Upload balanced_train_full.py

balanced_train_full.py

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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()