thomwolf
936e813c84
clean up examples - added squad example and test
2019-07-12 14:16:06 +02:00
thomwolf
e55d4c4ede
various updates to conversion, models and examples
2019-06-26 00:57:53 +02:00
Thomas Wolf
659af2cbd0
Merge pull request #604 from samuelbroscheit/master
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Fixing issue "Training beyond specified 't_total' steps with schedule 'warmup_linear'" reported in #556
2019-06-14 16:49:24 +02:00
Meet Pragnesh Shah
e02ce4dc79
[hotfix] Fix frozen pooler parameters in SWAG example.
2019-06-11 15:13:53 -07:00
samuelbroscheit
94247ad6cb
Make num_train_optimization_steps int
2019-05-13 12:38:22 +02:00
samuel.broscheit
49a77ac16f
Clean up a little bit
2019-05-12 00:31:10 +02:00
samuel.broscheit
3bf3f9596f
Fixing the issues reported in https://github.com/huggingface/pytorch-pretrained-BERT/issues/556
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Reason for issue was that optimzation steps where computed from example size, which is different from actual size of dataloader when an example is chunked into multiple instances.
Solution in this pull request is to compute num_optimization_steps directly from len(data_loader).
2019-05-12 00:13:45 +02:00
burcturkoglu
00c7fd2b79
Division to num_train_optimizer of global_step in lr_this_step is removed.
2019-05-09 10:57:03 +03:00
burcturkoglu
fa37b4da77
Merge branch 'master' of https://github.com/huggingface/pytorch-pretrained-BERT
2019-05-09 10:55:24 +03:00
burcturkoglu
5289b4b9e0
Division to num_train_optimizer of global_step in lr_this_step is removed.
2019-05-09 10:51:38 +03:00
MottoX
74dbba64bc
Prepare optimizer only when args.do_train is True
2019-05-02 19:09:29 +08:00
Thomas Wolf
2dee86319d
Merge pull request #527 from Mathieu-Prouveur/fix_value_training_loss
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Update example files so that tr_loss is not affected by args.gradient…
2019-04-30 11:12:55 +02:00
Mathieu Prouveur
87b9ec3843
Fix tr_loss rescaling factor using global_step
2019-04-29 12:58:29 +02:00
Mathieu Prouveur
ed8fad7390
Update example files so that tr_loss is not affected by args.gradient_accumulation_step
2019-04-24 14:07:00 +02:00
thomwolf
d94c6b0144
fix training schedules in examples to match new API
2019-04-23 11:17:06 +02:00
thomwolf
60ea6c59d2
added best practices for serialization in README and examples
2019-04-15 15:00:33 +02:00
thomwolf
179a2c2ff6
update example to work with new serialization semantic
2019-04-15 14:33:23 +02:00
Yuqiang Xie
77944d1b31
add tqdm to the process of eval
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Maybe better.
2019-03-21 20:59:33 +08:00
tseretelitornike
83857ffeaa
Added missing imports.
2019-03-15 12:45:48 +01:00
thomwolf
994d86609b
fixing PYTORCH_PRETRAINED_BERT_CACHE use in examples
2019-03-06 10:21:24 +01:00
thomwolf
5c85fc3977
fix typo - logger info
2019-03-06 10:05:21 +01:00
thomwolf
eebc8abbe2
clarify and unify model saving logic in examples
2019-02-11 14:04:19 +01:00
Thomas Wolf
848aae49e1
Merge branch 'master' into python_2
2019-02-06 00:13:20 +01:00
thomwolf
448937c00d
python 2 compatibility
2019-02-06 00:07:46 +01:00
thomwolf
1579c53635
more explicit notation: num_train_step => num_train_optimization_steps
2019-02-05 15:36:33 +01:00
Matej Svejda
5169069997
make examples consistent, revert error in num_train_steps calculation
2019-01-30 11:47:25 +01:00
Matej Svejda
9c6a48c8c3
fix learning rate/fp16 and warmup problem for all examples
2019-01-27 14:07:24 +01:00
thomwolf
c9fd350567
remove default when action is store_true in arguments
2019-01-07 13:01:54 +01:00
Grégory Châtel
186f75342e
Adding new pretrained model to the help of the bert_model argument.
2019-01-02 14:00:59 +01:00
thomwolf
ae88eb88a4
set encoding to 'utf-8' in calls to open
2018-12-14 13:48:58 +01:00
thomwolf
e1eab59aac
no fp16 on evaluation
2018-12-13 14:54:02 +01:00
thomwolf
087798b7fa
fix reloading model for evaluation in examples
2018-12-13 14:48:12 +01:00
thomwolf
0f544625f4
fix swag example for work with apex
2018-12-13 13:35:59 +01:00
Grégory Châtel
df34f22854
Removing the dependency to pandas and using the csv module to load data.
2018-12-10 17:45:23 +01:00
Grégory Châtel
d429c15f25
Removing old code from copy-paste.
2018-12-06 19:19:21 +01:00
Grégory Châtel
63c45056aa
Finishing the code for the Swag task.
2018-12-06 18:53:05 +01:00
Grégory Châtel
c45d8ac554
Storing the feature of each choice as a dict for readability.
2018-12-06 16:01:28 +01:00
Grégory Châtel
0812aee2c3
Fixing problems in convert_examples_to_features.
2018-12-06 15:53:07 +01:00
Grégory Châtel
f2b873e995
convert_examples_to_features code and small improvements.
2018-12-06 15:40:47 +01:00
Grégory Châtel
83fdbd6043
Adding read_swag_examples to load the dataset.
2018-12-06 14:02:46 +01:00
Grégory Châtel
7183cded4e
SwagExample class.
2018-12-06 13:39:44 +01:00
