | time: 3800.94s | valid loss 1.0526 | valid ppl 2.8652 | learning rate 5.0000
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+ | end of split 9 / 28 | epoch 10 | time: 3801.43s | valid loss 1.0529 | valid ppl 2.8659 | learning rate 5.0000
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+ | end of split 10 / 28 | epoch 10 | time: 3798.47s | valid loss 1.0526 | valid ppl 2.8652 | learning rate 5.0000
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+ | end of split 11 / 28 | epoch 10 | time: 3803.15s | valid loss 1.0526 | valid ppl 2.8650 | learning rate 5.0000
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+ | end of split 12 / 28 | epoch 10 | time: 3800.32s | valid loss 1.0526 | valid ppl 2.8650 | learning rate 5.0000
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+ | end of split 13 / 28 | epoch 10 | time: 3802.61s | valid loss 1.0525 | valid ppl 2.8647 | learning rate 5.0000
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+ | end of split 14 / 28 | epoch 10 | time: 3799.08s | valid loss 1.0525 | valid ppl 2.8648 | learning rate 5.0000
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+ | end of split 15 / 28 | epoch 10 | time: 3801.19s | valid loss 1.0525 | valid ppl 2.8647 | learning rate 5.0000
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+ | end of split 16 / 28 | epoch 10 | time: 3801.20s | valid loss 1.0524 | valid ppl 2.8646 | learning rate 5.0000
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+ | end of split 17 / 28 | epoch 10 | time: 3802.37s | valid loss 1.0524 | valid ppl 2.8645 | learning rate 5.0000
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+ | end of split 18 / 28 | epoch 10 | time: 3805.85s | valid loss 1.0523 | valid ppl 2.8643 | learning rate 5.0000
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+ | end of split 19 / 28 | epoch 10 | time: 3804.15s | valid loss 1.0524 | valid ppl 2.8644 | learning rate 5.0000
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+ | end of split 20 / 28 | epoch 10 | time: 3806.41s | valid loss 1.0523 | valid ppl 2.8642 | learning rate 5.0000
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+ | end of split 21 / 28 | epoch 10 | time: 3809.13s | valid loss 1.0522 | valid ppl 2.8639 | learning rate 5.0000
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+ | end of split 22 / 28 | epoch 10 | time: 3798.99s | valid loss 1.0523 | valid ppl 2.8641 | learning rate 5.0000
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+ | end of split 23 / 28 | epoch 10 | time: 3802.76s | valid loss 1.0522 | valid ppl 2.8639 | learning rate 5.0000
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+ | end of split 24 / 28 | epoch 10 | time: 3805.95s | valid loss 1.0522 | valid ppl 2.8639 | learning rate 5.0000
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+ | end of split 25 / 28 | epoch 10 | time: 3803.67s | valid loss 1.0522 | valid ppl 2.8639 | learning rate 5.0000
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+ | end of split 26 / 28 | epoch 10 | time: 3802.75s | valid loss 1.0521 | valid ppl 2.8635 | learning rate 5.0000
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+ | end of split 27 / 28 | epoch 10 | time: 3804.63s | valid loss 1.0520 | valid ppl 2.8633 | learning rate 5.0000
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+ | end of split 28 / 28 | epoch 10 | time: 1097.97s | valid loss 1.0520 | valid ppl 2.8634 | learning rate 5.0000
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+ | end of split 1 / 28 | epoch 11 | time: 3793.51s | valid loss 1.0520 | valid ppl 2.8634 | learning rate 5.0000
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+ | end of split 2 / 28 | epoch 11 | time: 3802.15s | valid loss 1.0520 | valid ppl 2.8633 | learning rate 5.0000
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+ | end of split 3 / 28 | epoch 11 | time: 3801.09s | valid loss 1.0518 | valid ppl 2.8629 | learning rate 5.0000
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+ | end of split 4 / 28 | epoch 11 | time: 3803.88s | valid loss 1.0518 | valid ppl 2.8629 | learning rate 5.0000
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+ | end of split 6 / 28 | epoch 11 | time: 3803.50s | valid loss 1.0518 | valid ppl 2.8629 | learning rate 5.0000
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+ | end of split 7 / 28 | epoch 11 | time: 3798.93s | valid loss 1.0518 | valid ppl 2.8627 | learning rate 5.0000
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+ | end of split 9 / 28 | epoch 11 | time: 3797.52s | valid loss 1.0517 | valid ppl 2.8624 | learning rate 5.0000
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+ | end of split 10 / 28 | epoch 11 | time: 3806.92s | valid loss 1.0518 | valid ppl 2.8627 | learning rate 5.0000
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+ | end of split 11 / 28 | epoch 11 | time: 3806.04s | valid loss 1.0516 | valid ppl 2.8622 | learning rate 5.0000
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+ | end of split 12 / 28 | epoch 11 | time: 3801.39s | valid loss 1.0519 | valid ppl 2.8632 | learning rate 5.0000
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+ | end of split 13 / 28 | epoch 11 | time: 3801.24s | valid loss 1.0516 | valid ppl 2.8622 | learning rate 5.0000
