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Kill model archive maps (#4636)

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* Kill model archive maps

* Fixup

* Also kill model_archive_map for MaskedBertPreTrainedModel

* Unhook config_archive_map

* Tokenizers: align with model id changes

* make style && make quality

* Fix CI
This commit is contained in:
Julien Chaumond
2020-06-02 09:39:33 -04:00
committed by GitHub
parent 47a551d17b
commit d4c2cb402d
115 changed files with 792 additions and 1323 deletions
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examples/text-classification/README.md
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@@ -258,7 +258,7 @@ TEST RESULTS {'val_loss': tensor(0.0707), 'precision': 0.852427800698191, 'recal
Based on the script [`run_xnli.py`](https://github.com/huggingface/transformers/blob/master/examples/text-classification/run_xnli.py).
[XNLI](https://www.nyu.edu/projects/bowman/xnli/) is crowd-sourced dataset based on [MultiNLI](http://www.nyu.edu/projects/bowman/multinli/). It is an evaluation benchmark for cross-lingual text representations. Pairs of text are labeled with textual entailment annotations for 15 different languages (including both high-resource language such as English and low-resource languages such as Swahili).
[XNLI](https://www.nyu.edu/projects/bowman/xnli/) is a crowd-sourced dataset based on [MultiNLI](http://www.nyu.edu/projects/bowman/multinli/). It is an evaluation benchmark for cross-lingual text representations. Pairs of text are labeled with textual entailment annotations for 15 different languages (including both high-resource language such as English and low-resource languages such as Swahili).
#### Fine-tuning on XNLI
@@ -273,7 +273,6 @@ on a single tesla V100 16GB. The data for XNLI can be downloaded with the follow
export XNLI_DIR=/path/to/XNLI
python run_xnli.py \
--model_type bert \
--model_name_or_path bert-base-multilingual-cased \
--language de \
--train_language en \