🚀 Feature request

Currently, the EncoderDecoderModel class in PyTorch automatically creates the decoder_input_ids based on the labels provided by the user (similar to how this is done for T5/BART). This should also be implemented for TFEncoderDecoderModel, because currently users should manually provide decoder_input_ids to the model.

One can take a look at the TF implementation

In gensim/models/fasttext.py:

    model = FastText(
        vector_size=m.dim,
        vector_size=m.dim,
        window=m.ws,
        window=m.ws,
        epochs=m.epoch,
        epochs=m.epoch,
        negative=m.neg,
        negative=m.neg,
        # FIXME: these next 2 lines read in unsupported FB FT modes (loss=3 softmax or loss=4 onevsall,
        # or model=3 supervi

Describe the bug
I'm having major trouble with from_csv.

Context: I'm writing tutorial for build simple text search engine with Jina + Hub. I don't want to include a whole section of processing datasets, hence just passing a CSV into from_csv. I tried with meme dataset (converted tsv) before, and now using [superhero dataset](https://www.kaggle.com/jonathanbesomi/superheroes-nlp-datas

Motivated by huggingface/transformers#12789 in Transformers, one welcoming change would be replacing assertions with proper exceptions. The only type of assertions we should keep are those used as sanity checks.

Currently, there is a total of 87 files with the assert statements (located under datasets and src/datasets), so when working on this, to manage the PR s

Is your feature request related to a problem? Please describe.
I typically used compressed datasets (e.g. gzipped) to save disk space. This works fine with AllenNLP during training because I can write my dataset reader to load the compressed data. However, the predict command opens the file and reads lines for the Predictor. This fails when it tries to load data from my compressed files.

cf nltk/nltk#2856 (comment)