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Update all references to canonical models (#29001)

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* Script & Manual edition

* Update
This commit is contained in:
Lysandre Debut
2024-02-16 08:16:58 +01:00
committed by GitHub
parent 1e402b957d
commit f497f564bb
561 changed files with 2682 additions and 2687 deletions
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14
examples/pytorch/text-classification/README.md
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@@ -31,7 +31,7 @@ GLUE is made up of a total of 9 different tasks. Here is how to run the script o
export TASK_NAME=mrpc
python run_glue.py \
--model_name_or_path bert-base-cased \
--model_name_or_path google-bert/bert-base-cased \
--task_name $TASK_NAME \
--do_train \
--do_eval \
@@ -68,7 +68,7 @@ The following example fine-tunes BERT on the `imdb` dataset hosted on our [hub](
```bash
python run_glue.py \
--model_name_or_path bert-base-cased \
--model_name_or_path google-bert/bert-base-cased \
--dataset_name imdb \
--do_train \
--do_predict \
@@ -90,7 +90,7 @@ We can specify the metric, the label column and aso choose which text columns to
dataset="amazon_reviews_multi"
subset="en"
python run_classification.py \
--model_name_or_path bert-base-uncased \
--model_name_or_path google-bert/bert-base-uncased \
--dataset_name ${dataset} \
--dataset_config_name ${subset} \
--shuffle_train_dataset \
@@ -113,7 +113,7 @@ The following is a multi-label classification example. It fine-tunes BERT on the
dataset="reuters21578"
subset="ModApte"
python run_classification.py \
--model_name_or_path bert-base-uncased \
--model_name_or_path google-bert/bert-base-uncased \
--dataset_name ${dataset} \
--dataset_config_name ${subset} \
--shuffle_train_dataset \
@@ -175,7 +175,7 @@ then
export TASK_NAME=mrpc
python run_glue_no_trainer.py \
--model_name_or_path bert-base-cased \
--model_name_or_path google-bert/bert-base-cased \
--task_name $TASK_NAME \
--max_length 128 \
--per_device_train_batch_size 32 \
@@ -202,7 +202,7 @@ that will check everything is ready for training. Finally, you can launch traini
export TASK_NAME=mrpc
accelerate launch run_glue_no_trainer.py \
--model_name_or_path bert-base-cased \
--model_name_or_path google-bert/bert-base-cased \
--task_name $TASK_NAME \
--max_length 128 \
--per_device_train_batch_size 32 \
@@ -232,7 +232,7 @@ This example code fine-tunes mBERT (multi-lingual BERT) on the XNLI dataset. It
```bash
python run_xnli.py \
--model_name_or_path bert-base-multilingual-cased \
--model_name_or_path google-bert/bert-base-multilingual-cased \
--language de \
--train_language en \
--do_train \