* minor doc fixes
correct superclass name and small grammar fixes
* correct the instance name in the error message
It appears to be `BaseTokenizer` from looking at:
`from tokenizers.implementations import BaseTokenizer as BaseTokenizerFast`
and not `Tokenizer` as it currently says.
* Attempt to fix the way squad_convert_examples_to_features pad the elements for the QA pipeline.
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Quality
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Make the code easier to read and avoid testing multiple test the same thing.
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* missing enum value on truncation_strategy.
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Rethinking for the easiest fix: expose the padding strategy on squad_convert_examples_to_features.
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Remove unused imports.
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Created model card for my extreme summarization model
* Update model_cards/yuvraj/xSumm/README.md
Co-authored-by: Julien Chaumond <chaumond@gmail.com>
* Created model card for my summarization model
* Update model_cards/yuvraj/summarizer-cnndm/README.md
Co-authored-by: Julien Chaumond <chaumond@gmail.com>
* DataParallel fixes:
1. switched to a more precise check
- if self.args.n_gpu > 1:
+ if isinstance(model, nn.DataParallel):
2. fix tests - require the same fixup under DataParallel as the training module
* another fix
* Don't pass sampler for iterable dataset
* Added check for test and eval dataloaders.
* Formatting
* Don't pass sampler for iterable dataset
* Added check for test and eval dataloaders.
* Formatting
* Cleaner if nesting.
* Added test for trainer and iterable dataset
* Formatting for test
* Fixed import when torch is available only.
* Added require torch decorator to helper class
* Moved dataset class inside unittest
* Removed nested if and changed model in test
* Checking torch availability for IterableDataset
Slightly breaking change, changes functionality for `use_cache` in XLNet: if use_cache is True and mem_len is 0 or None (which is the case in the base model config), the model behaves like GPT-2 and returns mems to be used as past in generation. At training time `use_cache` is overriden and always True.
Slightly breaking change, changes functionality for `use_cache` in XLNet: if use_cache is True and mem_len is 0 or None (which is the case in the base model config), the model behaves like GPT-2 and returns mems to be used as past in generation. At training time `use_cache` is overriden and always True.
