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|---|---|---|---|---|---|---|---|---|
chap04-0 | chap04-0 | 4
Implementing Text Classification Using Perceptron and Logistic Regression
In the previous chapters we have discussed the theory behind the perceptron and logistic regression, including mathematical explanations of how and why they are able to learn from examples.
In this chapter we will transition from math to co... | 11,350 | 11,432 | #!/usr/bin/env python
# coding: utf-8
# # Binary Text Classification with Perceptron
# In[1]:
import random
import numpy as np
from tqdm.notebook import tqdm
# set this variable to a number to be used as the random seed
# or to None if you don't want to set a random seed
seed = 1234
if seed is not None:
rando... | 5,070 | 5,166 | 0 |
chap04-1 | chap04-1 | 4
Implementing Text Classification Using Perceptron and Logistic Regression
In the previous chapters we have discussed the theory behind the perceptron and logistic regression, including mathematical explanations of how and why they are able to learn from examples.
In this chapter we will transition from math to co... | 16,510 | 16,556 | #!/usr/bin/env python
# coding: utf-8
# # Binary Text Classification with
# # Logistic Regression Implemented from Scratch
# In[1]:
import random
import numpy as np
from tqdm.notebook import tqdm
# set this variable to a number to be used as the random seed
# or to None if you don't want to set a random seed
see... | 4,482 | 4,514 | 1 |
chap04-2 | chap04-2 | 4
Implementing Text Classification Using Perceptron and Logistic Regression
In the previous chapters we have discussed the theory behind the perceptron and logistic regression, including mathematical explanations of how and why they are able to learn from examples.
In this chapter we will transition from math to co... | 27,786 | 27,991 | #!/usr/bin/env python
# coding: utf-8
# # Multiclass Text Classification with
# # Logistic Regression Implemented with PyTorch and CE Loss
# 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
tqd... | 2,684 | 2,750 | 2 |
chap04-3 | chap04-3 | 4
Implementing Text Classification Using Perceptron and Logistic Regression
In the previous chapters we have discussed the theory behind the perceptron and logistic regression, including mathematical explanations of how and why they are able to learn from examples.
In this chapter we will transition from math to co... | 16,420 | 16,500 | #!/usr/bin/env python
# coding: utf-8
# # Binary Text Classification with
# # Logistic Regression Implemented from Scratch
# In[1]:
import random
import numpy as np
from tqdm.notebook import tqdm
# set this variable to a number to be used as the random seed
# or to None if you don't want to set a random seed
see... | 4,407 | 4,453 | 3 |
chap04-4 | chap04-4 | 4
Implementing Text Classification Using Perceptron and Logistic Regression
In the previous chapters we have discussed the theory behind the perceptron and logistic regression, including mathematical explanations of how and why they are able to learn from examples.
In this chapter we will transition from math to co... | 9,407 | 9,479 | #!/usr/bin/env python
# coding: utf-8
# # Binary Text Classification with Perceptron
# In[1]:
import random
import numpy as np
from tqdm.notebook import tqdm
# set this variable to a number to be used as the random seed
# or to None if you don't want to set a random seed
seed = 1234
if seed is not None:
rando... | 3,534 | 3,558 | 4 |
chap04-5 | chap04-5 | 4
Implementing Text Classification Using Perceptron and Logistic Regression
In the previous chapters we have discussed the theory behind the perceptron and logistic regression, including mathematical explanations of how and why they are able to learn from examples.
In this chapter we will transition from math to co... | 10,827 | 10,931 | #!/usr/bin/env python
# coding: utf-8
# # Binary Text Classification with Perceptron
# In[1]:
import random
import numpy as np
from tqdm.notebook import tqdm
# set this variable to a number to be used as the random seed
# or to None if you don't want to set a random seed
seed = 1234
if seed is not None:
rando... | 4,004 | 4,054 | 5 |
chap04-6 | chap04-6 | 4
Implementing Text Classification Using Perceptron and Logistic Regression
In the previous chapters we have discussed the theory behind the perceptron and logistic regression, including mathematical explanations of how and why they are able to learn from examples.
