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|---|---|---|---|---|---|---|---|---|
chap15-12 | chap15-12 | 15
Implementing Encoder-decoder Methods
In this chapter we implement a machine translation application as an example of an encoder-decoder task.
In particular, we build on pre-trained encoder-decoder transformer models, which exist in the Hugging Face library for a wide variety of language pairs.
We first show how ... | 17,463 | 17,555 | #!/usr/bin/env python
# coding: utf-8
# # Machine Translation from Ro to En
# # Using the T5 Transformer with Fine-tuning
# Some initialization:
# In[1]:
import torch
import numpy as np
from transformers import set_seed
# random seed
seed = 42
# set random seed
if seed is not None:
print(f'random seed: {seed... | 4,835 | 4,954 | 12 |
chap15-13 | chap15-13 | 15
Implementing Encoder-decoder Methods
In this chapter we implement a machine translation application as an example of an encoder-decoder task.
In particular, we build on pre-trained encoder-decoder transformer models, which exist in the Hugging Face library for a wide variety of language pairs.
We first show how ... | 3,699 | 3,905 | #!/usr/bin/env python
# coding: utf-8
# # Machine Translation from English (En) to Romanian (Ro)
# # Using the T5 Transformer without Fine-tuning
# Some initialization:
# In[1]:
import torch
import numpy as np
from transformers import set_seed
# set to True to use the gpu (if there is one available)
use_gpu = Tr... | 1,522 | 1,683 | 13 |
chap15-14 | chap15-14 | 15
Implementing Encoder-decoder Methods
In this chapter we implement a machine translation application as an example of an encoder-decoder task.
In particular, we build on pre-trained encoder-decoder transformer models, which exist in the Hugging Face library for a wide variety of language pairs.
We first show how ... | 1,681 | 1,846 | #!/usr/bin/env python
# coding: utf-8
# # Machine Translation from English (En) to Romanian (Ro)
# # Using the T5 Transformer without Fine-tuning
# Some initialization:
# In[1]:
import torch
import numpy as np
from transformers import set_seed
# set to True to use the gpu (if there is one available)
use_gpu = Tr... | 615 | 653 | 14 |
chap15-15 | chap15-15 | 15
Implementing Encoder-decoder Methods
In this chapter we implement a machine translation application as an example of an encoder-decoder task.
In particular, we build on pre-trained encoder-decoder transformer models, which exist in the Hugging Face library for a wide variety of language pairs.
We first show how ... | 3,961 | 4,005 | #!/usr/bin/env python
# coding: utf-8
# # Machine Translation from English (En) to Romanian (Ro)
# # Using the T5 Transformer without Fine-tuning
# Some initialization:
# In[1]:
import torch
import numpy as np
from transformers import set_seed
# set to True to use the gpu (if there is one available)
use_gpu = Tr... | 1,862 | 2,032 | 15 |
chap15-16 | chap15-16 | 15
Implementing Encoder-decoder Methods
In this chapter we implement a machine translation application as an example of an encoder-decoder task.
In particular, we build on pre-trained encoder-decoder transformer models, which exist in the Hugging Face library for a wide variety of language pairs.
We first show how ... | 9,894 | 10,124 | #!/usr/bin/env python
# coding: utf-8
# # Machine Translation from English (En) to Romanian (Ro)
# # Using the T5 Transformer without Fine-tuning
# Some initialization:
# In[1]:
import torch
import numpy as np
from transformers import set_seed
# set to True to use the gpu (if there is one available)
use_gpu = Tr... | 3,516 | 3,555 | 16 |
chap15-17 | chap15-17 | 15
Implementing Encoder-decoder Methods
In this chapter we implement a machine translation application as an example of an encoder-decoder task.
In particular, we build on pre-trained encoder-decoder transformer models, which exist in the Hugging Face library for a wide variety of language pairs.
We first show how ... | 15,616 | 15,671 | #!/usr/bin/env python
# coding: utf-8
# # Machine Translation from Ro to En
# # Using the T5 Transformer with Fine-tuning
# Some initialization:
# In[1]:
import torch
import numpy as np
from transformers import set_seed
# random seed
seed = 42
# set random seed
if seed is not None:
print(f'random seed: {seed... | 2,611 | 2,644 | 17 |
chap15-18 | chap15-18 | 15
Implementing Encoder-decoder Methods
In this chapter we implement a machine translation application as an example of an encoder-decoder task.
