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
