Hi guys
First of all, what I am trying to do: I want to fine-tune a BERT Model on domain specific language and in a second step further fine-tune it for classification. To do so, I want to use a pretrained model, what forces me to use the original tokenizer (cannot use own vocab). I would like to share my code with you and have your opinions (are there mistakes?):
First we load the pre-trained tokenizer and model:
from transformers import BertTokenizer, BertForMaskedLM

tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForMaskedLM.from_pretrained('bert-base-uncased')

We are using BertForMaskedLM since the first fine-tuning step is to train the model on domain specific language (a text file with one sentence per line). Next we are reading the text file:
from transformers import LineByLineTextDataset
dataset = LineByLineTextDataset(
        tokenizer=tokenizer,
        file_path="test.txt",
        block_size=128
)

and define the data collator as:
from transformers import DataCollatorForLanguageModeling

data_collator = DataCollatorForLanguageModeling(
    tokenizer=tokenizer, mlm=True, mlm_probability=0.15
)

Finally we are training the model for MLM:
from transformers import Trainer, TrainingArguments

training_args = TrainingArguments(
    output_dir="./TestBERT",
    overwrite_output_dir=True,
    num_train_epochs=1,
    per_gpu_train_batch_size=16,
    save_steps=10_000,
    save_total_limit=2
)

trainer = Trainer(
    model=model,
    args=training_args,
    data_collator=data_collator,
    train_dataset=dataset
)

trainer.train()

We save the model and reload it for sequence classification (huggingface handles the heads):
from transformers import BertForSequenceClassification

trainer.save_model("./TestBERT")
model = BertForSequenceClassification.from_pretrained("./TestBERT", num_labels=2)

Finally we can fine-tune the model for sequence classification as usual. E.g.:
!wget http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz
!tar -xf aclImdb_v1.tar.gz


from pathlib import Path
def read_imdb_split(split_dir):
    split_dir = Path(split_dir)
    texts = []
    labels = []
    for label_dir in ["pos", "neg"]:
        for text_file in (split_dir/label_dir).iterdir():
            texts.append(text_file.read_text())
            labels.append(0 if label_dir is "neg" else 1)
    return texts, labels

train_texts, train_texts= read_imdb_split('aclImdb/train')
test_texts, test_labels = read_imdb_split('aclImdb/test')


from sklearn.model_selection import train_test_split
train_texts, val_texts, train_labels, val_labels = train_test_split(train_texts, further2, test_size=.2)


train_encodings = tokenizer(train_texts, truncation=True, padding=True)
val_encodings = tokenizer(val_texts, truncation=True, padding=True)
test_encodings = tokenizer(test_texts, truncation=True, padding=True)


import torch
class IMDbDataset(torch.utils.data.Dataset):
    def __init__(self, encodings, labels):
        self.encodings = encodings
        self.labels = labels
    def __getitem__(self, idx):
        item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
        item['labels'] = torch.tensor(self.labels[idx])
        return item
    def __len__(self):
        return len(self.labels)

train_dataset = IMDbDataset(train_encodings, train_labels)
val_dataset = IMDbDataset(val_encodings, val_labels)
test_dataset = IMDbDataset(test_encodings, test_labels)


from transformers import Trainer, TrainingArguments

training_args = TrainingArguments(
    output_dir='./results',          # output directory
    num_train_epochs=3,              # total number of training epochs
    per_device_train_batch_size=8,  # batch size per device during training
    per_device_eval_batch_size=8,   # batch size for evaluation
    warmup_steps=500,                # number of warmup steps for learning rate scheduler
    weight_decay=0.01,               # strength of weight decay
    logging_dir='./logs',            # directory for storing logs
    logging_steps=10,
)

trainer = Trainer(
    model=model,                         # the instantiated 🤗 Transformers model to be trained
    args=training_args,                  # training arguments, defined above
    train_dataset=train_dataset,         # training dataset
    eval_dataset=val_dataset             # evaluation dataset
)

trainer.train()

Does anyone detect any obvious mistakes I am making or is this the correct proceeding? Further I want to freeze some layers during the first fine-tuning step to avoid forgetting (of the pre-trained learning). I assume I would have to write my own trainer for it (will do any maybe comment on this post).
Best