In particular, we build on pre-trained encoder-decoder transformer models, which exist in the Hugging Face library for a wide variety of language pairs.
We first show how ... | 3,467 | 3,572 | #!/usr/bin/env python
# coding: utf-8
# # Machine Translation from English (En) to Romanian (Ro)
# # Using the T5 Transformer without Fine-tuning
# Some initialization:
# In[1]:
import torch
import numpy as np
from transformers import set_seed
# set to True to use the gpu (if there is one available)
use_gpu = Tr... | 1,307 | 1,329 | 18 |
chap15-19 | chap15-19 | 15
Implementing Encoder-decoder Methods
In this chapter we implement a machine translation application as an example of an encoder-decoder task.
In particular, we build on pre-trained encoder-decoder transformer models, which exist in the Hugging Face library for a wide variety of language pairs.
We first show how ... | 12,250 | 12,308 | #!/usr/bin/env python
# coding: utf-8
# # Machine Translation from Ro to En
# # Using the T5 Transformer with Fine-tuning
# Some initialization:
# In[1]:
import torch
import numpy as np
from transformers import set_seed
# random seed
seed = 42
# set random seed
if seed is not None:
print(f'random seed: {seed... | 356 | 386 | 19 |
chap15-20 | chap15-20 | 15
Implementing Encoder-decoder Methods
In this chapter we implement a machine translation application as an example of an encoder-decoder task.
In particular, we build on pre-trained encoder-decoder transformer models, which exist in the Hugging Face library for a wide variety of language pairs.
We first show how ... | 2,012 | 2,078 | #!/usr/bin/env python
# coding: utf-8
# # Machine Translation from English (En) to Romanian (Ro)
# # Using the T5 Transformer without Fine-tuning
# Some initialization:
# In[1]:
import torch
import numpy as np
from transformers import set_seed
# set to True to use the gpu (if there is one available)
use_gpu = Tr... | 678 | 702 | 20 |
chap15-21 | chap15-21 | 15
Implementing Encoder-decoder Methods
In this chapter we implement a machine translation application as an example of an encoder-decoder task.
In particular, we build on pre-trained encoder-decoder transformer models, which exist in the Hugging Face library for a wide variety of language pairs.
We first show how ... | 2,636 | 2,738 | #!/usr/bin/env python
# coding: utf-8
# # Machine Translation from English (En) to Romanian (Ro)
# # Using the T5 Transformer without Fine-tuning
# Some initialization:
# In[1]:
import torch
import numpy as np
from transformers import set_seed
# set to True to use the gpu (if there is one available)
use_gpu = Tr... | 898 | 1,047 | 21 |
chap15-22 | chap15-22 | 15
Implementing Encoder-decoder Methods
In this chapter we implement a machine translation application as an example of an encoder-decoder task.
In particular, we build on pre-trained encoder-decoder transformer models, which exist in the Hugging Face library for a wide variety of language pairs.
We first show how ... | 10,619 | 10,743 | #!/usr/bin/env python
# coding: utf-8
# # Machine Translation from English (En) to Romanian (Ro)
# # Using the T5 Transformer without Fine-tuning
# Some initialization:
# In[1]:
import torch
import numpy as np
from transformers import set_seed
# set to True to use the gpu (if there is one available)
use_gpu = Tr... | 3,826 | 3,882 | 22 |
chap15-23 | chap15-23 | 15
Implementing Encoder-decoder Methods
In this chapter we implement a machine translation application as an example of an encoder-decoder task.
In particular, we build on pre-trained encoder-decoder transformer models, which exist in the Hugging Face library for a wide variety of language pairs.
We first show how ... | 12,570 | 12,907 | #!/usr/bin/env python
# coding: utf-8
# # Machine Translation from Ro to En
# # Using the T5 Transformer with Fine-tuning
# Some initialization:
# In[1]:
import torch
import numpy as np
from transformers import set_seed
# random seed
seed = 42
# set random seed
if seed is not None:
print(f'random seed: {seed... | 1,303 | 1,324 | 23 |
chap15-24 | chap15-24 | 15
Implementing Encoder-decoder Methods
In this chapter we implement a machine translation application as an example of an encoder-decoder task.
