* Documentation about loading a fast tokenizer within Transformers
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* style
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* closes#10258
* typo
* reworked deberta test
* implemented the comments from BigBird01 regarding sequence pair encoding of deberta
* Update style
* VOCAB_FILES_NAMES is now a oneliner as suggested by @sgugger
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* added #fmt: on as requested by @sgugger
* Style
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Squash all commits into one
* Update ViTFeatureExtractor to use image_utils instead of torchvision
* Remove torchvision and add Pillow
* Small docs improvement
* Address most comments by @sgugger
* Fix tests
* Clean up conversion script
* Pooler first draft
* Fix quality
* Improve conversion script
* Make style and quality
* Make fix-copies
* Minor docs improvements
* Should use fix-copies instead of manual handling
* Revert "Should use fix-copies instead of manual handling"
This reverts commit fd4e591bce.
* Place ViT in alphabetical order
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Replace is_sagemaker_distributed_available
* Merge SageMakerTrainer into Trainer
* Test with shorter condition
* Put back deleted line
* Deprecate SageMakerTrainer and SageMakerTrainingArguments
* Apply suggestions from code review
Co-authored-by: Philipp Schmid <32632186+philschmid@users.noreply.github.com>
Co-authored-by: Philipp Schmid <32632186+philschmid@users.noreply.github.com>
a fully qualified model.
We simply forgot to change the call for this one when this landed:
https://github.com/huggingface/transformers/pull/10888
It's odd that tests didn't catch that. Should we add some ?
(It's a pretty edgy test case, but it does run within the API).
* init
* first working test
* added todo for setup.py
* working test for single node multi node ddp and smd
* added tensorflow single node test
* added directory for pytorch and tensorflow due to different requirements.txt
* added directory for pytorch and tensorflow
* added comment for run_glue until it is available
* added output_dir to it
* smaller dataset to make test running faster
* adjust HP and script
* adjusted parameter for tensorflow
* refactored test scripts
* adjusted make file
* init
* first working test
* added todo for setup.py
* working test for single node multi node ddp and smd
* added tensorflow single node test
* added directory for pytorch and tensorflow due to different requirements.txt
* added directory for pytorch and tensorflow
* added comment for run_glue until it is available
* added output_dir to it
* smaller dataset to make test running faster
* adjust HP and script
* adjusted parameter for tensorflow
* refactored test scripts
* adjusted make file
* updated dlc container
* commented in all tests
* added both ecr images
* added new master branches
* debug
* added new datasets version
* init
* strange rebase bug
* removed changes
* changed min version for tests to work
* updated DLC
* added model parallel test
* removed test files
* removed test files
* tested with ned dlc
* added correct sagemaker sdk version
* adjust DLCs for official one
* reworked tests
* quality
* removed default profile added documentation to it
* added step in release for sagemaker tests
* reverted version for example script removed duplicated script and added install from master to requirements.txt
* removed mistaken .DS_Stores from mac
* fixed tests
* added Sylvains feedback
* make style
* added lysandre's feedback
* Initial commit
* Another bunch of updates
* make style quliaty + delete debug arg from bash script
* Use compue_metrics func
* Do a few fixes
* Add copyright
* Fix typos
A new argument `length_column_name` has been added to
`TrainingArguments`, with default value `"length"`. If this column
exists and `group_by_length` is `True`, the train sampler will use
it for grouping rather than computing it before training starts.
This is an optimization that allows the user to prepare data for fast
processing, preventing sequential access to the dataset as described in
issue #10909.
* Add NER example with accelerate library
* This commit contains the first (yet really unfinished)
version of a script for showing how to train HuggingFace model
with their new accelerate library.
* Fix metric calculation
* make style quality
* mv ner_no_trainer to token-classification dir
* Delete --debug flag from running script
* hf_datasets -> raw_datasets
* Make a few slight adjustments
* Add an informative comment + rewrite a help comment
* Change header
* Fix a few things
* Enforce to use fast tokenizers only
* DataCollatorWithPadding -> DataCollatorForTokenClassification
* Change bash script: python3 -> accelerate launch
* make style
* Add a few missing things (see below)
* Add a max-lenghth padding to predictions and labels to
enable accelerate gather functionality
* Add PyTorch no trainer example to the example README.md
* Remove --do-train from args as being redundant for now
* DataCollatorWithPadding -> DataCollatorForTokenClassification
* Remove some obsolete args.do_train conditions from the script
* Delete --do_train from bash running script
* Delete use_slow_tokenizer from args
* Add unintentionally removed flag --label_all_tokens
* Delete --debug flag from running script
