Add fine-tune presets to ModelTrainer

[alanakbik]

This is a first step of refactoring the ModelTrainer to make fine-tuning more convenient. Essentially this adds a fine_tune routine that sets default parameters used for fine-tuning (AdamW optimizer, small learning rate, few epochs, cyclic learning rate scheduling, etc.).

New syntax with fine_tune method:

from flair.data import Corpus
from flair.datasets import TREC_6
from flair.embeddings import TransformerDocumentEmbeddings
from flair.models import TextClassifier
from flair.trainers import ModelTrainer

# 1. get the corpus
corpus: Corpus = TREC_6()

# 2. what label do we want to predict?
label_type = 'question_class'

# 3. create the label dictionary
label_dict = corpus.make_label_dictionary(label_type=label_type)

# 4. initialize transformer document embeddings (many models are available)
document_embeddings = TransformerDocumentEmbeddings('distilbert-base-uncased', fine_tune=True)

# 5. create the text classifier
classifier = TextClassifier(document_embeddings, label_dictionary=label_dict, label_type=label_type)

# 6. initialize trainer
trainer = ModelTrainer(classifier, corpus)

# 7. run training with fine-tuning
trainer.fine_tune('resources/taggers/question-classification-with-transformer',
                  learning_rate=5.0e-5,
                  mini_batch_size=4,
                  )

Old syntax (from tutorial):

import torch
from torch.optim.lr_scheduler import OneCycleLR

from flair.data import Corpus
from flair.datasets import TREC_6
from flair.embeddings import TransformerDocumentEmbeddings
from flair.models import TextClassifier
from flair.trainers import ModelTrainer

# 1. get the corpus
corpus: Corpus = TREC_6()

# 2. what label do we want to predict?
label_type = 'question_class'

# 3. create the label dictionary
label_dict = corpus.make_label_dictionary(label_type=label_type)

# 4. initialize transformer document embeddings (many models are available)
document_embeddings = TransformerDocumentEmbeddings('distilbert-base-uncased', fine_tune=True)

# 5. create the text classifier
classifier = TextClassifier(document_embeddings, label_dictionary=label_dict, label_type=label_type)

# 6. initialize trainer with AdamW optimizer
trainer = ModelTrainer(classifier, corpus, optimizer=torch.optim.AdamW)

# 7. run training with fine-tuning
trainer.train('resources/taggers/question-classification-with-transformer',
              learning_rate=5.0e-5,
              mini_batch_size=4,
              max_epochs=10,
              scheduler=OneCycleLR,
              embeddings_storage_mode='none',
              weight_decay=0.,
              )