In particular, we build on pre-trained encoder-decoder transformer models, which exist in the Hugging Face library for a wide variety of language pairs.
We first show how ... | 1,556 | 1,680 | #!/usr/bin/env python
# coding: utf-8
# # Machine Translation from English (En) to Romanian (Ro)
# # Using the T5 Transformer without Fine-tuning
# Some initialization:
# In[1]:
import torch
import numpy as np
from transformers import set_seed
# set to True to use the gpu (if there is one available)
use_gpu = Tr... | 585 | 615 | 24 |
chap15-25 | chap15-25 | 15
Implementing Encoder-decoder Methods
In this chapter we implement a machine translation application as an example of an encoder-decoder task.
In particular, we build on pre-trained encoder-decoder transformer models, which exist in the Hugging Face library for a wide variety of language pairs.
We first show how ... | 3,573 | 3,698 | #!/usr/bin/env python
# coding: utf-8
# # Machine Translation from English (En) to Romanian (Ro)
# # Using the T5 Transformer without Fine-tuning
# Some initialization:
# In[1]:
import torch
import numpy as np
from transformers import set_seed
# set to True to use the gpu (if there is one available)
use_gpu = Tr... | 1,388 | 1,495 | 25 |
chap15-26 | chap15-26 | 15
Implementing Encoder-decoder Methods
In this chapter we implement a machine translation application as an example of an encoder-decoder task.
In particular, we build on pre-trained encoder-decoder transformer models, which exist in the Hugging Face library for a wide variety of language pairs.
We first show how ... | 8,651 | 8,954 | #!/usr/bin/env python
# coding: utf-8
# # Machine Translation from English (En) to Romanian (Ro)
# # Using the T5 Transformer without Fine-tuning
# Some initialization:
# In[1]:
import torch
import numpy as np
from transformers import set_seed
# set to True to use the gpu (if there is one available)
use_gpu = Tr... | 2,970 | 3,000 | 26 |
chap07-0 | chap07-0 | 7
Implementing Text Classification with Feed Forward Networks
In this chapter we provide an implementation of the multilayer neural network described in Chapter 5, along with several of the best practices discussed in Chapter 6.
Remaining fairly simple, our network will consist of three neuron layers that are ful... | 5,455 | 5,540 | #!/usr/bin/env python
# coding: utf-8
# # Text Classification with a Feed-forward Neural Network and BOW features
# First, we will do some initialization.
# In[1]:
import random
import torch
import numpy as np
import pandas as pd
from tqdm.notebook import tqdm
# enable tqdm in pandas
tqdm.pandas()
# set to True ... | 4,336 | 4,360 | 0 |
chap07-1 | chap07-1 | 7
Implementing Text Classification with Feed Forward Networks
In this chapter we provide an implementation of the multilayer neural network described in Chapter 5, along with several of the best practices discussed in Chapter 6.
Remaining fairly simple, our network will consist of three neuron layers that are ful... | 2,587 | 2,730 | #!/usr/bin/env python
# coding: utf-8
# # Text Classification with a Feed-forward Neural Network and BOW features
# First, we will do some initialization.
# In[1]:
import random
import torch
import numpy as np
import pandas as pd
from tqdm.notebook import tqdm
# enable tqdm in pandas
tqdm.pandas()
# set to True ... | 2,796 | 2,858 | 1 |
chap07-2 | chap07-2 | 7
Implementing Text Classification with Feed Forward Networks
In this chapter we provide an implementation of the multilayer neural network described in Chapter 5, along with several of the best practices discussed in Chapter 6.
Remaining fairly simple, our network will consist of three neuron layers that are ful... | 8,099 | 8,269 | #!/usr/bin/env python
# coding: utf-8
# # Text Classification with a Feed-forward Neural Network and BOW features
# First, we will do some initialization.
# In[1]:
import random
import torch
import numpy as np
import pandas as pd
from tqdm.notebook import tqdm
# enable tqdm in pandas
tqdm.pandas()
# set to True ... | 5,114 | 5,307 | 2 |
chap07-3 | chap07-3 | 7
Implementing Text Classification with Feed Forward Networks
In this chapter we provide an implementation of the multilayer neural network described in Chapter 5, along with several of the best practices discussed in Chapter 6.
