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This colab is a demonstration of using Tensorflow Hub for text classification in non-English/local languages. Here we choose Bangla as the local language and use pretrained word embeddings to solve a multiclass classification task where we classify Bangla news articles in 5 categories. The pretrained embeddings for Bangla comes from fastText which is a library by Facebook with released pretrained word vectors for 157 languages.
We'll use TF-Hub's pretrained embedding exporter for converting the word embeddings to a text embedding module first and then use the module to train a classifier with tf.keras, Tensorflow's high level user friendly API to build deep learning models. Even if we are using fastText embeddings here, it's possible to export any other embeddings pretrained from other tasks and quickly get results with Tensorflow hub.
Setup
%%bash
# https://github.com/pypa/setuptools/issues/1694#issuecomment-466010982
pip install -q gdown --no-use-pep517
%%bash
sudo apt-get install -y unzip
Reading package lists... Building dependency tree... Reading state information... Suggested packages: zip The following NEW packages will be installed: unzip 0 upgraded, 1 newly installed, 0 to remove and 107 not upgraded. Need to get 167 kB of archives. After this operation, 558 kB of additional disk space will be used. Get:1 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic/main amd64 unzip amd64 6.0-21ubuntu1 [167 kB] Fetched 167 kB in 0s (603 kB/s) Selecting previously unselected package unzip. (Reading database ... 194321 files and directories currently installed.) Preparing to unpack .../unzip_6.0-21ubuntu1_amd64.deb ... Unpacking unzip (6.0-21ubuntu1) ... Setting up unzip (6.0-21ubuntu1) ... Processing triggers for mime-support (3.60ubuntu1) ... Processing triggers for man-db (2.8.3-2ubuntu0.1) ... dpkg-preconfigure: unable to re-open stdin: No such file or directory
import os
import tensorflow as tf
import tensorflow_hub as hub
import gdown
import numpy as np
from sklearn.metrics import classification_report
import matplotlib.pyplot as plt
import seaborn as sns
Dataset
We will use BARD (Bangla Article Dataset) which has around 3,76,226 articles collected from different Bangla news portals and labelled with 5 categories : economy, state, international, sports and entertainment. We download the file from Google Drive this (bit.ly/BARD_DATASET) link is referring to from this GitHub repository.
gdown.download(
url='https://drive.google.com/uc?id=1Ag0jd21oRwJhVFIBohmX_ogeojVtapLy',
output='bard.zip',
quiet=True
)
'bard.zip'
%%bash
unzip -qo bard.zip
Export pretrained word vectors to TF-Hub module
TF-Hub provides some handy scripts for converting word embeddings to TF-hub text embedding modules here. To make the module for Bangla or any other languages we simply have to download the word embedding .txt or .vec file to the same directory as export_v2.py and run the script.
The exporter reads the embedding vectors and exports it to a Tensorflow SavedModel. A SavedModel contains a complete TensorFlow program including weights and graph. TF-Hub can load the SavedModel as a module which we will use to build the model for text classification. Since we are using tf.keras to build the model we will use hub.KerasLayer which provides a wrapper for a hub module to use as a Keras Layer.
First we will get our word embeddings from fastText and embedding exporter from TF-Hub repo.
%%bash
curl -O https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.bn.300.vec.gz
curl -O https://raw.githubusercontent.com/tensorflow/hub/master/examples/text_embeddings_v2/export_v2.py
gunzip -qf cc.bn.300.vec.gz --k
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 840M 100 840M 0 0 11.9M 0 0:01:10 0:01:10 --:--:-- 12.2M
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 7603 100 7603 0 0 15644 0 --:--:-- --:--:-- --:--:-- 15644
Then we will run the exporter script on our embedding file. Since fastText embeddings has a header line and are pretty large(around 3.3 GB for bangla after converting to a module) we ignore the first line and export only the first 100, 000 tokens to the text embedding module.
