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https://github.com/huggingface/transformers.git
synced 2025-08-03 03:31:05 +06:00
Added tqdm to preprocessing
This commit is contained in:
piero
Julien Chaumond
parent
afc7dcd94d
commit
611961ade7
@ -18,13 +18,14 @@ import torch.utils.data as data
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from nltk.tokenize.treebank import TreebankWordDetokenizer
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from torchtext import data as torchtext_data
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from torchtext import datasets
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from tqdm import tqdm, trange
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from transformers import GPT2Tokenizer, GPT2LMHeadModel
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torch.manual_seed(0)
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np.random.seed(0)
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EPSILON = 1e-10
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device = 'cpu'
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device = "cpu"
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example_sentence = "This is incredible! I love it, this is the best chicken I have ever had."
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max_length_seq = 100
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@ -109,8 +110,8 @@ class Dataset(data.Dataset):
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def __getitem__(self, index):
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"""Returns one data pair (source and target)."""
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data = {}
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data['X'] = self.X[index]
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data['y'] = self.y[index]
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data["X"] = self.X[index]
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data["y"] = self.y[index]
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return data
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@ -133,8 +134,8 @@ def collate_fn(data):
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for key in data[0].keys():
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item_info[key] = [d[key] for d in data]
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x_batch, _ = pad_sequences(item_info['X'])
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y_batch = torch.tensor(item_info['y'], dtype=torch.long)
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x_batch, _ = pad_sequences(item_info["X"])
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y_batch = torch.tensor(item_info["y"], dtype=torch.long)
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return x_batch, y_batch
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@ -144,8 +145,8 @@ def cached_collate_fn(data):
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for key in data[0].keys():
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item_info[key] = [d[key] for d in data]
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x_batch = torch.cat(item_info['X'], 0)
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y_batch = torch.tensor(item_info['y'], dtype=torch.long)
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x_batch = torch.cat(item_info["X"], 0)
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y_batch = torch.tensor(item_info["y"], dtype=torch.long)
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return x_batch, y_batch
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@ -168,7 +169,7 @@ def train_epoch(data_loader, discriminator, optimizer,
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if batch_idx % log_interval == 0:
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print(
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'Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
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"Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(
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epoch + 1,
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samples_so_far, len(data_loader.dataset),
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100 * samples_so_far / len(data_loader.dataset), loss.item()
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@ -185,7 +186,7 @@ def evaluate_performance(data_loader, discriminator):
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input_t, target_t = input_t.to(device), target_t.to(device)
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output_t = discriminator(input_t)
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# sum up batch loss
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test_loss += F.nll_loss(output_t, target_t, reduction='sum').item()
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test_loss += F.nll_loss(output_t, target_t, reduction="sum").item()
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# get the index of the max log-probability
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pred_t = output_t.argmax(dim=1, keepdim=True)
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correct += pred_t.eq(target_t.view_as(pred_t)).sum().item()
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@ -193,8 +194,8 @@ def evaluate_performance(data_loader, discriminator):
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test_loss /= len(data_loader.dataset)
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print(
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'Performance on test set: '
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'Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)'.format(
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"Performance on test set: "
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"Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)".format(
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test_loss, correct, len(data_loader.dataset),
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100. * correct / len(data_loader.dataset)
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)
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@ -208,8 +209,8 @@ def predict(input_sentence, model, classes, cached=False):
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input_t = model.avg_representation(input_t)
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log_probs = model(input_t).data.cpu().numpy().flatten().tolist()
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print('Input sentence:', input_sentence)
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print('Predictions:', ", ".join(
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print("Input sentence:", input_sentence)
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print("Predictions:", ", ".join(
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"{}: {:.4f}".format(c, math.exp(log_prob)) for c, log_prob in
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zip(classes, log_probs)
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))
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@ -222,7 +223,7 @@ def get_cached_data_loader(dataset, batch_size, discriminator, shuffle=False):
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xs = []
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ys = []
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for batch_idx, (x, y) in enumerate(data_loader):
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for batch_idx, (x, y) in enumerate(tqdm(data_loader, ascii=True)):
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with torch.no_grad():
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x = x.to(device)
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avg_rep = discriminator.avg_representation(x).cpu().detach()
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@ -240,16 +241,16 @@ def get_cached_data_loader(dataset, batch_size, discriminator, shuffle=False):
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def train_discriminator(
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dataset, dataset_fp=None, pretrained_model='gpt2-medium',
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dataset, dataset_fp=None, pretrained_model="gpt2-medium",
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epochs=10, batch_size=64, log_interval=10,
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save_model=False, cached=False, no_cuda=False):
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global device
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device = "cuda" if torch.cuda.is_available() and not no_cuda else "cpu"
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print('Preprocessing {} dataset...'.format(dataset))
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print("Preprocessing {} dataset...".format(dataset))
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start = time.time()
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if dataset == 'SST':
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if dataset == "SST":
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idx2class = ["positive", "negative", "very positive", "very negative",
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"neutral"]
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class2idx = {c: i for i, c in enumerate(idx2class)}
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@ -271,7 +272,7 @@ def train_discriminator(
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x = []
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y = []
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for i in range(len(train_data)):
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for i in trange(len(train_data), ascii=True):
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seq = TreebankWordDetokenizer().detokenize(
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vars(train_data[i])["text"]
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)
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@ -283,7 +284,7 @@ def train_discriminator(
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test_x = []
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test_y = []