Remaining fairly simple, our network will consist of three neuron layers that are ful... | 8,456 | 8,512 | #!/usr/bin/env python
# coding: utf-8
# # Text Classification with a Feed-forward Neural Network and BOW features
# First, we will do some initialization.
# In[1]:
import random
import torch
import numpy as np
import pandas as pd
from tqdm.notebook import tqdm
# enable tqdm in pandas
tqdm.pandas()
# set to True ... | 5,388 | 5,473 | 3 |
chap07-4 | chap07-4 | 7
Implementing Text Classification with Feed Forward Networks
In this chapter we provide an implementation of the multilayer neural network described in Chapter 5, along with several of the best practices discussed in Chapter 6.
Remaining fairly simple, our network will consist of three neuron layers that are ful... | 3,870 | 4,292 | #!/usr/bin/env python
# coding: utf-8
# # Text Classification with a Feed-forward Neural Network and BOW features
# First, we will do some initialization.
# In[1]:
import random
import torch
import numpy as np
import pandas as pd
from tqdm.notebook import tqdm
# enable tqdm in pandas
tqdm.pandas()
# set to True ... | 3,891 | 3,917 | 4 |
chap07-5 | chap07-5 | 7
Implementing Text Classification with Feed Forward Networks
In this chapter we provide an implementation of the multilayer neural network described in Chapter 5, along with several of the best practices discussed in Chapter 6.
Remaining fairly simple, our network will consist of three neuron layers that are ful... | 6,732 | 6,884 | #!/usr/bin/env python
# coding: utf-8
# # Text Classification with a Feed-forward Neural Network and BOW features
# First, we will do some initialization.
# In[1]:
import random
import torch
import numpy as np
import pandas as pd
from tqdm.notebook import tqdm
# enable tqdm in pandas
tqdm.pandas()
# set to True ... | 4,455 | 4,684 | 5 |
chap07-6 | chap07-6 | 7
Implementing Text Classification with Feed Forward Networks
In this chapter we provide an implementation of the multilayer neural network described in Chapter 5, along with several of the best practices discussed in Chapter 6.
Remaining fairly simple, our network will consist of three neuron layers that are ful... | 235 | 559 | #!/usr/bin/env python
# coding: utf-8
# # Text Classification with a Feed-forward Neural Network and BOW features
# First, we will do some initialization.
# In[1]:
import random
import torch
import numpy as np
import pandas as pd
from tqdm.notebook import tqdm
# enable tqdm in pandas
tqdm.pandas()
# set to True ... | 4,336 | 4,360 | 6 |
chap07-7 | chap07-7 | 7
Implementing Text Classification with Feed Forward Networks
In this chapter we provide an implementation of the multilayer neural network described in Chapter 5, along with several of the best practices discussed in Chapter 6.
Remaining fairly simple, our network will consist of three neuron layers that are ful... | 4,522 | 4,655 | #!/usr/bin/env python
# coding: utf-8
# # Text Classification with a Feed-forward Neural Network and BOW features
# First, we will do some initialization.
# In[1]:
import random
import torch
import numpy as np
import pandas as pd
from tqdm.notebook import tqdm
# enable tqdm in pandas
tqdm.pandas()
# set to True ... | 3,994 | 4,017 | 7 |
chap07-8 | chap07-8 | 7
Implementing Text Classification with Feed Forward Networks
In this chapter we provide an implementation of the multilayer neural network described in Chapter 5, along with several of the best practices discussed in Chapter 6.
Remaining fairly simple, our network will consist of three neuron layers that are ful... | 7,619 | 7,697 | #!/usr/bin/env python
# coding: utf-8
# # Text Classification with a Feed-forward Neural Network and BOW features
# First, we will do some initialization.
# In[1]:
import random
import torch
import numpy as np
import pandas as pd
from tqdm.notebook import tqdm
# enable tqdm in pandas
tqdm.pandas()
# set to True ... | 4,693 | 4,749 | 8 |
chap07-9 | chap07-9 | 7
Implementing Text Classification with Feed Forward Networks
In this chapter we provide an implementation of the multilayer neural network described in Chapter 5, along with several of the best practices discussed in Chapter 6.
