To be saver in regards of pickle, I propose to use a dict-format to store all properties required to recreate the embeddings (weights are stored with the model itself anyways).
This allows opening Flairmodels with incompatible parameters via torch.load(...) and therefore allows debugging version conflicts.

During development I also found & fixed the following issues:

• DocumentLMEmbeddings were not providing the right names for their embeddings. So taking the correct usage of doc_lm_embedding.embedd(sentence);sentence.get_embeddings(doc_lm_embedding.get_names()) Would result into an empty tensor
• Frozen FlairEmbeddings always use dropout: since the .train() method didn't call it's super method, the .eval() call in the __init__ was negated, leading to dropout staying enabled as that is the default.
• Some Embeddings where not in .eval() mode after creating.
• Add tests to embeddings that have no tests yet.
• HashEmbeddings were returning an index error, unless each sentence had exactly one token (indexing unflattened array)
• ElmoEmbeddings are deprecated as allennlp will stop support soon.
• TextRegression model is now rightfully importable as from flair.models import TextRegressior

This also implements two classes AutoFlairModel and AutoFlairClassifier which can be used to to load any model, given that their type is clear.
Example usages are here:

from flair import AutoFlairClassifier
tagger = AutoFlairClassifier.load("ner-large")
tars = AutoFlairClassifier.load("tars-tagger")
tars.save("model.pt")
tars2 = AutoFlairClassifier.load("model.pt")
relation = AutoFlairClassifier.load("relation")
offensive = AutoFlairClassifier.load("de-offensive-language")
multi = MultitaskModel([offensive, tagger, relation])
multi.save("multi.pt")
multi2 = AutoFlairClassifier.load("multi.pt")
...

The difference between AutoFlairModel and AutoFlairClassifier is that AutoFlairClassifier is limited to only classifers (no text-regressor) while it provides stronger typing hints (all methods the Classifier provides extra, e.g.: predict)

Potential issues are:

• current models do not contain class information. I added a simple method that tries to parse the content into the right class but that might fail. E.g. before changing the code from model = SequenceTagger.load("my-model.pt") to model = AutoFlairClassifier.load("my-model.pt") I would recommend loading it once and saving it again on the newest version.
• MultitaskModel got a rework of the internal state, therefore older models cannot be loaded. I don't see this as an big issue as those were never released before. But it is something to be aware of.