fine_tuning.py

@@ -1,59 +0,0 @@
-import random
-import torch
-from transformers import (
-    AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer
-)
-
-class DataWrapper(torch.utils.data.Dataset):
-    def __init__(self, encodings, labels):
-        self.encodings = encodings
-        self.labels = labels
-
-    def __getitem__(self, idx):
-        item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
-        item['labels'] = torch.tensor(self.labels[idx])
-        return item
-
-    def __len__(self):
-        return len(self.labels)
-
-def read_data(file_path):
-    with open(file_path) as f:
-        return f.readlines()
-
-def wirte_output(file_path, data):
-    with open(file_path,  'w') as writer:
-        for result in trainer.predict(data):
-            writer.write(f"{str(result)}\n")
-
-print("STEP 1 - READ DATA")
-X_train = read_data('train/in.tsv')
-y_train = read_data('train/expected.tsv')
-X_dev = read_data('dev-0/in.tsv')
-X_test = read_data('test-A/in.tsv')
-
-print("STEP 2 - SHUFFLE")
-data_train = list(zip(X_train, y_train))
-data_train = random.sample(data_train, 15000)
-
-
-print("STEP 3 - FINE TUNING")
-tokenizer = AutoTokenizer.from_pretrained("roberta-base")
-
-train_encodings = tokenizer([text[0] for text in data_train], truncation=True, padding=True)
-train_dataset = DataWrapper(train_encodings, [int(text[1]) for text in data_train])
-
-model = AutoModelForSequenceClassification.from_pretrained("roberta-base", num_labels=2)
-args = TrainingArguments("model")
-
-device = torch.device("cpu")
-# device = torch.device("cuda")
-model.to(device)
-
-trainer = Trainer(model=model, args=args, train_dataset=train_dataset)
-trainer.train()
-
-print("STEP 4 - WRITE OUTPUT")
-wirte_output('train/out.tsv', X_train)
-wirte_output('dev-0/out.tsv', X_dev)
-wirte_output('test-A/out.tsv', X_test)

geval_results.txt

@@ -1,5 +0,0 @@
-Likelihood	0.0000
-Accuracy	0.7517
-F1.0	0.6119
-Precision	0.6848
-Recall	0.5531

log_reg.py

@@ -1,94 +0,0 @@
-import numpy as np
-import pandas as pd
-import torch
-import csv
-import gensim.downloader
-import torch
-from nltk import word_tokenize
-
-class NeuralNetwork(torch.nn.Module):
-    def __init__(self, input_size, hidden_size, num_classes):
-        super(NeuralNetwork, self).__init__()
-        self.l1 = torch.nn.Linear(input_size, hidden_size)
-        self.l2 = torch.nn.Linear(hidden_size, num_classes)
-
-    def forward(self, x):
-        x = self.l1(x)
-        x = torch.relu(x)
-        x = self.l2(x)
-        x = torch.sigmoid(x)
-        return x
-
-print('STEP 1 - LOAD DATA')
-names = ['content', 'id', 'label']
-train_data_content = pd.read_table('train/in.tsv', error_bad_lines=False, header=None, quoting=csv.QUOTE_NONE, names=names[:2])
-train_data_labels = pd.read_table('train/expected.tsv', error_bad_lines=False, header=None, quoting=csv.QUOTE_NONE, names=names[2:])
-dev_data = pd.read_table('dev-0/in.tsv', error_bad_lines=False, header=None, quoting=csv.QUOTE_NONE, names=names[:2])
-test_data = pd.read_table('test-A/in.tsv', error_bad_lines=False, header=None, quoting=csv.QUOTE_NONE, names=names[:2])
-
-print('STEP 2 - SET PARAMS')
-hidden_size = int(input('Hidden units size: ') or '600')
-epochs = int(input("Epochs: ") or '5')
-batch_size = int(input("Batch size: ") or '15')
-
-print('STEP 3 - PREPROCESSING')
-# lowercase all content
-X_train = train_data_content['content'].str.lower()
-y_train = train_data_labels['label']
-X_dev = dev_data['content'].str.lower()
-X_test = test_data['content'].str.lower()
-
-# tokenize datasets
-X_train = [word_tokenize(content) for content in X_train]
-X_dev = [word_tokenize(content) for content in X_dev]
-X_test = [word_tokenize(content) for content in X_test]
-
-# use Google word2vec algorithm
-word2vec = gensim.downloader.load('word2vec-google-news-300')
-X_train = [np.mean([word2vec[word] for word in content if word in word2vec] or [np.zeros(300)], axis=0) for content in X_train]
-X_dev = [np.mean([word2vec[word] for word in content if word in word2vec] or [np.zeros(300)], axis=0) for content in X_dev]
-X_test = [np.mean([word2vec[word] for word in content if word in word2vec] or [np.zeros(300)], axis=0) for content in X_test]
-
-print('STEP 4 - MODEL TRAINING')
-#prepare neural model
-
-model = NeuralNetwork(300, hidden_size, 1)
-criterion = torch.nn.BCELoss()
-optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
-
-for epoch in range(epochs):
-    model.train()
-    for i in range(0, y_train.shape[0], batch_size):
-        X = X_train[i:i+batch_size]
-        X = torch.tensor(X)
-        y = y_train[i:i+batch_size]
-        y = torch.tensor(y.astype(np.float32).to_numpy()).reshape(-1, 1)
-        outputs = model(X.float())
-        loss = criterion(outputs, y)
-        optimizer.zero_grad()
-        loss.backward()
-        optimizer.step()
-
-print('STEP 5 - PREDICTION')
-y_dev, y_test = [], []
-model.eval()
-with torch.no_grad():
-    for i in range(0, len(X_dev), batch_size):
-        X = X_dev[i:i+batch_size]
-        X = torch.tensor(X)
-        outputs = model(X.float())
-        prediction = (outputs > 0.5)
-        y_dev += prediction.tolist()
-    for i in range(0, len(X_test), batch_size):
-        X = X_test[i:i+batch_size]
-        X = torch.tensor(X)
-        outputs = model(X.float())
-        y = (outputs > 0.5)
-        y_test += prediction.tolist()
-
-print('STEP 6 - EXPORT RESULTS')
-# export results to tsv
-y_dev = np.asarray(y_dev, dtype=np.int32)
-y_test = np.asarray(y_test, dtype=np.int32)
-y_dev.tofile('./dev-0/out.tsv', sep='\n')
-y_test.tofile('./test-A/out.tsv', sep='\n')

output_geval_fine.txt

@@ -1,5 +0,0 @@
-Likelihood	0.0000
-Accuracy	0.8253
-F1.0	0.7472
-Precision	0.7659
-Recall	0.7294