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
* Added embeddings layer
* Added layoutlm layers, main model, maskedlm and token classification classes
* Added model classes to tf auto models
* Added model to PT to TF conversion script
* Added model to doc README
* Added tests
* Removed unused imports
* Added layoutlm model, test, and doc for sequence classification, and fix imports in __init__.py
* Made tests pass!
* Fixed typos in imports and docs
* Fixed a typo in embeddings layer
* Removed imports
* Fixed formatting issues, imports, tests
* Added layoutlm layers, main model, maskedlm and token classification classes
* Added model classes to tf auto models
* Added model to PT to TF conversion script
* Removed unused imports
* Added layoutlm model, test, and doc for sequence classification, and fix imports in __init__.py
* Made tests pass!
* Fixed typos in imports and docs
* Removed imports
* Fixed small formatting issues
* Removed duplicates import from main __init__.py
* Chnaged deafult arg to true for adding pooling layer to tf layoutlm
* Fixed formatting issues
* Style
* Added copied from to classes copied from bert
* Fixed doc strings examples to work with layoutlm inputs
* Removed PyTorch reference in doc strings example
* Added integration tests
* Cleaned up initialization file
* Updated model checkpoint identifiers
* Fixed imports
Co-authored-by: Amir Tahmasbi <amir@ehsai.ca>
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
the orignal code in line 246 is
```
tokenizer: Optional["PreTrainedTokenizerBase"] = None,
```
it should be
```
tokenizer: Optional[PreTrainedTokenizerBase] = None,
```
Sylvain Gugger
Suraj Patil
Lysandre Debut
Philipp Schmid
WybeKoper
Patrick von Platen
Yih-Dar
Nicolas Patry
Vasudev Gupta
Stas Bekman
Daniel Stancl
pcuenca
Sylvain Gugger
Masatoshi Suzuki
Guillaume Filion
Bhadresh Savani
Tomy Hsieh
lexhuismans
Amir Tahmasbi
Jethro Kuan
Sidd Karamcheti
Eliza Szczechla
imzhengzx
RafaelWO