Repository: huggingface/transformers
Pull request: Update Readme to Run Multiple Choice Script from Example Directory

# What does this PR do?

In the Multiple Choice example, the Readme file includes the instruction 
```bash
python examples/pytorch/multiple-choice/run_swag.py \
--model_name_or_path FacebookAI/roberta-base \
--do_train \
--do_eval \
--learning_rate 5e-5 \
--num_train_epochs 3 \
--output_dir /tmp/swag_base \
--per_device_eval_batch_size=16 \
--per_device_train_batch_size=16 \
--overwrite_output
```
which may cause an error when attempting to find the file if the user is currently in the example directory. Assuming the user is in the example directory, this PR suggests removing the complete path and running the Python script directly from that location. Other instructions in this Readme, as well as in other examples, run the script from their respective directories without using the complete path ( examples/DIR_EXAMPLE/SCRIPT.py ).

## Before submitting
- [X] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [X] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
      Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

@stevhliu hi, I believe you cloud revie this PR.

Changed files:
filename: examples/pytorch/README.md
patch: @@ -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 \

filename: examples/pytorch/contrastive-image-text/README.md
patch: @@ -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 \

filename: examples/pytorch/multiple-choice/README.md
patch: @@ -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 \

filename: examples/pytorch/summarization/README.md
patch: @@ -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 \

filename: examples/pytorch/translation/README.md
patch: @@ -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 \