%%bash
python export_v2.py --embedding_file=cc.bn.300.vec --export_path=text_module --num_lines_to_ignore=1 --num_lines_to_use=100000
2020-05-26 17:13:27.399408: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2020-05-26 17:13:27.437679: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2020-05-26 17:13:27.438390: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:00:05.0 name: Tesla V100-SXM2-16GB computeCapability: 7.0 coreClock: 1.53GHz coreCount: 80 deviceMemorySize: 15.78GiB deviceMemoryBandwidth: 836.37GiB/s 2020-05-26 17:13:27.438658: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2020-05-26 17:13:27.440440: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2020-05-26 17:13:27.442404: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2020-05-26 17:13:27.442736: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2020-05-26 17:13:27.444379: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2020-05-26 17:13:27.445183: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2020-05-26 17:13:27.448506: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2020-05-26 17:13:27.448626: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2020-05-26 17:13:27.449346: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2020-05-26 17:13:27.449961: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2020-05-26 17:13:27.450359: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 AVX512F FMA 2020-05-26 17:13:27.457000: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 2000165000 Hz 2020-05-26 17:13:27.457497: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f0e7c000b20 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2020-05-26 17:13:27.457527: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2020-05-26 17:13:27.550655: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2020-05-26 17:13:27.551443: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0xf4ee300 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2020-05-26 17:13:27.551482: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Tesla V100-SXM2-16GB, Compute Capability 7.0 2020-05-26 17:13:27.551769: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2020-05-26 17:13:27.552454: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:00:05.0 name: Tesla V100-SXM2-16GB computeCapability: 7.0 coreClock: 1.53GHz coreCount: 80 deviceMemorySize: 15.78GiB deviceMemoryBandwidth: 836.37GiB/s 2020-05-26 17:13:27.552516: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2020-05-26 17:13:27.552541: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2020-05-26 17:13:27.552551: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2020-05-26 17:13:27.552565: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2020-05-26 17:13:27.552575: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2020-05-26 17:13:27.552584: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2020-05-26 17:13:27.552594: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2020-05-26 17:13:27.552679: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2020-05-26 17:13:27.553322: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2020-05-26 17:13:27.553916: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2020-05-26 17:13:27.553968: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2020-05-26 17:13:27.555407: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2020-05-26 17:13:27.555447: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2020-05-26 17:13:27.555454: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2020-05-26 17:13:27.555577: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2020-05-26 17:13:27.556197: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2020-05-26 17:13:27.556890: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 14893 MB memory) -> physical GPU (device: 0, name: Tesla V100-SXM2-16GB, pci bus id: 0000:00:05.0, compute capability: 7.0) WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/ops/resource_variable_ops.py:1817: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version. Instructions for updating: If using Keras pass *_constraint arguments to layers. W0526 17:13:29.088178 139705524873024 deprecation.py:506] From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/ops/resource_variable_ops.py:1817: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version. Instructions for updating: If using Keras pass *_constraint arguments to layers. INFO:tensorflow:Assets written to: text_module/assets I0526 17:13:29.537315 139705524873024 builder_impl.py:775] Assets written to: text_module/assets
module_path = "text_module"
embedding_layer = hub.KerasLayer(module_path, trainable=False)
The text embedding module takes a batch of sentences in a 1D tensor of strings as input and outputs the embedding vectors of shape (batch_size, embedding_dim) corresponding to the sentences. It preprocesses the input by splitting on spaces. Word embeddings are combined to sentence embeddings with the sqrtn combiner(See here). For demonstration we pass a list of Bangla words as input and get the corresponding embedding vectors.
embedding_layer(['বাস', 'বসবাস', 'ট্রেন', 'যাত্রী', 'ট্রাক'])
<tf.Tensor: shape=(5, 300), dtype=float64, numpy=
array([[ 0.0462, -0.0355, 0.0129, ..., 0.0025, -0.0966, 0.0216],
[-0.0631, -0.0051, 0.085 , ..., 0.0249, -0.0149, 0.0203],
[ 0.1371, -0.069 , -0.1176, ..., 0.029 , 0.0508, -0.026 ],
[ 0.0532, -0.0465, -0.0504, ..., 0.02 , -0.0023, 0.0011],
[ 0.0908, -0.0404, -0.0536, ..., -0.0275, 0.0528, 0.0253]])>
Convert to Tensorflow Dataset
Since the dataset is really large instead of loading the entire dataset in memory we will use a generator to yield samples in run-time in batches using Tensorflow Dataset functionalities. The dataset is also very imbalanced, so before using the generator we will shuffle the dataset.
dir_names = ['economy', 'sports', 'entertainment', 'state', 'international']
file_paths = []
labels = []
for i, dir in enumerate(dir_names):
file_names = ["/".join([dir, name]) for name in os.listdir(dir)]
file_paths += file_names
labels += [i] * len(os.listdir(dir))
np.random.seed(42)
permutation = np.random.permutation(len(file_paths))
file_paths = np.array(file_paths)[permutation]
labels = np.array(labels)[permutation]
We can check the distribution of labels in the training and validation examples after shuffling.
train_frac = 0.8
train_size = int(len(file_paths) * train_frac)
# plot training vs validation distribution
plt.subplot(1, 2, 1)
plt.hist(labels[0:train_size])
plt.title("Train labels")
plt.subplot(1, 2, 2)
plt.hist(labels[train_size:])
plt.title("Validation labels")
plt.tight_layout()
To create a Dataset using generator we first write a generator function which reads each of the articles from file_paths and the labels from the label array, and yields one training example at each step. We pass this generator function to the tf.data.Dataset.from_generator method and specify the output types. Each training example is a tuple containing an article of tf.string data type and one-hot encoded label. We split the dataset with a train-validation split of 80-20 using the skip and take method.
def load_file(path, label):
return tf.io.read_file(path), label
def make_datasets(train_size):
batch_size = 256
train_files = file_paths[:train_size]
train_labels = labels[:train_size]
train_ds = tf.data.Dataset.from_tensor_slices((train_files, train_labels))
train_ds = train_ds.map(load_file).shuffle(5000)
train_ds = train_ds.batch(batch_size).prefetch(tf.data.experimental.AUTOTUNE)
test_files = file_paths[train_size:]
test_labels = labels[train_size:]
test_ds = tf.data.Dataset.from_tensor_slices((test_files, test_labels))
test_ds = test_ds.map(load_file)
test_ds = test_ds.batch(batch_size).prefetch(tf.data.experimental.AUTOTUNE)
return train_ds, test_ds
train_data, validation_data = make_datasets(train_size)
Model Training and Evaluation
Since we have already added a wrapper around our module to use it as any other layer in keras we can create a small Sequential model which is a linear stack of layers. We can add our text embedding module with model.add just like any other layer. We compile the model by specifying the loss and optimizer and train it for 10 epochs. tf.keras API can handle tensorflow datasets as input, so we can pass a Dataset instance to the fit method for model training. Since we are using a generator function, tf.data will handle generating the samples, batching them and feeding them to the model.
