[FLAX] Question Answering Example (#13649)

* flax qa example

* Updated README:  Added Large model

* added utils_qa.py FULL_COPIES

* Updates:
1. Copyright Year updated
2. added dtype arg
3. passing seed and dtype to load model
4. Check eval flag before running eval

* updated README

* updated code comment

examples/flax/question-answering/README.md

+<!---
+Copyright 2021 The Google Flax Team Authors and HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# Question Answering examples
+
+Based on the script [`run_qa.py`](https://github.com/huggingface/transformers/blob/master/examples/flax/question-answering/run_qa.py).
+
+**Note:** This script only works with models that have a fast tokenizer (backed by the 🤗 Tokenizers library) as it
+uses special features of those tokenizers. You can check if your favorite model has a fast tokenizer in
+[this table](https://huggingface.co/transformers/index.html#supported-frameworks), if it doesn't you can still use the old version
+of the script.
+
+
+The following example fine-tunes BERT on SQuAD:
+
+To begin with it is recommended to create a model repository to save the trained model and logs.
+Here we call the model `"bert-qa-squad-test"`, 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 bert-qa-squad-test
+```
+
+Next we clone the model repository to add the tokenizer and model files.
+
+```
+git clone https://huggingface.co/<your-username>/bert-qa-squad-test
+```
+
+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_qa.py`.
+
+```bash
+export MODEL_DIR="./bert-qa-squad-test"
+ln -s ~/transformers/examples/flax/question-answering/run_qa.py run_qa.py
+```
+
+```bash
+python run_qa.py \
+  --model_name_or_path bert-base-uncased \
+  --dataset_name squad \
+  --do_train   \
+  --do_eval   \
+  --max_seq_length 384 \
+  --doc_stride 128 \
+  --learning_rate 3e-5 \
+  --num_train_epochs 2 \
+  --per_device_train_batch_size 12 \
+  --output_dir ${MODEL_DIR} \
+  --eval_steps 1000 \
+  --push_to_hub
+```
+
+Using the command above, the script will train for 2 epochs and run eval after each epoch. 
+Metrics and hyperparameters are stored in Tensorflow event files in `--output_dir`.
+You can see the results by running `tensorboard` in that directory:
+
+```bash
+$ tensorboard --logdir .
+```
+
+or directly on the hub under *Training metrics*.
+
+Training with the previously defined hyper-parameters yields the following results:
+
+```bash
+f1 = 88.62
+exact_match = 81.34
+```
+
+sample Metrics - [tfhub.dev](https://tensorboard.dev/experiment/6gU75Hx8TGCnc6tr4ZgI9Q)
+
+Here is an example training on 4 TITAN RTX GPUs and Bert Whole Word Masking uncased model to reach a F1 > 93 on SQuAD1.1:
+
+```bash
+export CUDA_VISIBLE_DEVICES=0,1,2,3
+python run_qa.py   \
+--model_name_or_path bert-large-uncased-whole-word-masking   \
+--dataset_name squad   \
+--do_train   \
+--do_eval   \
+--per_device_train_batch_size 6   \
+--learning_rate 3e-5   \
+--num_train_epochs 2   \
+--max_seq_length 384   \
+--doc_stride 128   \
+--output_dir /tmp/wwm_uncased_finetuned_squad/ \
+--eval_steps 1000
+```
+
+Training with the previously defined hyper-parameters yields the following results:
+
+```bash
+f1 = 93.31
+exact_match = 87.04
+```
+
+
+### Usage notes
+
+Note that when contexts are long they may be split into multiple training cases, not all of which may contain
+the answer span. 
+
+As-is, the example script will train on SQuAD or any other question-answering dataset formatted the same way, and can handle user
+inputs as well.
+
+### Memory usage and data loading
+
+One thing to note is that all data is loaded into memory in this script. Most question answering datasets are small
+enough that this is not an issue, but if you have a very large dataset you will need to modify the script to handle
+data streaming.

examples/flax/question-answering/requirements.txt

+datasets >= 1.8.0
+jax>=0.2.17
+jaxlib>=0.1.68
+flax>=0.3.4
+optax>=0.0.8
\ No newline at end of file

utils/check_copies.py

@@ -28,7 +28,10 @@ PATH_TO_DOCS = "docs/source"
 REPO_PATH = "."
 
 # Mapping for files that are full copies of others (keys are copies, values the file to keep them up to data with)
-FULL_COPIES = {"examples/tensorflow/question-answering/utils_qa.py": "examples/pytorch/question-answering/utils_qa.py"}
+FULL_COPIES = {
+    "examples/tensorflow/question-answering/utils_qa.py": "examples/pytorch/question-answering/utils_qa.py",
+    "examples/flax/question-answering/utils_qa.py": "examples/pytorch/question-answering/utils_qa.py",
+}
 
 
 LOCALIZED_READMES = {