Add openvino QAT question answering and image classification (#78)

* Add question answering example

* Add dtype for model inputs configuration

* Add image classification example

* Add option to specify the data collator

* Add option to remove the unused calibration dataset columns

* Change the examples README models

* Fix type

* Add keyword name in ov config

* Add max training samples to example README

examples/openvino/image-classification/README.md

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+<!---
+Copyright 2022 The 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.
+-->
+# Image classification
+
+This folder contains [`run_image_classification.py`](https://github.com/huggingface/optimum/blob/main/examples/openvino/image-classification/run_image_classification.py), a script to fine-tune a 🤗 Transformers model on an image classification dataset while applying quantization aware training (QAT). QAT can be easily applied by replacing the Transformers [`Trainer`](https://huggingface.co/docs/transformers/main/en/main_classes/trainer#trainer) with the Optimum [`OVTrainer`]. Any model from our [hub](https://huggingface.co/models) can be fine-tuned and quantized, as long as the model is supported by the [`AutoModelForImageClassification`](https://huggingface.co/docs/transformers/main/en/model_doc/auto#transformers.AutoModelForImageClassification) API.
+
+### Fine-tuning ViT on the beans dataset
+
+Here we show how to apply quantization aware training (QAT) on a fine-tuned Vision Transformer (ViT) on the beans dataset (to classify the disease type of bean leaves).
+
+```bash
+python run_image_classification.py \
+    --model_name_or_path nateraw/vit-base-beans \
+    --dataset_name beans \
+    --remove_unused_columns False \
+    --do_train \
+    --do_eval \
+    --learning_rate 2e-5 \
+    --num_train_epochs 1 \
+    --per_device_train_batch_size 8 \
+    --per_device_eval_batch_size 8 \
+    --logging_strategy steps \
+    --logging_steps 10 \
+    --evaluation_strategy epoch \
+    --save_strategy epoch \
+    --save_total_limit 3 \
+    --seed 1337 \
+    --output_dir /tmp/beans_outputs/
+```

examples/openvino/image-classification/requirements.txt

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+datasets >= 1.8.0
+torch >= 1.9.0
+torchvision>=0.6.0
+evaluate