IoannisTr/Tech_Stocks_Trading_Assistant

1import os2os.environ["TOKENIZERS_PARALLELISM"] = "false"3os.environ['WANDB_DISABLED'] = "true"4import pandas as pd5from sklearn.preprocessing import LabelEncoder6from sklearn.model_selection import train_test_split7from transformers import (8    AutoTokenizer, 9    DataCollatorWithPadding,10    TrainingArguments,11    Trainer,12    AutoModelForSequenceClassification13)14from datasets import Dataset15 16#######################################17########## FinBERT training ###########18#######################################19 20class args:21    model = 'ProsusAI/finbert'22 23df = pd.read_csv('all-data.csv', 24                 names = ['labels','messages'],25                 encoding='ISO-8859-1')26 27df = df[['messages', 'labels']]28 29le = LabelEncoder()30df['labels'] = le.fit_transform(df['labels'])31 32X, y = df['messages'].values, df['labels'].values33 34xtrain, xtest, ytrain, ytest = train_test_split(X, y, test_size=0.1) 35xtrain, xvalid, ytrain, yvalid = train_test_split(xtrain, ytrain, test_size=0.2) 36 37train_dataset_raw = Dataset.from_dict({'text':xtrain, 'labels':ytrain})38valid_dataset_raw = Dataset.from_dict({'text':xvalid, 'labels':yvalid})39 40tokenizer = AutoTokenizer.from_pretrained(args.model)41 42def tokenize_fn(examples):43    return tokenizer(examples['text'], truncation=True)44 45train_dataset = train_dataset_raw.map(tokenize_fn, batched=True)46valid_dataset = valid_dataset_raw.map(tokenize_fn, batched=True)47 48data_collator = DataCollatorWithPadding(tokenizer)49 50model = AutoModelForSequenceClassification.from_pretrained(args.model)51 52train_args = TrainingArguments(53    './Finbert Trained/',54    per_device_train_batch_size=16,55    per_device_eval_batch_size=2*16,56    num_train_epochs=5,57    learning_rate=2e-5,58    weight_decay=0.01,59    warmup_ratio=0.1,    60    do_eval=True,61    do_train=True,62    do_predict=True,63    evaluation_strategy='epoch',64    save_strategy="no",65)66 67trainer = Trainer(68    model,69    train_args,70    train_dataset=train_dataset,71    eval_dataset=valid_dataset,72    data_collator=data_collator,73    tokenizer=tokenizer 74)75 76trainer.train()77 78# saving the model and the weights79model.save_pretrained('fine_tuned_FinBERT')80# saving the tokenizer81tokenizer.save_pretrained("fine_tuned_FinBERT/tokenizer/")82 83