Remaining fairly simple, our network will consist of three neuron layers that are ful... | 4,656 | 4,718 | #!/usr/bin/env python
# coding: utf-8
# # Text Classification with a Feed-forward Neural Network and BOW features
# First, we will do some initialization.
# In[1]:
import random
import torch
import numpy as np
import pandas as pd
from tqdm.notebook import tqdm
# enable tqdm in pandas
tqdm.pandas()
# set to True ... | 4,049 | 4,083 | 9 |
chap11-0 | chap11-0 | 11
Implementing Part-of-speech Tagging Using Recurrent Neural Networks
The previous chapter was our first exposure to recurrent neural networks, which included intuitions for why they are useful for natural language processing, various architectures, and training algorithms.
In this chapter we will put them to use,... | 8,135 | 8,185 | #!/usr/bin/env python
# coding: utf-8
# # Part-of-speech Tagging Using RNNs
# Some initialization:
# In[4]:
import random
import torch
import numpy as np
import pandas as pd
from tqdm.notebook import tqdm
# enable tqdm in pandas
tqdm.pandas()
# set to True to use the gpu (if there is one available)
use_gpu = Tru... | 4,599 | 4,841 | 0 |
chap11-1 | chap11-1 | 11
Implementing Part-of-speech Tagging Using Recurrent Neural Networks
The previous chapter was our first exposure to recurrent neural networks, which included intuitions for why they are useful for natural language processing, various architectures, and training algorithms.
In this chapter we will put them to use,... | 11,882 | 12,078 | #!/usr/bin/env python
# coding: utf-8
# # Part-of-speech Tagging Using RNNs
# Some initialization:
# In[4]:
import random
import torch
import numpy as np
import pandas as pd
from tqdm.notebook import tqdm
# enable tqdm in pandas
tqdm.pandas()
# set to True to use the gpu (if there is one available)
use_gpu = Tru... | 7,089 | 7,130 | 1 |
chap11-2 | chap11-2 | 11
Implementing Part-of-speech Tagging Using Recurrent Neural Networks
The previous chapter was our first exposure to recurrent neural networks, which included intuitions for why they are useful for natural language processing, various architectures, and training algorithms.
In this chapter we will put them to use,... | 11,504 | 11,579 | #!/usr/bin/env python
# coding: utf-8
# # Part-of-speech Tagging Using RNNs
# Some initialization:
# In[4]:
import random
import torch
import numpy as np
import pandas as pd
from tqdm.notebook import tqdm
# enable tqdm in pandas
tqdm.pandas()
# set to True to use the gpu (if there is one available)
use_gpu = Tru... | 6,663 | 6,693 | 2 |
chap11-3 | chap11-3 | 11
Implementing Part-of-speech Tagging Using Recurrent Neural Networks
The previous chapter was our first exposure to recurrent neural networks, which included intuitions for why they are useful for natural language processing, various architectures, and training algorithms.
In this chapter we will put them to use,... | 5,385 | 5,699 | #!/usr/bin/env python
# coding: utf-8
# # Part-of-speech Tagging Using RNNs
# Some initialization:
# In[4]:
import random
import torch
import numpy as np
import pandas as pd
from tqdm.notebook import tqdm
# enable tqdm in pandas
tqdm.pandas()
# set to True to use the gpu (if there is one available)
use_gpu = Tru... | 2,482 | 2,663 | 3 |
chap11-4 | chap11-4 | 11
Implementing Part-of-speech Tagging Using Recurrent Neural Networks
The previous chapter was our first exposure to recurrent neural networks, which included intuitions for why they are useful for natural language processing, various architectures, and training algorithms.
