|
2023-01-08 08:23:21,495 ---------------------------------------------------------------------------------------------------- |
|
2023-01-08 08:23:21,498 Model: "SequenceTagger( |
|
(embeddings): TransformerWordEmbeddings( |
|
(model): BertModel( |
|
(embeddings): BertEmbeddings( |
|
(word_embeddings): Embedding(100000, 768, padding_idx=0) |
|
(position_embeddings): Embedding(512, 768) |
|
(token_type_embeddings): Embedding(2, 768) |
|
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
|
|
(pooler): BertPooler( |
|
(dense): Linear(in_features=768, out_features=768, bias=True) |
|
(activation): Tanh() |
|
) |
|
) |
|
(word_dropout): WordDropout(p=0.05) |
|
(locked_dropout): LockedDropout(p=0.5) |
|
(linear): Linear(in_features=768, out_features=18, bias=True) |
|
(beta): 1.0 |
|
(weights): None |
|
(weight_tensor) None |
|
)" |
|
2023-01-08 08:23:21,500 ---------------------------------------------------------------------------------------------------- |
|
2023-01-08 08:23:21,505 Corpus: "Corpus: 26116 train + 2902 dev + 1572 test sentences" |
|
2023-01-08 08:23:21,506 ---------------------------------------------------------------------------------------------------- |
|
2023-01-08 08:23:21,506 Parameters: |
|
2023-01-08 08:23:21,507 - learning_rate: "5e-06" |
|
2023-01-08 08:23:21,509 - mini_batch_size: "4" |
|
2023-01-08 08:23:21,510 - patience: "3" |
|
2023-01-08 08:23:21,512 - anneal_factor: "0.5" |
|
2023-01-08 08:23:21,513 - max_epochs: "25" |
|
2023-01-08 08:23:21,513 - shuffle: "False" |
|
2023-01-08 08:23:21,514 - train_with_dev: "False" |
|
2023-01-08 08:23:21,515 - batch_growth_annealing: "False" |
|
2023-01-08 08:23:21,516 ---------------------------------------------------------------------------------------------------- |
|
2023-01-08 08:23:21,517 Model training base path: "resources/taggers/NSURL-2019_25epochs" |
|
2023-01-08 08:23:21,518 ---------------------------------------------------------------------------------------------------- |
|
2023-01-08 08:23:21,519 Device: cuda:0 |
|
2023-01-08 08:23:21,519 ---------------------------------------------------------------------------------------------------- |
|
2023-01-08 08:23:21,520 Embeddings storage mode: none |
|
2023-01-08 18:00:13,690 ---------------------------------------------------------------------------------------------------- |
|
2023-01-08 18:02:30,863 epoch 25 - iter 652/6529 - loss 0.12185023 - samples/sec: 19.02 - lr: 0.000000 |
|
2023-01-08 18:04:48,105 epoch 25 - iter 1304/6529 - loss 0.12151675 - samples/sec: 19.01 - lr: 0.000000 |
|
2023-01-08 18:07:03,845 epoch 25 - iter 1956/6529 - loss 0.12293666 - samples/sec: 19.22 - lr: 0.000000 |
|
2023-01-08 18:09:20,797 epoch 25 - iter 2608/6529 - loss 0.12248209 - samples/sec: 19.05 - lr: 0.000000 |
|
2023-01-08 18:11:38,782 epoch 25 - iter 3260/6529 - loss 0.12236612 - samples/sec: 18.91 - lr: 0.000000 |
|
2023-01-08 18:13:57,739 epoch 25 - iter 3912/6529 - loss 0.12284535 - samples/sec: 18.78 - lr: 0.000000 |
|
2023-01-08 18:16:19,460 epoch 25 - iter 4564/6529 - loss 0.12312537 - samples/sec: 18.41 - lr: 0.000000 |
|
2023-01-08 18:18:34,844 epoch 25 - iter 5216/6529 - loss 0.12315613 - samples/sec: 19.27 - lr: 0.000000 |
|
2023-01-08 18:20:52,724 epoch 25 - iter 5868/6529 - loss 0.12280164 - samples/sec: 18.92 - lr: 0.000000 |
|
2023-01-08 18:23:11,733 epoch 25 - iter 6520/6529 - loss 0.12286952 - samples/sec: 18.77 - lr: 0.000000 |
|
2023-01-08 18:23:13,587 ---------------------------------------------------------------------------------------------------- |
|
2023-01-08 18:23:13,590 EPOCH 25 done: loss 0.1229 - lr 0.0000000 |
|
2023-01-08 18:24:28,587 DEV : loss 0.1607247292995453 - f1-score (micro avg) 0.9119 |
|
2023-01-08 18:24:28,641 BAD EPOCHS (no improvement): 4 |
|
2023-01-08 18:24:29,854 ---------------------------------------------------------------------------------------------------- |
|
2023-01-08 18:24:29,857 Testing using last state of model ... |
|
2023-01-08 18:25:11,654 0.9081 0.8984 0.9033 0.8277 |
|
2023-01-08 18:25:11,656 |
|
Results: |
|
- F-score (micro) 0.9033 |
|
- F-score (macro) 0.8976 |
|
- Accuracy 0.8277 |
|
|
|
By class: |
|
precision recall f1-score support |
|
|
|
ORG 0.9016 0.8667 0.8838 1523 |
|
LOC 0.9113 0.9305 0.9208 1425 |
|
PER 0.9216 0.9322 0.9269 1224 |
|
DAT 0.8623 0.7958 0.8277 480 |
|
MON 0.9665 0.9558 0.9611 181 |
|
PCT 0.9375 0.9740 0.9554 77 |
|
TIM 0.8235 0.7925 0.8077 53 |
|
|
|
micro avg 0.9081 0.8984 0.9033 4963 |
|
macro avg 0.9035 0.8925 0.8976 4963 |
|
weighted avg 0.9076 0.8984 0.9028 4963 |
|
samples avg 0.8277 0.8277 0.8277 4963 |
|
|
|
2023-01-08 18:25:11,656 ---------------------------------------------------------------------------------------------------- |