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import pandas as pd
df = pd.read_feather("//media/data/mbti-reddit/disprop_sample100k_total.feather") #change this to proper path
#'/content/drive/MyDrive/Colab Notebooks/clickbait_hold_X.csv'
df=df.drop(columns=['authors','subreddit'])
df=df.sample(80000, random_state=1) #random sampling
df['labels'] = df['labels'].replace(['INTP','ISTP','ENTP','ESTP','INFP','ISFP','ENFP','ESFP', \
'INTJ','ISTJ','ENTJ','ESTJ','INFJ','ISFJ','ENFJ','ESFJ'], \
[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15])
df=df.rename(columns={'labels':'labels','comments':'text'})
from datasets import Dataset
dataset = Dataset.from_pandas(df)
dataset.shuffle(seed=27)
split_set = dataset.train_test_split(test_size=0.2)
from transformers import AlbertTokenizer, AlbertModel
tokenizer = AlbertTokenizer.from_pretrained('albert-base-v2')
from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer
model = AutoModelForSequenceClassification.from_pretrained("albert-base-v2", num_labels=16)
def preprocess_function(examples):
return tokenizer(examples["text"], truncation=True)
tokenized_dataset = split_set.map(preprocess_function, batched=True)
from transformers import DataCollatorWithPadding
#tokenized_datasets = tokenized_datasets.remove_columns(books_dataset["train"].column_names)
data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
import evaluate
import numpy as np
def compute_metrics(eval_preds):
metric = evaluate.combine([
evaluate.load("precision"),
evaluate.load("recall")])
#evaluate.load("precision", average="weighted"),
#evaluate.load("recall", average="weighted")])
logits, labels = eval_preds
predictions = np.argmax(logits, axis=-1)
return metric.compute(predictions=predictions, references=labels, average='weighted')
training_args = TrainingArguments(
evaluation_strategy="epoch",
#save_strategy="epoch",
output_dir="/home/deimann/mbti-project/balanced_train",
#save_total_limit=5,
#load_best_model_at_end = True,
learning_rate=2e-5,#2e
per_device_train_batch_size=36 ,#16
per_device_eval_batch_size=16,#16
num_train_epochs=10,
weight_decay=0.01,
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_dataset["train"],
eval_dataset=tokenized_dataset["test"],
tokenizer=tokenizer,
data_collator=data_collator,
#compute_metrics=compute_metrics,
)
trainer.train() |