Update datasets task tags to align tags with models (#4067)

* update tasks list

* update tags in dataset cards

* more cards updates

* update dataset tags parser

* fix multi-choice-qa

* style

* small improvements in some dataset cards

* allow certain tag fields to be empty

* update vision datasets tags

* use multi-class-image-classification and remove other tags

Commit from https://github.com/huggingface/datasets/commit/edb4411d4e884690b8b328dba4360dbda6b3cbc8

README.md

@@ -14,9 +14,9 @@ size_categories:
 source_datasets:
 - original
 task_categories:
-- conditional-text-generation
+- text2text-generation
 task_ids:
-- conditional-text-generation-other-meaning-representtion-to-text
+- text2text-generation-other-meaning-representation-to-text
 paperswithcode_id: e2e
 pretty_name: End-to-End NLG Challenge
 ---
@@ -67,7 +67,7 @@ https://arxiv.org/abs/1706.09254
 
 ### Supported Tasks and Leaderboards
 
-- `conditional-text-generation-other-meaning-representtion-to-text`: The dataset can be used to train a model to generate descriptions in the restaurant domain from meaning representations, which consists in taking as input some data about a restaurant and generate a sentence in natural language that presents the different aspects of the data about the restaurant.. Success on this task is typically measured by achieving a *high* [BLEU](https://huggingface.co/metrics/bleu), [NIST](https://huggingface.co/metrics/nist), [METEOR](https://huggingface.co/metrics/meteor), [Rouge-L](https://huggingface.co/metrics/rouge), [CIDEr](https://huggingface.co/metrics/cider). The TGen model (Dusek and Jurcıcek, 2016a) was used a baseline, had the following scores:
+- `text2text-generation-other-meaning-representation-to-text`: The dataset can be used to train a model to generate descriptions in the restaurant domain from meaning representations, which consists in taking as input some data about a restaurant and generate a sentence in natural language that presents the different aspects of the data about the restaurant.. Success on this task is typically measured by achieving a *high* [BLEU](https://huggingface.co/metrics/bleu), [NIST](https://huggingface.co/metrics/nist), [METEOR](https://huggingface.co/metrics/meteor), [Rouge-L](https://huggingface.co/metrics/rouge), [CIDEr](https://huggingface.co/metrics/cider). The TGen model (Dusek and Jurcıcek, 2016a) was used a baseline, had the following scores:
 
 |          | BLEU	  | NIST   | METEOR | ROUGE_L | CIDEr  |
 | -------- | ------ | ------ | ------ | ------- | ------ |