-
Notifications
You must be signed in to change notification settings - Fork 1
/
training.py
302 lines (251 loc) · 12.9 KB
/
training.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
"""
This code is for language identification using SentenceTransformer, a pre-trained transformer-based model for
natural language processing. The code first loads the Papluca Language Identification dataset using
HuggingFace's load_dataset method and splits it into train, validation, and test sets. The code then uses
the pretrained SentenceTransformer model as a feature extractor to generate embeddings from the texts and
trains a single Linear layer on top of the embeddings for the multiclass language identification task.
The code also uses PyTorch Lightning for training and evaluating the model.
GitHub repository: https://github.com/kayodeolaleye/multilang-identification
"""
from datasets import load_dataset
import random
import argparse
from datetime import datetime
from sklearn.preprocessing import LabelEncoder
from transformers import AutoModel, AutoTokenizer
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
from datasets import load_dataset
import pytorch_lightning as pl
import numpy as np
import random
import os
import umap
from torch.utils.data import TensorDataset
from pytorch_lightning.callbacks import ModelCheckpoint
from pytorch_lightning import Trainer
import matplotlib.pyplot as plt
from pytorch_lightning import Trainer, seed_everything
from pytorch_lightning.callbacks import ModelCheckpoint
from pytorch_lightning.loggers import WandbLogger
def parse_args():
parser = argparse.ArgumentParser(description='Language Identification')
parser.add_argument('--model_name', type=str, help='pretrained model to use')
parser.add_argument('--dropout', type=float, default=0.0, help='Choose dropout value to use')
parser.add_argument('--epochs', default=1, type=int, help='Number of maximum epochs')
parser.add_argument('--batch_size', default=32, type=int, help='Batch size')
parser.add_argument('--num_workers', default=4, type=int, help='Number of workers to generate minibatch')
parser.add_argument('--lr', default=1e-3, type=float, help='Init learning rate')
parser.add_argument('--num_classes', type=int, default=20, help='Number of classes')
parser.add_argument('--sample_size', type=int, default=100, help='Number of documents to use for each language')
parser.add_argument('--seed', type=int, default=42, help='Random seed')
args = parser.parse_args()
return args
def get_subset_dict(languages, subset_texts, subset_labels):
"""
creates a dictionary where the keys are the language names,
and the values are a list of text samples for each language.
"""
subset_dict = {}
for lang in languages:
subset_dict[lang] = [text for text, label in zip(subset_texts, subset_labels) if label == lang]
return subset_dict
class SentenceBERTDataset(torch.utils.data.Dataset):
def __init__(self, text_lst):
self.text_lst = text_lst
def __len__(self):
return len(self.text_lst)
def __getitem__(self, idx):
return self.text_lst[idx]
def encode_labels(subset_dict):
"""
encodes the language labels as integers and returns both the encoded labels
and their corresponding decoded labels
"""
label_encoder = LabelEncoder()
labels = list(subset_dict.keys())
label_encoder.fit(labels)
encoded_labels = label_encoder.transform(labels)
# decoded_labels = label_encoder.inverse_transform(encoded_labels)
return encoded_labels, labels
def get_embeddings_dict(languages, subset_dict, pretrained_model, tokenizer, max_length, sample_size=100):
"""
generates embeddings for a given dictionary of texts using the pretrained model.
"""
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
pretrained_model = pretrained_model.to(device)
embeddings_dict = {}
for lang in languages:
few_samples = random.sample(subset_dict[lang], k=sample_size)
dataset = SentenceBERTDataset(few_samples)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=32, shuffle=False)
tokens = tokenizer.batch_encode_plus(few_samples, max_length=max_length, padding="max_length", truncation=True)
tokens = {k: torch.tensor(v).to(device) for k, v in tokens.items()}
embeddings = pretrained_model(tokens["input_ids"]).pooler_output.cpu().numpy()
if lang not in embeddings_dict:
embeddings_dict[lang] = embeddings
else:
embeddings_dict[lang] = np.concatenate((embeddings_dict[lang], embeddings), axis=0)
return embeddings_dict
def plot_embeddings(embeddings, labels, save_path):
"""
plots the embeddings using UMAP
"""
encoded_labels, labels = encode_labels(labels)
print("Plotting embeddings...")
