* Authorize last version of tokenizer
* Update version table
* Fix conversion of spm tokenizers and fix some hub links
* Bump tokenizers version to 0.10.1rc1
* Add script to check tokenizers conversion with XNLI
* Add some more mask_token lstrip support
* Must modify mask_token in slow tokenizers too
* Keep using the old method for Pegasus
* add missing import
Co-authored-by: Anthony MOI <m.anthony.moi@gmail.com>
Adding new `encoder_no_repeat_ngram_size` to `generate`.
Blenderbot results seemed off compared to original ParlAI script:
`https://parl.ai/projects/recipes/`. Notably the model seems
to repeat a lot what was said during the conversation.
The actual problem was that `no_repeat_ngram_size` actually applies
to the `encoder_input_ids` but HF's `no_repeat_ngram_size` applies
to the previously generated ids (within the decoder). The history
conversation of blenderbot is within the `encoder` part so that
explains why HF's implementation had the repetitions.
This fix was focused on blenderbot *not* small and added tests
for those because they are quite different in configuration.
This change includes:
- Adding a new EncoderNoRepeatLogitProcessor.
- Adding 1 new arg to `generate` (`encoder_no_repeat_ngram_size`)
- Adding 1 new config parameter `encoder_no_repeat_ngram_size`.
- Adding 2 tests, one for the pipeline (high level, inputs exhibited
repeat behavior, one low level for EncoderNoRepeatLogitProcessor)
- Factored NoRepeatLogitProcessor so that logic could be reused.
Further work:
- Blenderbot conversational pipeline still does not behave correctly
as they way input is prepared within the pipeline is still incorrect
(follow up PR)
- Blenderbot allows the bot to have personas, which is done by
prepending "your personna: XXXX" to the input, this could be explored
too in a follow up PR.
@patrickvonplaten
@LysandreJik
* Update src/transformers/generation_logits_process.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/generation_utils.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/generation_utils.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/configuration_utils.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Doc quality.
* Fixing test.
* Last fixes.
* Fixing to account for batch_size.
* Update src/transformers/configuration_utils.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/generation_utils.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Add {decoder_,}head_mask to LED
* Fix create_custom_forward signatue in encoder
* Add head_mask to longformer
* Add head_mask to longformer to fix dependencies
of LED on Longformer.
* Not working yet
* Add mising one input in longofrmer_modeling.py
* make fix-copies
* change tokenizer requirement
* split line
* Correct typo from list to str
* improve style
* make other function pretty as well
* add comment
* correct typo
* add new test
* pass tests for tok without padding token
* Apply suggestions from code review
* Change documentation to correctly specify loss tensor size
* Change documentation to correct input format for labels
* Corrected output size of loss tensor for sequence classifier, multiple choice model and question answering
This affects Adafactor with relative_step=False and scale_parameter=True.
Updating group["lr"] makes the result of ._get_lr() depends on the previous call,
i.e., on the scale of other parameters. This isn't supposed to happen.
* MOD: fit chinese wwm to new datasets
* MOD: move wwm to new folder
* MOD: formate code
* Styling
* MOD add param and recover trainer
Co-authored-by: Sylvain Gugger <sylvain.gugger@gmail.com>
* [t5 doc] typos
a few run away backticks
@sgugger
* style
* [trainer] put fp16 args together
this PR proposes a purely cosmetic change that puts all the fp16 args together - so they are easier to manager/read
@sgugger
* style
* [wandb] make WANDB_DISABLED disable wandb with any value
This PR solves part of https://github.com/huggingface/transformers/issues/9623
It tries to actually do what https://github.com/huggingface/transformers/issues/9699 requested/discussed and that is any value of `WANDB_DISABLED` should disable wandb.
The current behavior is that it has to be one of `ENV_VARS_TRUE_VALUES = {"1", "ON", "YES"}`
I have been using `WANDB_DISABLED=true` everywhere in scripts as it was originally advertised. I have no idea why this was changed to a sub-set of possible values. And it's not documented anywhere.
@sgugger
* WANDB_DISABLED=true to disable; make tf trainer consistent
* style
* Add {decoder_,}head_mask to fsmt_modeling.py
* Enable test_headmasking and some changes to docs
* Remove test_head_masking flag from fsmt test file
Remove test_head_masking flag from test_modeling_fsmt.py
since test_head_masking is set to be True by default (thus it is redundant to store).
* Merge master and remove test_head_masking = True
* Rebase necessary due to an update of jaxlib
* Remove test_head_masking=True in tests/test_modeling_fsmt.py
as it is redundant.
* TFBart lables consider both pad token and -100
* make style
* fix for all other models
Co-authored-by: kykim <kykim>
Co-authored-by: patrickvonplaten <patrick.v.platen@gmail.com>
