metadata
license: apache-2.0
pegasus-indonesian-base_pretrained
Github : PegasusAnthony
This model is a pretrained version of pegasus-indonesian-base_finetune on kaggle id news 2017, CC_News_id, and OSCAR_2201.
It achieves the following results on the evaluation set:
- Train Loss: 2.34832262992858
- Train Accuracy: 0.262173235416412
- Validation Loss: 2.34894156455993
- Validation Accuracy: 0.266122311353683
- Train Lr: 0.000136618677061051
- Epoch: 40
Intended uses & limitations
This model is uncased, can't read special characters except "," and ".", having hard time understanding numbers, and performance only tested on news article text.
Training and evaluation data
Pretrain dataset:
Training procedure
For replication, go to GitHub page
Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adafactor', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 0.005, 'beta_2_decay': -0.8, 'epsilon_1': 1e-30, 'epsilon_2': 0.001, 'clip_threshold': 1.0, 'relative_step': True}
- training_precision: float32
configuration.vocab_size = 32103
configuration.d_model = 512
configuration.dropout = 0.15
configuration.decoder_attention_heads = 8
configuration.decoder_layers = 12
configuration.decoder_ffn_dim = 3072
configuration.encoder_attention_heads = 8
configuration.encoder_layers = 12
configuration.encoder_ffn_dim = 3072
Training results
Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Train Lr | Epoch |
---|---|---|---|---|---|
4.1939034461975 | 0.145276814699172 | 3.39564657211303 | 0.186678826808929 | 0.00499999988824129 | 1 |
3.13256049156188 | 0.208270609378814 | 2.82256889343261 | 0.233325317502021 | 0.00499999988824129 | 2 |
2.84938621520996 | 0.229006066918373 | 2.72168040275573 | 0.23955675959587 | 0.00499999988824129 | 3 |
2.76001143455505 | 0.234559893608093 | 2.65143990516662 | 0.243813350796699 | 0.00499999988824129 | 4 |
2.70404982566833 | 0.238061532378196 | 2.6107530593872 | 0.246574580669403 | 0.00452418718487024 | 5 |
2.6638650894165 | 0.240613579750061 | 2.57847166061401 | 0.248678594827651 | 0.00409365398809313 | 6 |
2.63293719291687 | 0.242613524198532 | 2.55772447586059 | 0.250325441360473 | 0.00370409130118787 | 7 |
2.60750746726989 | 0.244251564145088 | 2.53469848632812 | 0.251805543899536 | 0.00335160037502646 | 8 |
2.58670353889465 | 0.245637223124504 | 2.51883554458618 | 0.253003656864166 | 0.00303265335969626 | 9 |
2.56865572929382 | 0.24682830274105 | 2.49989652633666 | 0.254459708929061 | 0.00274405837990343 | 10 |
2.55285787582397 | 0.247884958982467 | 2.50092124938964 | 0.254229605197906 | 0.00248292670585215 | 11 |
2.53919672966003 | 0.248811900615692 | 2.47859454154968 | 0.255691051483154 | 0.00224664504639804 | 12 |
2.52694725990295 | 0.249630719423294 | 2.46921157836914 | 0.25649145245552 | 0.00203284854069352 | 13 |
2.51587128639221 | 0.250377029180526 | 2.46414017677307 | 0.257025629281997 | 0.0018393974751234 | 14 |
2.50599193572998 | 0.251064419746398 | 2.4557819366455 | 0.257613778114318 | 0.00166435563005507 | 15 |
2.49690246582031 | 0.251682370901107 | 2.44843244552612 | 0.258032590150833 | 0.00150597130414098 | 16 |
2.48859119415283 | 0.252267301082611 | 2.43858122825622 | 0.258764535188674 | 0.00136265915352851 | 17 |
2.48097324371337 | 0.252792716026306 | 2.43251323699951 | 0.259270757436752 | 0.00123298505786806 | 18 |
2.47009921073913 | 0.253554105758667 | 2.43577146530151 | 0.258938610553741 | 0.00111565098632127 | 19 |
2.45849394798278 | 0.254375785589218 | 2.42337107658386 | 0.260090589523315 | 0.00100948277395218 | 20 |
2.44776940345764 | 0.255127549171447 | 2.41147446632385 | 0.260682851076126 | 0.000913417781703174 | 21 |
2.43759155273437 | 0.255834341049194 | 2.41405510902404 | 0.260819226503372 | 0.000826494593638926 | 22 |
2.42819571495056 | 0.256486028432846 | 2.40314364433288 | 0.26152354478836 | 0.000747843238059431 | 23 |
2.41974592208862 | 0.257094115018844 | 2.39181518554687 | 0.262460082769393 | 0.000676676572766155 | 24 |
2.41181802749633 | 0.257666647434234 | 2.3825569152832 | 0.263035386800766 | 0.000612282310612499 | 25 |
2.4044873714447 | 0.258173674345016 | 2.37829279899597 | 0.263585090637207 | 0.000554015976376831 | 26 |
2.39774870872497 | 0.258645176887512 | 2.37718510627746 | 0.263547003269195 | 0.000501294387504458 | 27 |
2.39184403419494 | 0.259076595306396 | 2.37379837036132 | 0.264020860195159 | 0.00045358992065303 | 28 |
2.38593125343322 | 0.259495466947555 | 2.37083029747009 | 0.264293819665908 | 0.000410425127483904 | 29 |
2.38093471527099 | 0.259853214025497 | 2.36486291885375 | 0.264451295137405 | 0.000371368019841611 | 30 |
2.37621307373046 | 0.260185241699218 | 2.36547923088073 | 0.264706671237945 | 0.000336027675075456 | 31 |
2.37177920341491 | 0.260504961013793 | 2.3609721660614 | 0.264981210231781 | 0.000304050423437729 | 32 |
2.3679461479187 | 0.260774314403533 | 2.36445379257202 | 0.264800041913986 | 0.000275116210104897 | 33 |
2.3643410205841 | 0.261037856340408 | 2.3573100566864 | 0.265379041433334 | 0.000248935451963916 | 34 |
2.36092805862426 | 0.261268675327301 | 2.36105728149414 | 0.264868646860122 | 0.000225246112677268 | 35 |
2.35798692703247 | 0.261485010385513 | 2.35409832000732 | 0.265503793954849 | 0.000203811112442053 | 36 |
2.35523629188537 | 0.26168617606163 | 2.35252356529235 | 0.265713244676589 | 0.000184415926923975 | 37 |
2.35284709930419 | 0.261859744787216 | 2.35101222991943 | 0.265856444835662 | 0.000166866433573886 | 38 |
2.35047316551208 | 0.262033462524414 | 2.34698224067687 | 0.266099989414215 | 0.000150986990774981 | 39 |
2.34832262992858 | 0.262173235416412 | 2.34894156455993 | 0.266122311353683 | 0.000136618677061051 | 40 |
Framework versions
- Transformers 4.30.2
- TensorFlow 2.12.0
- Datasets 2.13.1
- Tokenizers 0.13.3
Special Thanks
Research supported with Cloud TPUs from Google’s TPU Research Cloud (TRC)