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for i in range(len(test_data)):
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for i in trange(len(test_data), ascii=True):
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seq = TreebankWordDetokenizer().detokenize(
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vars(test_data[i])["text"]
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)
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@ -301,7 +302,7 @@ def train_discriminator(
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"default_class": 2,
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}
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elif dataset == 'clickbait':
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elif dataset == "clickbait":
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idx2class = ["non_clickbait", "clickbait"]
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class2idx = {c: i for i, c in enumerate(idx2class)}
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@ -317,31 +318,33 @@ def train_discriminator(
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try:
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data.append(eval(line))
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except:
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print('Error evaluating line {}: {}'.format(
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print("Error evaluating line {}: {}".format(
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i, line
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))
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continue
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x = []
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y = []
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y = []
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for i, d in enumerate(data):
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try:
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seq = discriminator.tokenizer.encode(d["text"])
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with open("datasets/clickbait/clickbait_train_prefix.txt") as f:
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for i, line in enumerate(tqdm(f, ascii=True)):
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try:
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d = eval(line)
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seq = discriminator.tokenizer.encode(d["text"])
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if len(seq) < max_length_seq:
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seq = torch.tensor(
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[50256] + seq, device=device, dtype=torch.long
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)
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else:
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print("Line {} is longer than maximum length {}".format(
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i, max_length_seq
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))
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continue
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x.append(seq)
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y.append(d['label'])
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except:
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print("Error tokenizing line {}, skipping it".format(i))
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pass
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if len(seq) < max_length_seq:
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seq = torch.tensor(
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[50256] + seq, device=device, dtype=torch.long
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)
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else:
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print("Line {} is longer than maximum length {}".format(
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i, max_length_seq
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))
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continue
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x.append(seq)
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y.append(d["label"])
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except:
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print("Error evaluating / tokenizing"
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" line {}, skipping it".format(i))
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pass
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full_dataset = Dataset(x, y)
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train_size = int(0.9 * len(full_dataset))
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@ -358,7 +361,7 @@ def train_discriminator(
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"default_class": 1,
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}
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elif dataset == 'toxic':
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elif dataset == "toxic":
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idx2class = ["non_toxic", "toxic"]
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class2idx = {c: i for i, c in enumerate(idx2class)}
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@ -368,37 +371,29 @@ def train_discriminator(
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cached_mode=cached
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).to(device)
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with open("datasets/toxic/toxic_train.txt") as f:
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data = []
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for i, line in enumerate(f):
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try:
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data.append(eval(line))
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except:
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print('Error evaluating line {}: {}'.format(
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i, line
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))
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continue
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x = []
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y = []
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for i, d in enumerate(data):
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try:
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seq = discriminator.tokenizer.encode(d["text"])
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with open("datasets/toxic/toxic_train.txt") as f:
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for i, line in enumerate(tqdm(f, ascii=True)):
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try:
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d = eval(line)
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seq = discriminator.tokenizer.encode(d["text"])
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if len(seq) < max_length_seq:
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seq = torch.tensor(
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[50256] + seq, device=device, dtype=torch.long
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)
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else:
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print("Line {} is longer than maximum length {}".format(
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i, max_length_seq
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))
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continue
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x.append(seq)
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y.append(int(np.sum(d['label']) > 0))
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except:
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print("Error tokenizing line {}, skipping it".format(i))
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pass
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if len(seq) < max_length_seq:
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seq = torch.tensor(
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[50256] + seq, device=device, dtype=torch.long
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)
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else:
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print("Line {} is longer than maximum length {}".format(
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i, max_length_seq
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))
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continue
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x.append(seq)
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y.append(int(np.sum(d["label"]) > 0))
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except:
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print("Error evaluating / tokenizing"
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" line {}, skipping it".format(i))
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pass
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full_dataset = Dataset(x, y)
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train_size = int(0.9 * len(full_dataset))
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@ -415,18 +410,18 @@ def train_discriminator(
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"default_class": 0,
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}
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else: # if dataset == 'generic':
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else: # if dataset == "generic":
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# This assumes the input dataset is a TSV with the following structure:
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# class \t text
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if dataset_fp is None:
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raise ValueError('When generic dataset is selected, '
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'dataset_fp needs to be specified aswell.')