In this chapter we will put them to use,... | 7,998 | 8,045 | #!/usr/bin/env python
# coding: utf-8
# # Part-of-speech Tagging Using RNNs
# Some initialization:
# In[4]:
import random
import torch
import numpy as np
import pandas as pd
from tqdm.notebook import tqdm
# enable tqdm in pandas
tqdm.pandas()
# set to True to use the gpu (if there is one available)
use_gpu = Tru... | 4,204 | 4,230 | 4 |
chap11-5 | chap11-5 | 11
Implementing Part-of-speech Tagging Using Recurrent Neural Networks
The previous chapter was our first exposure to recurrent neural networks, which included intuitions for why they are useful for natural language processing, various architectures, and training algorithms.
In this chapter we will put them to use,... | 4,229 | 4,744 | #!/usr/bin/env python
# coding: utf-8
# # Part-of-speech Tagging Using RNNs
# Some initialization:
# In[4]:
import random
import torch
import numpy as np
import pandas as pd
from tqdm.notebook import tqdm
# enable tqdm in pandas
tqdm.pandas()
# set to True to use the gpu (if there is one available)
use_gpu = Tru... | 1,578 | 1,655 | 5 |
chap11-6 | chap11-6 | 11
Implementing Part-of-speech Tagging Using Recurrent Neural Networks
The previous chapter was our first exposure to recurrent neural networks, which included intuitions for why they are useful for natural language processing, various architectures, and training algorithms.
In this chapter we will put them to use,... | 9,958 | 10,009 | #!/usr/bin/env python
# coding: utf-8
# # Part-of-speech Tagging Using RNNs
# Some initialization:
# In[4]:
import random
import torch
import numpy as np
import pandas as pd
from tqdm.notebook import tqdm
# enable tqdm in pandas
tqdm.pandas()
# set to True to use the gpu (if there is one available)
use_gpu = Tru... | 5,517 | 5,555 | 6 |
chap11-7 | chap11-7 | 11
Implementing Part-of-speech Tagging Using Recurrent Neural Networks
The previous chapter was our first exposure to recurrent neural networks, which included intuitions for why they are useful for natural language processing, various architectures, and training algorithms.
In this chapter we will put them to use,... | 8,461 | 8,594 | #!/usr/bin/env python
# coding: utf-8
# # Part-of-speech Tagging Using RNNs
# Some initialization:
# In[4]:
import random
import torch
import numpy as np
import pandas as pd
from tqdm.notebook import tqdm
# enable tqdm in pandas
tqdm.pandas()
# set to True to use the gpu (if there is one available)
use_gpu = Tru... | 4,933 | 5,083 | 7 |
chap11-8 | chap11-8 | 11
Implementing Part-of-speech Tagging Using Recurrent Neural Networks
The previous chapter was our first exposure to recurrent neural networks, which included intuitions for why they are useful for natural language processing, various architectures, and training algorithms.
In this chapter we will put them to use,... | 6,775 | 6,968 | #!/usr/bin/env python
# coding: utf-8
# # Part-of-speech Tagging Using RNNs
# Some initialization:
# In[4]:
import random
import torch
import numpy as np
import pandas as pd
from tqdm.notebook import tqdm
# enable tqdm in pandas
tqdm.pandas()
# set to True to use the gpu (if there is one available)
use_gpu = Tru... | 3,702 | 3,725 | 8 |
chap11-9 | chap11-9 | 11
Implementing Part-of-speech Tagging Using Recurrent Neural Networks
The previous chapter was our first exposure to recurrent neural networks, which included intuitions for why they are useful for natural language processing, various architectures, and training algorithms.
In this chapter we will put them to use,... | 3,599 | 3,769 | #!/usr/bin/env python
# coding: utf-8
# # Part-of-speech Tagging Using RNNs
# Some initialization:
# In[4]:
import random
import torch
import numpy as np
import pandas as pd
from tqdm.notebook import tqdm
# enable tqdm in pandas
tqdm.pandas()
# set to True to use the gpu (if there is one available)
use_gpu = Tru... | 1,332 | 1,396 | 9 |
chap11-10 | chap11-10 | 11
Implementing Part-of-speech Tagging Using Recurrent Neural Networks
The previous chapter was our first exposure to recurrent neural networks, which included intuitions for why they are useful for natural language processing, various architectures, and training algorithms.