reducer = umap.UMAP()
embedding = reducer.fit_transform(embeddings)
# plt.figure(figsize=(10, 10))
plt.scatter(embedding[:, 0], embedding[:, 1], c=encoded_labels, s=0.9, cmap="Spectral")
plt.savefig(os.path.join(save_path, "umap.png"))
def concat_embeddings(embeddings_dict):
embeddings = []
labels = []
for key, value in embeddings_dict.items():
embeddings.extend(value)
labels.extend([key] * len(value))
return embeddings, labels
def encode_labels(labels):
label_encoder = LabelEncoder()
label_encoder.fit(labels)
encoded_labels = label_encoder.transform(labels)
return encoded_labels, labels
class LanguageIdentifierDataModule(pl.LightningDataModule):
def __init__(self, train_data, train_labels, val_data, val_labels, test_data, test_labels, batch_size, num_workers):
super().__init__()
self.train_data = train_data
self.train_labels = train_labels
self.val_data = val_data
self.val_labels = val_labels
self.test_data = test_data
self.test_labels = test_labels
self.batch_size = batch_size
self.num_workers = num_workers
def setup(self, stage=None):
# Create a TensorDataset object with the data and target tensors
self.train_dataset = TensorDataset(torch.tensor(self.train_data), torch.LongTensor(self.train_labels))
self.valid_dataset = TensorDataset(torch.tensor(self.val_data), torch.LongTensor(self.val_labels))
self.test_dataset = TensorDataset(torch.tensor(self.test_data), torch.LongTensor(self.test_labels))
def train_dataloader(self):
return torch.utils.data.DataLoader(self.train_dataset, batch_size=self.batch_size, shuffle=True, num_workers=self.num_workers)
def val_dataloader(self):
return torch.utils.data.DataLoader(self.valid_dataset, batch_size=self.batch_size, shuffle=False, num_workers=self.num_workers)
def test_dataloader(self):
return torch.utils.data.DataLoader(self.test_dataset, batch_size=self.batch_size, shuffle=False, num_workers=self.num_workers)
class LanguageIdentifier(pl.LightningModule):
def __init__(self):
super(LanguageIdentifier, self).__init__()
self.classifier = nn.Sequential(
nn.Linear(384, 20),
nn.Softmax(dim=-1)
)
def forward(self, input):
# forwards the input to the model
output = self.classifier(input).squeeze()
return output
def training_step(self, batch, batch_idx):
input, labels = batch
y_hat = self(input)
loss = nn.CrossEntropyLoss()(y_hat, labels)
# Convert the predicted class probabilities to class indices
_, predicted_class_indices = torch.max(y_hat, dim=1)
# Compute the accuracy
accuracy = (predicted_class_indices == labels).float().mean()
# log the loss and accuracy
self.log("train_loss", loss, on_step=True, on_epoch=True, prog_bar=True, logger=True)
self.log("train_accuracy", accuracy, on_step=True, on_epoch=True, prog_bar=True, logger=True)
return {"loss": loss, "acc": accuracy}
def validation_step(self, batch, batch_idx):
input, labels = batch
y_hat = self(input)
val_loss = nn.CrossEntropyLoss()(y_hat, labels)
# Convert the predicted class probabilities to class indices
_, predicted_class_indices = torch.max(y_hat, dim=1)
# Compute the accuracy
val_acc = (predicted_class_indices == labels).float().mean()
self.log("val_loss", val_loss, on_step=True, on_epoch=True, prog_bar=True, logger=True)
self.log("val_acc", val_acc, on_step=True, on_epoch=True, prog_bar=True, logger=True)
return {"val_loss": val_loss, "val_acc": val_acc}
def validation_end(self, outputs):
val_loss = torch.stack([x["val_loss"] for x in outputs]).mean()
val_acc = torch.stack([x["val_acc"] for x in outputs]).mean()
return {"val_loss": val_loss, "val_acc": val_acc}
def configure_optimizers(self):
return optim.Adam(self.parameters(), lr=1e-3)
def test_step(self, batch, batch_idx):
# get input and labels
input, labels = batch
# forward pass
y_hat = self(input)
loss = nn.CrossEntropyLoss()(y_hat, labels)
