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prompt-tuning.py
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prompt-tuning.py
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import torch
import torch.nn as nn
from torch.functional import F
import transformers as ts
from datasets import Dataset
import numpy as np
import matplotlib.pyplot as plt
import os
import pickle
torch.cuda.empty_cache()
SAVE_PATH = "PATH_TO_CHECKPOINTS/biomedical_model_checkpoints/prompt-biobert/"
MODEL_PATH = "dmis-lab/biobert-v1.1"
os.environ["WANDB_DISABLED"] = "true"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
train_dict = pickle.load(open("train_dict" , "rb")) #Path to Training File
val_dict = pickle.load(open("val_dict" , "rb")) #Path to Validation File
train_dataset = Dataset.from_dict(train_dict)
val_dataset = Dataset.from_dict(val_dict)
tokenizer = ts.AutoTokenizer.from_pretrained(MODEL_PATH)
data_collator = ts.DataCollatorWithPadding(tokenizer=tokenizer , return_tensors="pt")
def mappingFunction(dataset):
return tokenizer(dataset["text"])
final_train_dataset = train_dataset.map(mappingFunction , batched=True)
final_val_dataset = val_dataset.map(mappingFunction , batched=True)
model = ts.AutoModelForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=1)
model.add_adapter("prefix_tuning" , config=ts.adapters.PrefixTuningConfig(flat=False, prefix_length=30))
model.train_adapter("prefix_tuning")
totalCount = 0
trainableCount = 0
for name , param in model.named_parameters():
totalCount += param.numel()
if param.requires_grad:
trainableCount += param.numel()
print("All Params Count = " + str(totalCount/1e6))
print("Trainable Params Count = " + str(trainableCount/1e6))
print(str(trainableCount/totalCount * 100))
def collator_function(dataset):
keys = dataset[0].keys()
output_dict = {
key: [] for key in keys
}
for item in dataset:
for key in keys:
output_dict[key].append(item[key])
labels = torch.tensor(output_dict.pop("label"))
output_dict.pop("text")
collator_output = data_collator(output_dict)
collator_output["labels"] = labels
return collator_output
def train():
training_arguments = ts.TrainingArguments(
"output/",
save_steps= 3000,
num_train_epochs=10,
learning_rate=1e-4,
lr_scheduler_type="cosine",
per_device_train_batch_size=64,
per_device_eval_batch_size=64,
weight_decay=0.01,
remove_unused_columns=False,
logging_steps=100,
seed=123,
)
trainer = ts.Trainer(
model=model,
args=training_arguments,
train_dataset=final_train_dataset,
eval_dataset=final_val_dataset,
data_collator=collator_function,
)
trainer.train()
trainer.save_model(SAVE_PATH)
import seaborn as sn
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
all_labels = []
all_preds = []
model.cpu()
model.eval()
for sample in final_val_dataset:
input_sample = collator_function([sample])
output = model(**input_sample)
predicted_label = round(float(torch.sigmoid(output["logits"]).view(-1)))
all_labels.append(sample["label"])
all_preds.append(predicted_label)
array = confusion_matrix(all_labels, all_preds)
disp = ConfusionMatrixDisplay(confusion_matrix=array,display_labels=[0,1])
disp.plot()
plt.show()
train()