Update Readme to Run Multiple Choice Script from Example Directory (#39323)

* Update Readme to run in current place

* Update Readme files to execute PyTorch examples from their respective folders

examples/pytorch/README.md

@@ -65,7 +65,7 @@ examples/pytorch/token-classification/run_ner.py \
 
 Most example scripts should have the first two command line arguments and some have the third one. You can quickly check if a given example supports any of these by passing a `-h` option, e.g.:
 ```bash
-examples/pytorch/token-classification/run_ner.py -h
+token-classification/run_ner.py -h
 ```
 
 ## Resuming training
@@ -110,7 +110,7 @@ classification MNLI task using the `run_glue` script, with 8 GPUs:
 
 ```bash
 torchrun \
-    --nproc_per_node 8 pytorch/text-classification/run_glue.py \
+    --nproc_per_node 8 text-classification/run_glue.py \
     --model_name_or_path google-bert/bert-large-uncased-whole-word-masking \
     --task_name mnli \
     --do_train \

examples/pytorch/contrastive-image-text/README.md

@@ -84,7 +84,7 @@ loaded using the pre-trained weights.
 Finally, we can run the example script to train the model:
 
 ```bash
-python examples/pytorch/contrastive-image-text/run_clip.py \
+python run_clip.py \
     --output_dir ./clip-roberta-finetuned \
     --model_name_or_path ./clip-roberta \
     --data_dir $PWD/data \

examples/pytorch/multiple-choice/README.md

@@ -21,7 +21,7 @@ limitations under the License.
 `run_swag` allows you to fine-tune any model from our [hub](https://huggingface.co/models) (as long as its architecture as a `ForMultipleChoice` version in the library) on the SWAG dataset or your own csv/jsonlines files as long as they are structured the same way. To make it works on another dataset, you will need to tweak the `preprocess_function` inside the script.
 
 ```bash
-python examples/pytorch/multiple-choice/run_swag.py \
+python run_swag.py \
 --model_name_or_path FacebookAI/roberta-base \
 --do_train \
 --do_eval \

examples/pytorch/summarization/README.md

@@ -40,7 +40,7 @@ and you also will find examples of these below.
 
 Here is an example on a summarization task:
 ```bash
-python examples/pytorch/summarization/run_summarization.py \
+python run_summarization.py \
     --model_name_or_path google-t5/t5-small \
     --do_train \
     --do_eval \
@@ -64,7 +64,7 @@ And here is how you would use it on your own files, after adjusting the values f
 `--train_file`, `--validation_file`, `--text_column` and `--summary_column` to match your setup:
 
 ```bash
-python examples/pytorch/summarization/run_summarization.py \
+python run_summarization.py \
     --model_name_or_path google-t5/t5-small \
     --do_train \
     --do_eval \

examples/pytorch/translation/README.md

@@ -42,7 +42,7 @@ and you also will find examples of these below.
 Here is an example of a translation fine-tuning with a MarianMT model:
 
 ```bash
-python examples/pytorch/translation/run_translation.py \
+python run_translation.py \
     --model_name_or_path Helsinki-NLP/opus-mt-en-ro \
     --do_train \
     --do_eval \
@@ -62,7 +62,7 @@ MBart and some T5 models require special handling.
 T5 models `google-t5/t5-small`, `google-t5/t5-base`, `google-t5/t5-large`, `google-t5/t5-3b` and `google-t5/t5-11b` must use an additional argument: `--source_prefix "translate {source_lang} to {target_lang}"`. For example:
 
 ```bash
-python examples/pytorch/translation/run_translation.py \
+python run_translation.py \
     --model_name_or_path google-t5/t5-small \
     --do_train \
     --do_eval \
@@ -85,7 +85,7 @@ For the aforementioned group of T5 models it's important to remember that if you
 MBart models require a different format for `--source_lang` and `--target_lang` values, e.g. instead of `en` it expects `en_XX`, for `ro` it expects `ro_RO`. The full MBart specification for language codes can be found [here](https://huggingface.co/facebook/mbart-large-cc25). For example:
 
 ```bash
-python examples/pytorch/translation/run_translation.py \
+python run_translation.py \
     --model_name_or_path facebook/mbart-large-en-ro  \
     --do_train \
     --do_eval \
@@ -104,7 +104,7 @@ And here is how you would use the translation finetuning on your own files, afte
 values for the arguments `--train_file`, `--validation_file` to match your setup:
 
 ```bash
-python examples/pytorch/translation/run_translation.py \
+python run_translation.py \
     --model_name_or_path google-t5/t5-small \
     --do_train \
     --do_eval \
@@ -133,7 +133,7 @@ Here the languages are Romanian (`ro`) and English (`en`).
 If you want to use a pre-processed dataset that leads to high BLEU scores, but for the `en-de` language pair, you can use `--dataset_name stas/wmt14-en-de-pre-processed`, as following:
 
 ```bash
-python examples/pytorch/translation/run_translation.py \
+python run_translation.py \
     --model_name_or_path google-t5/t5-small \
     --do_train \
     --do_eval \