flax ner example (#13365)

* flax ner example

* added task to README

* updated readme

* 1. ArgumentParser -> HfArgumentParser
2. step-wise logging,eval and save

* added requirements.txt

* added progress bar

* updated README

* added check_min_version

* updated training data permuattion with JAX

* added metric lib to requirements

* updated readme table

* fixed imports

examples/flax/token-classification/README.md

@@ -0,0 +1,74 @@
+<!---
+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.
+-->
+
+# Token classification examples
+
+Fine-tuning the library models for token classification task such as Named Entity Recognition (NER), Parts-of-speech tagging (POS) or phrase extraction (CHUNKS). The main script run_flax_ner.py leverages the 🤗 Datasets library. You can easily customize it to your needs if you need extra processing on your datasets.
+
+It will either run on a datasets hosted on our hub or with your own text files for training and validation, you might just need to add some tweaks in the data preprocessing.
+
+The following example fine-tunes BERT on CoNLL-2003:
+
+To begin with it is recommended to create a model repository to save the trained model and logs.
+Here we call the model `"bert-ner-conll2003-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-ner-conll2003-test
+```
+
+Next we clone the model repository to add the tokenizer and model files.
+
+```
+git clone https://huggingface.co/<your-username>/bert-ner-conll2003-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_flax_ner.py`.
+
+```bash
+export MODEL_DIR="./bert-ner-conll2003-test"
+ln -s ~/transformers/examples/flax/token-classification/run_flax_ner.py run_flax_ner.py
+```
+
+```bash
+python run_flax_ner.py \
+  --model_name_or_path bert-base-cased \
+  --dataset_name conll2003 \
+  --max_seq_length 128 \
+  --learning_rate 2e-5 \
+  --num_train_epochs 3 \
+  --per_device_train_batch_size 4 \
+  --output_dir ${MODEL_DIR} \
+  --eval_steps 300 \
+  --push_to_hub
+```
+
+Using the command above, the script will train for 3 epochs and run eval after each epoch. 
+Metrics and hyperparameters are stored in Tensorflow event files in `--output_dir`.
+You can see the results by running `tensorboard` in that directory:
+
+```bash
+$ tensorboard --logdir .
+```
+
+or directly on the hub under *Training metrics*.
+
+sample Metrics - [tfhub.dev](https://tensorboard.dev/experiment/u52qsBIpQSKEEXEJd2LVYA)