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import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0,1"
from transformers import CamembertTokenizer, CamembertForSequenceClassification, CamembertConfig
from transformers import Trainer, TrainingArguments
import pandas as pd
import numpy as np
from loadDataSet import loadData, labels_to_numeric
from helpers import compute_max_sent_length, get_device, set_seed
from bert_utils import (
FrenchDataset,
compute_metrics,
)
from nltk.tokenize import sent_tokenize
set_seed(1)
if __name__ == "__main__":
# Device
device = get_device()
# Paths
base_path = "../code/"#"/home/mgaman/projects/french_dialect/data/Corpus/"
train_path = base_path + "train_slices.txt"
val_path = base_path + "val_slices.txt"
# Load the data
trainSamples, trainLabels = loadData("train", train_path)
valSamples, valLabels = loadData("validation", val_path)
print("Initial train size: %d" % len(trainSamples))
print("Val size: %d" % len(valSamples))
# Load the CamemBERT tokenizer
print("Loading CamemBERT tokenizer...")
tokenizer = CamembertTokenizer.from_pretrained("camembert-base")
# Compute max sentence length
# Sentence length: Max = 512; Min = 3; Average = 445.26780711145534
# For 3-sentence paragraphs: Average sentence length: 102.64167598045637
#compute_max_sent_length(tokenizer, trainSamples)
# Use approx the average in the dataset
max_len = 128
# Labels to numeric format
# 0 - BE, 1 - CA, 2 - CH, 3 - FR
trainLabels = labels_to_numeric(trainLabels)
valLabels = labels_to_numeric(valLabels)
# Tokenize / Prepare the training set
train_encodings = tokenizer(trainSamples, truncation=True, padding=True, max_length=max_len)
# Tokenize / Prepare the validation set
valid_encodings = tokenizer(valSamples, truncation=True, padding=True, max_length=max_len)
# Convert our tokenized data into a torch Dataset
train_dataset = FrenchDataset(train_encodings, trainLabels)
valid_dataset = FrenchDataset(valid_encodings, valLabels)
# Load the model and pass to device
config = CamembertConfig.from_pretrained("camembert-base", output_hidden_states=True)
model = CamembertForSequenceClassification.from_pretrained("camembert-base", num_labels=4).to(device)
# Train args
training_args = TrainingArguments(
output_dir="./bert_models_saved/out_fold", # output directory
num_train_epochs=30, # total number of training epochs
per_device_train_batch_size=32, # batch size per device during training
per_device_eval_batch_size=32, # 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
load_best_model_at_end=True, # load the best model when finished training (default metric is loss)
# but you can specify `metric_for_best_model` argument to change to accuracy or other metric
logging_steps=250, # log & save weights each logging_steps
eval_steps=250,
#learning_rate=5e-5,
save_total_limit=5,
save_strategy="steps",
evaluation_strategy="steps", # evaluate each `logging_steps`
)
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=valid_dataset, # evaluation dataset
compute_metrics=compute_metrics, # the callback that computes metrics of interest
)
# Train the model
trainer.train()
# Save best only
trainer.save_model("./bert_models_saved/out_fold")
# Evaluate the best performing model
trainer.evaluate()
# Save for later
model.save_pretrained("./bert_models_saved/best_model/")
tokenizer.save_pretrained("./bert_models_saved/best_model/")
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