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baseline_roberta-large.py
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from datasets import load_dataset, load_metric
from dataset import load
from datasets import Dataset, Features, ClassLabel, Value
from transformers import AutoTokenizer
from transformers import AutoModelForTokenClassification, TrainingArguments, Trainer
from sklearn.metrics import accuracy_score, precision_recall_fscore_support, classification_report
import numpy as np
import numpy.ma as ma
from transformers import DataCollatorForTokenClassification
from tqdm import tqdm
import datetime
import random
task = "task1" # Should be one of "ner", "pos" or "chunk"
model_checkpoint ="roberta-large" #
run_name= f"{model_checkpoint}-{task}-multi"
run_name = run_name.replace("/","-") + " " + str(datetime.datetime.now())[:-7]
batch_size = 8
label_all_tokens = True
data_factor =1 # train and test on x percent of the data
## load data
val_data = load("data/sepp_nlg_2021_train_dev_data.zip","dev","de")
val_data += load("data/sepp_nlg_2021_train_dev_data.zip","dev","en")
val_data += load("data/sepp_nlg_2021_train_dev_data.zip","dev","fr")
val_data += load("data/sepp_nlg_2021_train_dev_data.zip","dev","it")
train_data = load("data/sepp_nlg_2021_train_dev_data.zip","train","de")
train_data += load("data/sepp_nlg_2021_train_dev_data.zip","train","en")
train_data += load("data/sepp_nlg_2021_train_dev_data.zip","train","fr")
train_data += load("data/sepp_nlg_2021_train_dev_data.zip","train","it")
random.shuffle(train_data)
random.shuffle(val_data)
## tokenize data
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, strip_accent=False, add_prefix_space=True)
def tokenize_and_align_data(data,stride=0):
tokenizer_settings = {'is_split_into_words':True,'return_offsets_mapping':True,
'padding':False, 'truncation':True, 'stride':stride,
'max_length':tokenizer.model_max_length, 'return_overflowing_tokens':True}
tokenized_inputs = tokenizer(data[0], **tokenizer_settings)
labels = []
for i,document in enumerate(tokenized_inputs.encodings):
doc_encoded_labels = []
last_word_id = None
for word_id in document.word_ids:
if word_id == None: #or last_word_id == word_id:
doc_encoded_labels.append(-100)
else:
#document_id = tokenized_inputs.overflow_to_sample_mapping[i]
#label = examples[task][document_id][word_id]
label = data[1][word_id]
doc_encoded_labels.append(int(label))
last_word_id = word_id
labels.append(doc_encoded_labels)
tokenized_inputs["labels"] = labels
return tokenized_inputs
def to_dataset(data,stride=0):
labels, token_type_ids, input_ids, attention_masks = [],[],[],[]
for item in tqdm(data):
result = tokenize_and_align_data(item,stride=stride)
labels += result['labels']
input_ids += result['input_ids']
attention_masks += result['attention_mask']
return Dataset.from_dict({'labels': labels, 'input_ids':input_ids, 'attention_mask':attention_masks})
train_data = train_data[:int(len(train_data)*data_factor)] # limit data to x%
print("tokenize training data")
tokenized_dataset_train = to_dataset(train_data,stride=100)
del train_data
print("tokenize validation data")
val_data = val_data[:int(len(val_data)*data_factor)] # limit data to x%
tokenized_dataset_val = to_dataset(val_data)
del val_data
## metrics
def compute_metrics_sklearn(pred):
mask = np.less(pred.label_ids,0) # mask out -100 values
labels = ma.masked_array(pred.label_ids,mask).compressed()
preds = ma.masked_array(pred.predictions.argmax(-1),mask).compressed()
precision, recall, f1, _ = precision_recall_fscore_support(labels, preds, average='binary')
acc = accuracy_score(labels, preds)
return {
'f1': f1,
'precision': precision,
'recall': recall,
'accuracy':acc,
}
## train model
model = AutoModelForTokenClassification.from_pretrained(model_checkpoint, num_labels=2)
args = TrainingArguments(
output_dir=f"models/{run_name}/checkpoints",
run_name=run_name,
evaluation_strategy = "epoch",
learning_rate=5e-5,
per_device_train_batch_size=batch_size,
per_device_eval_batch_size=batch_size,
gradient_accumulation_steps=1,
num_train_epochs=2,
adafactor=True,
weight_decay=0.005,
#weight_decay=2.4793153505992856e-11,
#adam_epsilon=5.005649261324263e-10,
warmup_steps=50,
#lr_scheduler_type="cosine",
report_to=["tensorboard"],
logging_dir='runs/'+run_name, # directory for storing logs
logging_first_step=True,
logging_steps=100,
save_steps= 10000,
save_total_limit=10,
seed=16,
fp16=True
)
data_collator = DataCollatorForTokenClassification(tokenizer)
trainer = Trainer(
model,
args,
train_dataset=tokenized_dataset_train,
eval_dataset=tokenized_dataset_val,
data_collator=data_collator,
tokenizer=tokenizer,
compute_metrics=compute_metrics_sklearn
)
trainer.train()
trainer.save_model(f"models/{run_name}/final")