In this chapter we will put them to use,... | 8,073 | 8,129 | #!/usr/bin/env python
# coding: utf-8
# # Part-of-speech Tagging Using RNNs
# Some initialization:
# In[4]:
import random
import torch
import numpy as np
import pandas as pd
from tqdm.notebook import tqdm
# enable tqdm in pandas
tqdm.pandas()
# set to True to use the gpu (if there is one available)
use_gpu = Tru... | 4,505 | 4,579 | 10 |
chap11-11 | chap11-11 | 11
Implementing Part-of-speech Tagging Using Recurrent Neural Networks
The previous chapter was our first exposure to recurrent neural networks, which included intuitions for why they are useful for natural language processing, various architectures, and training algorithms.
In this chapter we will put them to use,... | 8,720 | 8,825 | #!/usr/bin/env python
# coding: utf-8
# # Part-of-speech Tagging Using RNNs
# Some initialization:
# In[4]:
import random
import torch
import numpy as np
import pandas as pd
from tqdm.notebook import tqdm
# enable tqdm in pandas
tqdm.pandas()
# set to True to use the gpu (if there is one available)
use_gpu = Tru... | 5,161 | 5,203 | 11 |
chap11-12 | chap11-12 | 11
Implementing Part-of-speech Tagging Using Recurrent Neural Networks
The previous chapter was our first exposure to recurrent neural networks, which included intuitions for why they are useful for natural language processing, various architectures, and training algorithms.
In this chapter we will put them to use,... | 12,827 | 12,897 | #!/usr/bin/env python
# coding: utf-8
# # Part-of-speech Tagging Using RNNs
# Some initialization:
# In[4]:
import random
import torch
import numpy as np
import pandas as pd
from tqdm.notebook import tqdm
# enable tqdm in pandas
tqdm.pandas()
# set to True to use the gpu (if there is one available)
use_gpu = Tru... | 7,781 | 8,577 | 12 |
chap11-13 | chap11-13 | 11
Implementing Part-of-speech Tagging Using Recurrent Neural Networks
The previous chapter was our first exposure to recurrent neural networks, which included intuitions for why they are useful for natural language processing, various architectures, and training algorithms.
In this chapter we will put them to use,... | 5,804 | 5,961 | #!/usr/bin/env python
# coding: utf-8
# # Part-of-speech Tagging Using RNNs
# Some initialization:
# In[4]:
import random
import torch
import numpy as np
import pandas as pd
from tqdm.notebook import tqdm
# enable tqdm in pandas
tqdm.pandas()
# set to True to use the gpu (if there is one available)
use_gpu = Tru... | 3,247 | 3,273 | 13 |
chap11-14 | chap11-14 | 11
Implementing Part-of-speech Tagging Using Recurrent Neural Networks
The previous chapter was our first exposure to recurrent neural networks, which included intuitions for why they are useful for natural language processing, various architectures, and training algorithms.
In this chapter we will put them to use,... | 9,414 | 9,468 | #!/usr/bin/env python
# coding: utf-8
# # Part-of-speech Tagging Using RNNs
# Some initialization:
# In[4]:
import random
import torch
import numpy as np
import pandas as pd
from tqdm.notebook import tqdm
# enable tqdm in pandas
tqdm.pandas()
# set to True to use the gpu (if there is one available)
use_gpu = Tru... | 5,370 | 5,408 | 14 |
chap11-15 | chap11-15 | 11
Implementing Part-of-speech Tagging Using Recurrent Neural Networks
The previous chapter was our first exposure to recurrent neural networks, which included intuitions for why they are useful for natural language processing, various architectures, and training algorithms.
In this chapter we will put them to use,... | 4,747 | 4,966 | #!/usr/bin/env python
# coding: utf-8
# # Part-of-speech Tagging Using RNNs
# Some initialization:
# In[4]:
import random
import torch
import numpy as np
import pandas as pd
from tqdm.notebook import tqdm
# enable tqdm in pandas
tqdm.pandas()
# set to True to use the gpu (if there is one available)
use_gpu = Tru... | 1,804 | 1,827 | 15 |
chap11-16 | chap11-16 | 11
Implementing Part-of-speech Tagging Using Recurrent Neural Networks
The previous chapter was our first exposure to recurrent neural networks, which included intuitions for why they are useful for natural language processing, various architectures, and training algorithms.