Model
def create_model():
model = tf.keras.Sequential([
tf.keras.layers.Input(shape=[], dtype=tf.string),
embedding_layer,
tf.keras.layers.Dense(64, activation="relu"),
tf.keras.layers.Dense(16, activation="relu"),
tf.keras.layers.Dense(5),
])
model.compile(loss=tf.losses.SparseCategoricalCrossentropy(from_logits=True),
optimizer="adam", metrics=['accuracy'])
return model
model = create_model()
# Create earlystopping callback
early_stopping_callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=0, patience=3)
WARNING:tensorflow:Layer dense is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2. The layer has dtype float32 because it's dtype defaults to floatx.
If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.
To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.
Training
history = model.fit(train_data,
validation_data=validation_data,
epochs=5,
callbacks=[early_stopping_callback])
Epoch 1/5 1176/1176 [==============================] - 51s 43ms/step - loss: 0.2144 - accuracy: 0.9280 - val_loss: 0.1474 - val_accuracy: 0.9485 Epoch 2/5 1176/1176 [==============================] - 49s 42ms/step - loss: 0.1402 - accuracy: 0.9506 - val_loss: 0.1327 - val_accuracy: 0.9529 Epoch 3/5 1176/1176 [==============================] - 49s 42ms/step - loss: 0.1293 - accuracy: 0.9539 - val_loss: 0.1260 - val_accuracy: 0.9541 Epoch 4/5 1176/1176 [==============================] - 49s 42ms/step - loss: 0.1217 - accuracy: 0.9562 - val_loss: 0.1198 - val_accuracy: 0.9559 Epoch 5/5 1176/1176 [==============================] - 49s 41ms/step - loss: 0.1165 - accuracy: 0.9575 - val_loss: 0.1172 - val_accuracy: 0.9569
Evaluation
We can visualize the accuracy and loss curves for training and validation data using the history object returned by the fit method which contains the loss and accuracy value for each epoch.
# Plot training & validation accuracy values
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('Model accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show()
# Plot training & validation loss values
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show()
Prediction
We can get the predictions for the validation data and check the confusion matrix to see the model's performance for each of the 5 classes. As predict method returns us the n-d array for probabilities for each class which we convert to class labels using np.argmax.
y_pred = model.predict(validation_data)
y_pred = np.argmax(y_pred, axis=1)
samples = file_paths[0:3]
for i, sample in enumerate(samples):
f = open(sample)
text = f.read()
print(text[0:100])
print("True Class: ", sample.split("/")[0])
print("Predicted Class: ", dir_names[y_pred[i]])
f.close()
বাংলা বর্ষ ১৪২২ বিদায় এবং নববর্ষ ১৪২৩ উদ্যাপন উপলক্ষে বাংলাদেশ জাতীয় জাদুঘর তিন দিনব্যাপী লোকজ ম True Class: entertainment Predicted Class: state শেরপুরের ঝিনাইগাতী সীমান্তে ফের বন্য হাতির আক্রমণে উত্তম মারাক (৫৫) নামের এক কৃষক নিহত হয়েছেন। নিহত True Class: state Predicted Class: state আর্জেন্টিনার খেলা চলছে। চিলির মোটেল ফ্যান্টাসিয়ায় তিল ধারণের ঠাঁই নেই। সবাই অপেক্ষা করে আছেন, কখন True Class: sports Predicted Class: state
Compare Performance
Now we can take the correct labels for the validation data from labels and compare it with our predictions to get the classification_report.
y_true = np.array(labels[train_size:])
print(classification_report(y_true, y_pred, target_names=dir_names))
precision recall f1-score support
economy 0.82 0.78 0.80 3897
sports 0.99 0.99 0.99 10204
entertainment 0.92 0.93 0.93 6256
state 0.97 0.98 0.97 48512
international 0.95 0.90 0.93 6377
accuracy 0.96 75246
macro avg 0.93 0.91 0.92 75246
weighted avg 0.96 0.96 0.96 75246
We can also compare our model's performance with the published results obtained in the original paper who report a 0.96 precision .The original authors described many preprocessing steps done on the dataset like dropping punctuations and digits, removing top 25 most frequest stop words. As we can see in the classification_report, we also gain a 0.96 precision and accuracy after training only 5 epochs without any preprocessing!
In this example when we created the Keras layer from our embedding module we set trainable=False, which means the embedding weights will not be updated during training. Try setting it to True to reach 97% accuracy with this dataset with only 2 epochs.