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import os
os.environ["TOKENIZERS_PARALLELISM"] = "false"
os.environ['WANDB_DISABLED'] = "true"
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from transformers import (
AutoTokenizer,
DataCollatorWithPadding,
TrainingArguments,
Trainer,
AutoModelForSequenceClassification
)
from datasets import Dataset
#######################################
########## FinBERT training ###########
#######################################
class args:
model = 'ProsusAI/finbert'
df = pd.read_csv('all-data.csv',
names = ['labels','messages'],
encoding='ISO-8859-1')
df = df[['messages', 'labels']]
le = LabelEncoder()
df['labels'] = le.fit_transform(df['labels'])
X, y = df['messages'].values, df['labels'].values
xtrain, xtest, ytrain, ytest = train_test_split(X, y, test_size=0.1)
xtrain, xvalid, ytrain, yvalid = train_test_split(xtrain, ytrain, test_size=0.2)
train_dataset_raw = Dataset.from_dict({'text':xtrain, 'labels':ytrain})
valid_dataset_raw = Dataset.from_dict({'text':xvalid, 'labels':yvalid})
tokenizer = AutoTokenizer.from_pretrained(args.model)
def tokenize_fn(examples):
return tokenizer(examples['text'], truncation=True)
train_dataset = train_dataset_raw.map(tokenize_fn, batched=True)
valid_dataset = valid_dataset_raw.map(tokenize_fn, batched=True)
data_collator = DataCollatorWithPadding(tokenizer)
model = AutoModelForSequenceClassification.from_pretrained(args.model)
train_args = TrainingArguments(
'./Finbert Trained/',
per_device_train_batch_size=16,
per_device_eval_batch_size=2*16,
num_train_epochs=5,
learning_rate=2e-5,
weight_decay=0.01,
warmup_ratio=0.1,
do_eval=True,
do_train=True,
do_predict=True,
evaluation_strategy='epoch',
save_strategy="no",
)
trainer = Trainer(
model,
train_args,
train_dataset=train_dataset,
eval_dataset=valid_dataset,
data_collator=data_collator,
tokenizer=tokenizer
)
trainer.train()
# saving the model and the weights
model.save_pretrained('fine_tuned_FinBERT')
# saving the tokenizer
tokenizer.save_pretrained("fine_tuned_FinBERT/tokenizer/")
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