# Convert the predicted class probabilities to class indices
_, predicted_class_indices = torch.max(y_hat, dim=1)
# Compute the accuracy
test_acc = (predicted_class_indices == labels).float().mean()
# log the accuracy
self.log("test_acc", test_acc)
return {'test_acc': test_acc}
def main(args):
os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8"
os.environ["TOKENIZERS_PARALLELISM"] = "false"
# Set the seed for the random number generator
seed_everything(args.seed, workers=True)
# Load the dataset
dataset = load_dataset('papluca/language-identification')
train_texts = dataset['train']['text']
train_labels = dataset['train']['labels']
dev_texts = dataset['validation']['text']
dev_labels = dataset['validation']['labels']
test_texts = dataset['test']['text']
test_labels = dataset['test']['labels']
train_dict = {}
dev_dict = {}
test_dict = {}
languages = set([label for text, label in zip(train_texts, train_labels)])
train_dict = get_subset_dict(languages, train_texts, train_labels)
dev_dict = get_subset_dict(languages, dev_texts, dev_labels)
test_dict = get_subset_dict(languages, test_texts, test_labels)
pretrained_model = AutoModel.from_pretrained("sentence-transformers/" + args.model_name) #all-MiniLM-L12-v2 all-MiniLM-L6-v2
# freeze the pretrained model
for param in pretrained_model.parameters():
param.requires_grad = False
tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/" + args.model_name)
max_length = pretrained_model.config.max_position_embeddings
train_embeddings_dict = get_embeddings_dict(languages, train_dict, pretrained_model, tokenizer, max_length, sample_size=args.sample_size)
dev_embeddings_dict = get_embeddings_dict(languages, dev_dict, pretrained_model, tokenizer, max_length, sample_size=args.sample_size)
test_embeddings_dict = get_embeddings_dict(languages, test_dict, pretrained_model, tokenizer, max_length, sample_size=args.sample_size)
train_embeddings, train_labels = concat_embeddings(train_embeddings_dict)
dev_embeddings, dev_labels = concat_embeddings(dev_embeddings_dict)
test_embeddings, test_labels = concat_embeddings(test_embeddings_dict)
encoded_labels_train, labels_train = encode_labels(train_labels)
encoded_labels_dev, labels_dev = encode_labels(dev_labels)
encoded_labels_test, labels_test = encode_labels(test_labels)
# Create an instance of the model
lang_identifier = LanguageIdentifier()
# Create an instance of the lightning data module
data_module = LanguageIdentifierDataModule(train_embeddings, encoded_labels_train, dev_embeddings, encoded_labels_dev, test_embeddings, encoded_labels_test, batch_size=args.batch_size, num_workers=args.num_workers)
checkpoint_callback = ModelCheckpoint(
dirpath='checkpoints',
save_top_k=1,
verbose=True,
monitor='val_loss',
mode='min'
)
# Create Logger => Use Weights and biases
wandb_logger = WandbLogger(project="Language Identifier", name="multilingual_language_identifier")
# Train the model using Pytorch Lightning Trainer
trainer = Trainer(deterministic=True, enable_checkpointing=True, default_root_dir="checkpoints", max_epochs=args.epochs, callbacks=[checkpoint_callback], log_every_n_steps=32, accelerator='gpu', precision=16, devices=1, logger=wandb_logger)
trainer.fit(lang_identifier, data_module)
# Test with the best model
results = trainer.test(ckpt_path="best", datamodule=data_module)
model_save_path = 'outputs/LID-'+ args.model_name + '-' + datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
if not os.path.exists(model_save_path):
os.makedirs(model_save_path)
# Save the model
torch.save(lang_identifier.state_dict(), os.path.join(model_save_path, 'model.pt'))
plot_embeddings(train_embeddings, labels_train, model_save_path)
if __name__ == "__main__":
args = parse_args()
main(args)