[example/flax] add summarization readme (#12393)

* add readme

* update readme and add requirements

* Update examples/flax/summarization/README.md
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

examples/flax/summarization/README.md

+# Summarization (Seq2Seq model) training examples
+
+The following example showcases how to finetune a sequence-to-sequence model for summarization
+using the JAX/Flax backend.
+
+JAX/Flax allows you to trace pure functions and compile them into efficient, fused accelerator code on both GPU and TPU.
+Models written in JAX/Flax are **immutable** and updated in a purely functional
+way which enables simple and efficient model parallelism.
+
+`run_summarization_flax.py` is a lightweight example of how to download and preprocess a dataset from the 🤗 Datasets library or use your own files (jsonlines or csv), then fine-tune one of the architectures above on it.
+
+For custom datasets in `jsonlines` format please see: https://huggingface.co/docs/datasets/loading_datasets.html#json-files and you also will find examples of these below.
+
+Let's start by creating a model repository to save the trained model and logs.
+Here we call the model `"bart-base-xsum"`, but you can change the model name as you like.
+
+You can do this either directly on [huggingface.co](https://huggingface.co/new) (assuming that
+you are logged in) or via the command line:
+
+```
+huggingface-cli repo create bart-base-xsum
+```
+Next we clone the model repository to add the tokenizer and model files.
+```
+git clone https://huggingface.co/<your-username>/bart-base-xsum
+```
+To ensure that all tensorboard traces will be uploaded correctly, we need to 
+track them. You can run the following command inside your model repo to do so.
+
+```
+cd bart-base-xsum
+git lfs track "*tfevents*"
+```
+
+Great, we have set up our model repository. During training, we will automatically
+push the training logs and model weights to the repo.
+
+Next, let's add a symbolic link to the `run_summarization_flax.py`.
+
+```bash
+export MODEL_DIR="./bart-base-xsum"
+ln -s ~/transformers/examples/flax/summarization/run_summarization_flax.py run_summarization_flax.py
+```
+
+### Train the model
+Next we can run the example script to train the model:
+
+```bash
+python run_summarization_flax.py \
+	--output_dir ${MODEL_DIR} \
+	--model_name_or_path facebook/bart-base \
+	--tokenizer_name facebook/bart-base \
+	--dataset_name="xsum" \
+	--do_train --do_eval --do_predict --predict_with_generate \
+	--num_train_epochs 6 \
+	--learning_rate 5e-5 --warmup_steps 0 \
+	--per_device_train_batch_size 64 \
+	--per_device_eval_batch_size 64 \
+	--overwrite_output_dir \
+	--max_source_length 512 --max_target_length 64 \
+	--push_to_hub
+```
+
+This should finish in 37min, with validation loss and ROUGE2 score of 1.7785 and 17.01 respectively after 6 epochs. training statistics can be accessed on [tfhub.de](https://tensorboard.dev/experiment/OcPfOIgXRMSJqYB4RdK2tA/#scalars).
+
+> Note that here we used default `generate` arguments, using arguments specific for `xsum` dataset should give better ROUGE scores.  

examples/flax/summarization/requirements.txt

+datasets >= 1.1.3
+jax>=0.2.8
+jaxlib>=0.1.59
+flax>=0.3.4
+optax>=0.0.8