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* Pass datasets trust_remote_code

* Pass trust_remote_code in more tests

* Add trust_remote_dataset_code arg to some tests

* Revert "Temporarily pin datasets upper version to fix CI"

This reverts commit b7672826.

* Pass trust_remote_code in librispeech_asr_dummy docstrings

* Revert "Pin datasets<2.20.0 for examples"

This reverts commit 833fc17a.

* Pass trust_remote_code to all examples

* Revert "Add trust_remote_dataset_code arg to some tests" to research_projects

* Pass trust_remote_code to tests

* Pass trust_remote_code to docstrings

* Fix flax examples tests requirements

* Pass trust_remote_dataset_code arg to tests

* Replace trust_remote_dataset_code with trust_remote_code in one example

* Fix duplicate trust_remote_code

* Replace args.trust_remote_dataset_code with args.trust_remote_code

* Replace trust_remote_dataset_code with trust_remote_code in parser

* Replace trust_remote_dataset_code with trust_remote_code in dataclasses

* Replace trust_remote_dataset_code with trust_remote_code arg

* Remove deprecated logic and warnings

* Add back some code that seems to be important...

* Let's just add all he nllb stuff back; removing it is a bit more involved

* Remove kwargs

* Remove more kwargs

Fix --model_type in examples

updated examples/pytorch/language-modeling scripts and requirements.txt to require datasets>=2.14.0 (#30120)

updated requirements.txt and require_version() calls in examples/pytorch/language-modeling to require datasets>=2.14.0

feat: add support for torch_dtype
Co-authored-by: Jacky Lee <jackylee328@gmail.com>

* Update legacy Repository usage in `examples/pytorch/text-classification/run_glue_no_trainer.py`

Marked for deprecation here https://huggingface.co/docs/huggingface_hub/guides/upload#legacy-upload-files-with-git-lfs

* Fix import order

* Replace all example usage of deprecated Repository

* Fix remaining repo call and rename args variable

* Revert removing creation of gitignore files and don't change research examples

* add USE_TORCH_XLA env

* rename torch_tpu to torch_xla

* better is_torch_xla_available; fix some fsdp and performance issues

* fix format

* fix bug when pjrt_device is cpu

* fix bug

* fix the deprecation handling

---------
Co-authored-by: anw90 <ang868@gmail.com>
Co-authored-by: wangang.wa <wangang.wa@alibaba-inc.com>

* add: initial script to train clm fim

* fix: if training model from scratch, new tokens will be added and embeddings resized

* fix: fixed attention_mask errors when generating FIM data

* fix: file formatted using black

* add: run_fim_no_trainer.py and fixed some comments in run_fim.py

* add: added fim examples to the README.md and ran code fixup

* fix: little bug in both fim training scripts

* fix: remove comment from notebook and added a note on fim related params

* fix: minor typo in README

* add: suggested minor changes to README and run_fim.py

* add: gradient_accumulation_steps and gradient_checkpointing args

* add: improved model embedding resizing

* add: pad_to_multiple_of and attn_implementation params

* add: requested minor changes

* add: deepspeed zero compatibility

* add: resize embeddings layer with zero3 support for fim model initialization

* Script & Manual edition

* Update

Co-authored-by: p_spozzhang <p_spozzhang@tencent.com>

* change datasets

* fix

* Fix lr_scheduler

* Fix lr scheduler

While using `run_clm.py`,[^1] I noticed that some files were being added
to my global cache, not the local cache. I set the `cache_dir` parameter
for the one call to `evaluate.load()`, which partially solved the
problem. I figured that while I was fixing the one script upstream, I
might as well fix the problem in all other example scripts that I could.

There are still some files being added to my global cache, but this
appears to be a bug in `evaluate` itself. This commit at least moves
some of the files into the local cache, which is better than before.

To create this PR, I made the following regex-based transformation:
`evaluate\.load\((.*?)\)` -> `evaluate\.load\($1,
cache_dir=model_args.cache_dir\)`. After using that, I manually fixed
all modified files with `ruff` serving as useful guidance. During the
process, I removed one existing usage of the `cache_dir` parameter in a
script that did not have a corresponding `--cache-dir` argument
declared.

[^1]: I specifically used `pytorch/language-modeling/run_clm.py` from
v4.34.1 of the library. For the original code, see the following URL:
https://github.com/huggingface/transformers/tree/acc394c4f5e1283c19783581790b3dc3105a3697/examples/pytorch/language-modeling/run_clm.py.

* fix no sequence length models error

* block size check

---------

Co-authored-by: Adam Louly <adamlouly@microsoft.com@orttrainingdev9.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>

fixed the broken links belogs to dataset library of transformers

fix max pos issue

Co-authored-by: Adam Louly <adamlouly@microsoft.com@orttrainingdev9.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>

Add missing spaces in adjacent strings

checkpoint path

* refactor: change default block_size

* fix: return tf to origin

* fix: change files to origin

* rebase

* rebase

* rebase

* rebase

* rebase

* rebase

* rebase

* rebase

* refactor: add min block_size to files

* reformat: add min block_size for run_clm tf

* docs: change assert to raise and some small docs

* docs: add rule and some document

* fix: fix bug

* fix: fix bug

* chorse: revert logging

* chorse: revert

* docs: add space to docs

* docs: remove reduntant space

feat: correct update resume update with steps