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raise ValueError("When generic dataset is selected, "
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"dataset_fp needs to be specified aswell.")
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classes = set()
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with open(dataset_fp) as f:
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csv_reader = csv.reader(f, delimiter='\t')
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for row in csv_reader:
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csv_reader = csv.reader(f, delimiter="\t")
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for row in tqdm(csv_reader, ascii=True):
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if row:
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classes.add(row[0])
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@ -442,8 +437,8 @@ def train_discriminator(
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x = []
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y = []
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with open(dataset_fp) as f:
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csv_reader = csv.reader(f, delimiter='\t')
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for i, row in enumerate(csv_reader):
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csv_reader = csv.reader(f, delimiter="\t")
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for i, row in enumerate(tqdm(csv_reader, ascii=True)):
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if row:
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label = row[0]
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text = row[1]
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@ -458,9 +453,10 @@ def train_discriminator(
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)
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else:
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print("Line {} is longer than maximum length {}".format(
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i, max_length_seq
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))
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print(
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"Line {} is longer than maximum length {}".format(
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i, max_length_seq
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))
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continue
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x.append(seq)
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@ -487,12 +483,14 @@ def train_discriminator(
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}
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end = time.time()
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print('Preprocessed {} data points'.format(
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print("Preprocessed {} data points".format(
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len(train_dataset) + len(test_dataset))
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)
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print("Data preprocessing took: {:.3f}s".format(end - start))
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if cached:
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print("Building representation cache...")
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start = time.time()
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train_loader = get_cached_data_loader(
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@ -524,7 +522,7 @@ def train_discriminator(
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for epoch in range(epochs):
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start = time.time()
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print('\nEpoch', epoch + 1)
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print("\nEpoch", epoch + 1)
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train_epoch(
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discriminator=discriminator,
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@ -553,31 +551,31 @@ def train_discriminator(
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"{}_classifier_head_epoch_{}.pt".format(dataset, epoch))
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if __name__ == '__main__':
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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description='Train a discriminator on top of GPT-2 representations')
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parser.add_argument('--dataset', type=str, default='SST',
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choices=('SST', 'clickbait', 'toxic', 'generic'),
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help='dataset to train the discriminator on.'
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'In case of generic, the dataset is expected'
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'to be a TSBV file with structure: class \\t text')
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parser.add_argument('--dataset_fp', type=str, default='',
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help='File path of the dataset to use. '
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'Needed only in case of generic datadset')
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parser.add_argument('--pretrained_model', type=str, default='gpt2-medium',
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help='Pretrained model to use as encoder')
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parser.add_argument('--epochs', type=int, default=10, metavar='N',
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help='Number of training epochs')
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parser.add_argument('--batch_size', type=int, default=64, metavar='N',
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help='input batch size for training (default: 64)')
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parser.add_argument('--log_interval', type=int, default=10, metavar='N',
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help='how many batches to wait before logging training status')
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parser.add_argument('--save_model', action='store_true',
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help='whether to save the model')
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parser.add_argument('--cached', action='store_true',
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help='whether to cache the input representations')
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parser.add_argument('--no_cuda', action='store_true',
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help='use to turn off cuda')
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description="Train a discriminator on top of GPT-2 representations")
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parser.add_argument("--dataset", type=str, default="SST",
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choices=("SST", "clickbait", "toxic", "generic"),
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help="dataset to train the discriminator on."
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"In case of generic, the dataset is expected"
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"to be a TSBV file with structure: class \\t text")
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parser.add_argument("--dataset_fp", type=str, default="",
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help="File path of the dataset to use. "
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"Needed only in case of generic datadset")
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parser.add_argument("--pretrained_model", type=str, default="gpt2-medium",
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help="Pretrained model to use as encoder")
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parser.add_argument("--epochs", type=int, default=10, metavar="N",
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help="Number of training epochs")
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parser.add_argument("--batch_size", type=int, default=64, metavar="N",
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help="input batch size for training (default: 64)")
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parser.add_argument("--log_interval", type=int, default=10, metavar="N",
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help="how many batches to wait before logging training status")
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parser.add_argument("--save_model", action="store_true",
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help="whether to save the model")
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parser.add_argument("--cached", action="store_true",
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help="whether to cache the input representations")
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parser.add_argument("--no_cuda", action="store_true",
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help="use to turn off cuda")
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args = parser.parse_args()
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train_discriminator(**(vars(args)))
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