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+ | end of split 14 / 28 | epoch 11 | time: 3804.44s | valid loss 1.0515 | valid ppl 2.8620 | learning rate 5.0000
295
+ | end of split 15 / 28 | epoch 11 | time: 3801.34s | valid loss 1.0515 | valid ppl 2.8620 | learning rate 5.0000
296
+ | end of split 16 / 28 | epoch 11 | time: 3803.14s | valid loss 1.0514 | valid ppl 2.8618 | learning rate 5.0000
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+ | end of split 17 / 28 | epoch 11 | time: 3801.11s | valid loss 1.0514 | valid ppl 2.8617 | learning rate 5.0000
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+ | end of split 18 / 28 | epoch 11 | time: 3804.58s | valid loss 1.0513 | valid ppl 2.8613 | learning rate 5.0000
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+ | end of split 19 / 28 | epoch 11 | time: 3796.04s | valid loss 1.0513 | valid ppl 2.8615 | learning rate 5.0000
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+ | end of split 20 / 28 | epoch 11 | time: 3797.12s | valid loss 1.0512 | valid ppl 2.8611 | learning rate 5.0000
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+ | end of split 21 / 28 | epoch 11 | time: 1097.96s | valid loss 1.0512 | valid ppl 2.8612 | learning rate 5.0000
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+ | end of split 22 / 28 | epoch 11 | time: 3800.79s | valid loss 1.0513 | valid ppl 2.8613 | learning rate 5.0000
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+ | end of split 23 / 28 | epoch 11 | time: 3801.51s | valid loss 1.0518 | valid ppl 2.8629 | learning rate 5.0000
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+ | end of split 24 / 28 | epoch 11 | time: 3798.63s | valid loss 1.0513 | valid ppl 2.8614 | learning rate 5.0000
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+ | end of split 25 / 28 | epoch 11 | time: 3796.99s | valid loss 1.0512 | valid ppl 2.8612 | learning rate 5.0000
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+ | end of split 26 / 28 | epoch 11 | time: 3797.77s | valid loss 1.0512 | valid ppl 2.8610 | learning rate 5.0000
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+ | end of split 27 / 28 | epoch 11 | time: 3797.73s | valid loss 1.0512 | valid ppl 2.8610 | learning rate 5.0000
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+ | end of split 28 / 28 | epoch 11 | time: 3800.03s | valid loss 1.0511 | valid ppl 2.8607 | learning rate 5.0000
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+ | end of split 1 / 28 | epoch 12 | time: 3796.72s | valid loss 1.0511 | valid ppl 2.8609 | learning rate 5.0000
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+ | end of split 2 / 28 | epoch 12 | time: 1097.45s | valid loss 1.0510 | valid ppl 2.8604 | learning rate 5.0000
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+ | end of split 12 / 28 | epoch 12 | time: 3804.49s | valid loss 1.0507 | valid ppl 2.8598 | learning rate 5.0000
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+ | end of split 14 / 28 | epoch 12 | time: 3799.49s | valid loss 1.0506 | valid ppl 2.8594 | learning rate 5.0000
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+ | end of split 15 / 28 | epoch 12 | time: 3807.23s | valid loss 1.0506 | valid ppl 2.8595 | learning rate 5.0000
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+ | end of split 16 / 28 | epoch 12 | time: 3798.38s | valid loss 1.0506 | valid ppl 2.8592 | learning rate 5.0000
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+ | end of split 17 / 28 | epoch 12 | time: 3806.09s | valid loss 1.0506 | valid ppl 2.8595 | learning rate 5.0000
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+ | end of split 19 / 28 | epoch 12 | time: 3800.94s | valid loss 1.0505 | valid ppl 2.8589 | learning rate 5.0000
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+ | end of split 20 / 28 | epoch 12 | time: 3796.71s | valid loss 1.0505 | valid ppl 2.8590 | learning rate 5.0000
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+ | end of split 25 / 28 | epoch 12 | time: 3798.55s | valid loss 1.0502 | valid ppl 2.8582 | learning rate 5.0000
334
+ | end of split 26 / 28 | epoch 12 | time: 3797.73s | valid loss 1.0502 | valid ppl 2.8582 | learning rate 5.0000
335
+ | end of split 27 / 28 | epoch 12 | time: 3798.53s | valid loss 1.0502 | valid ppl 2.8582 | learning rate 5.0000
336
+ | end of split 28 / 28 | epoch 12 | time: 3797.17s | valid loss 1.0502 | valid ppl 2.8582 | learning rate 5.0000
pipeline.py ADDED
@@ -0,0 +1,22 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from typing import List, Dict
2
+ from flair.models.language_model import LanguageModel
3
+
4
+
5
+ class PreTrainedPipeline:
6
+ def __init__(self, path=""):
7
+ from huggingface_hub import hf_hub_download
8
+
9
+ self.model = LanguageModel.load_language_model(
10
+ hf_hub_download(repo_id="dchaplinsky/flair-uk-backward", filename="best-lm.pt")
11
+ )
12
+
13
+ def __call__(self, inputs: str) -> List[Dict]:
14
+ """
15
+ Args:
16
+ inputs (:obj:`str`):
17
+ a string containing some text
18
+ Return:
19
+ A :obj:`str`
20
+ """
21
+ inputs = inputs.strip()
22
+ return [{"generated_text": self.model.generate_text(inputs[::-1], temperature=0.5)[0]}]
requirements.txt ADDED
@@ -0,0 +1 @@
 
 
1
+ flair