In this chapter we will put them to use,... | 5,171 | 5,281 | #!/usr/bin/env python
# coding: utf-8
# # Part-of-speech Tagging Using RNNs
# Some initialization:
# In[4]:
import random
import torch
import numpy as np
import pandas as pd
from tqdm.notebook import tqdm
# enable tqdm in pandas
tqdm.pandas()
# set to True to use the gpu (if there is one available)
use_gpu = Tru... | 2,391 | 2,459 | 16 |
chap11-17 | chap11-17 | 11
Implementing Part-of-speech Tagging Using Recurrent Neural Networks
The previous chapter was our first exposure to recurrent neural networks, which included intuitions for why they are useful for natural language processing, various architectures, and training algorithms.
In this chapter we will put them to use,... | 8,878 | 8,945 | #!/usr/bin/env python
# coding: utf-8
# # Part-of-speech Tagging Using RNNs
# Some initialization:
# In[4]:
import random
import torch
import numpy as np
import pandas as pd
from tqdm.notebook import tqdm
# enable tqdm in pandas
tqdm.pandas()
# set to True to use the gpu (if there is one available)
use_gpu = Tru... | 5,273 | 5,370 | 17 |
chap11-18 | chap11-18 | 11
Implementing Part-of-speech Tagging Using Recurrent Neural Networks
The previous chapter was our first exposure to recurrent neural networks, which included intuitions for why they are useful for natural language processing, various architectures, and training algorithms.
In this chapter we will put them to use,... | 10,010 | 10,093 | #!/usr/bin/env python
# coding: utf-8
# # Part-of-speech Tagging Using RNNs
# Some initialization:
# In[4]:
import random
import torch
import numpy as np
import pandas as pd
from tqdm.notebook import tqdm
# enable tqdm in pandas
tqdm.pandas()
# set to True to use the gpu (if there is one available)
use_gpu = Tru... | 5,555 | 5,596 | 18 |
chap11-19 | chap11-19 | 11
Implementing Part-of-speech Tagging Using Recurrent Neural Networks
The previous chapter was our first exposure to recurrent neural networks, which included intuitions for why they are useful for natural language processing, various architectures, and training algorithms.
In this chapter we will put them to use,... | 2,093 | 2,369 | #!/usr/bin/env python
# coding: utf-8
# # Part-of-speech Tagging Using RNNs
# Some initialization:
# In[4]:
import random
import torch
import numpy as np
import pandas as pd
from tqdm.notebook import tqdm
# enable tqdm in pandas
tqdm.pandas()
# set to True to use the gpu (if there is one available)
use_gpu = Tru... | 747 | 772 | 19 |
chap11-20 | chap11-20 | 11
Implementing Part-of-speech Tagging Using Recurrent Neural Networks
The previous chapter was our first exposure to recurrent neural networks, which included intuitions for why they are useful for natural language processing, various architectures, and training algorithms.
In this chapter we will put them to use,... | 6,074 | 6,345 | #!/usr/bin/env python
# coding: utf-8
# # Part-of-speech Tagging Using RNNs
# Some initialization:
# In[4]:
import random
import torch
import numpy as np
import pandas as pd
from tqdm.notebook import tqdm
# enable tqdm in pandas
tqdm.pandas()
# set to True to use the gpu (if there is one available)
use_gpu = Tru... | 3,702 | 3,725 | 20 |
chap11-21 | chap11-21 | 11
Implementing Part-of-speech Tagging Using Recurrent Neural Networks
The previous chapter was our first exposure to recurrent neural networks, which included intuitions for why they are useful for natural language processing, various architectures, and training algorithms.
In this chapter we will put them to use,... | 8,597 | 8,719 | #!/usr/bin/env python
# coding: utf-8
# # Part-of-speech Tagging Using RNNs
# Some initialization:
# In[4]:
import random
import torch
import numpy as np
import pandas as pd
from tqdm.notebook import tqdm
# enable tqdm in pandas
tqdm.pandas()
# set to True to use the gpu (if there is one available)
use_gpu = Tru... | 5,092 | 5,136 | 21 |
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