In this chapter we will transition from math to co... | 23,684 | 24,252 | #!/usr/bin/env python
# coding: utf-8
# # Multiclass Text Classification with
# # Logistic Regression Implemented with PyTorch and CE Loss
# 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
tqd... | 1,551 | 1,715 | 6 |
chap04-7 | chap04-7 | 4
Implementing Text Classification Using Perceptron and Logistic Regression
In the previous chapters we have discussed the theory behind the perceptron and logistic regression, including mathematical explanations of how and why they are able to learn from examples.
In this chapter we will transition from math to co... | 22,466 | 22,543 | #!/usr/bin/env python
# coding: utf-8
# # Multiclass Text Classification with
# # Logistic Regression Implemented with PyTorch and CE Loss
# 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
tqd... | 1,054 | 1,179 | 7 |
chap04-8 | chap04-8 | 4
Implementing Text Classification Using Perceptron and Logistic Regression
In the previous chapters we have discussed the theory behind the perceptron and logistic regression, including mathematical explanations of how and why they are able to learn from examples.
In this chapter we will transition from math to co... | 8,010 | 8,094 | #!/usr/bin/env python
# coding: utf-8
# # Binary Text Classification with Perceptron
# In[1]:
import random
import numpy as np
from tqdm.notebook import tqdm
# set this variable to a number to be used as the random seed
# or to None if you don't want to set a random seed
seed = 1234
if seed is not None:
rando... | 2,401 | 2,437 | 8 |
chap04-9 | chap04-9 | 4
Implementing Text Classification Using Perceptron and Logistic Regression
In the previous chapters we have discussed the theory behind the perceptron and logistic regression, including mathematical explanations of how and why they are able to learn from examples.
In this chapter we will transition from math to co... | 19,082 | 19,452 | #!/usr/bin/env python
# coding: utf-8
# # Binary Text Classification with
# # Logistic Regression Implemented with PyTorch and BCE Loss
# In[1]:
import random
import numpy as np
import torch
from tqdm.notebook import tqdm
# set this variable to a number to be used as the random seed
# or to None if you don't want... | 4,090 | 4,394 | 9 |
chap04-10 | chap04-10 | 4
Implementing Text Classification Using Perceptron and Logistic Regression
In the previous chapters we have discussed the theory behind the perceptron and logistic regression, including mathematical explanations of how and why they are able to learn from examples.
In this chapter we will transition from math to co... | 15,479 | 15,530 | #!/usr/bin/env python
# coding: utf-8
# # Binary Text Classification with
# # Logistic Regression Implemented from Scratch
# In[1]:
import random
import numpy as np
from tqdm.notebook import tqdm
# set this variable to a number to be used as the random seed
# or to None if you don't want to set a random seed
see... | 3,894 | 3,910 | 10 |
chap04-11 | chap04-11 | 4
Implementing Text Classification Using Perceptron and Logistic Regression
In the previous chapters we have discussed the theory behind the perceptron and logistic regression, including mathematical explanations of how and why they are able to learn from examples.
In this chapter we will transition from math to co... | 11,433 | 11,666 | #!/usr/bin/env python
# coding: utf-8
# # Binary Text Classification with Perceptron
# In[1]:
import random
import numpy as np
from tqdm.notebook import tqdm
# set this variable to a number to be used as the random seed
# or to None if you don't want to set a random seed
seed = 1234
if seed is not None:
rando... | 5,166 | 5,220 | 11 |
chap04-12 | chap04-12 | 4
Implementing Text Classification Using Perceptron and Logistic Regression
In the previous chapters we have discussed the theory behind the perceptron and logistic regression, including mathematical explanations of how and why they are able to learn from examples.
In this chapter we will transition from math to co... | 18,594 | 18,722 | #!/usr/bin/env python
# coding: utf-8
# # Binary Text Classification with
# # Logistic Regression Implemented with PyTorch and BCE Loss
# In[1]:
import random
import numpy as np
import torch
from tqdm.notebook import tqdm
# set this variable to a number to be used as the random seed
# or to None if you don't want... | 3,597 | 3,632 | 12 |
chap04-13 | chap04-13 | 4
Implementing Text Classification Using Perceptron and Logistic Regression
In the previous chapters we have discussed the theory behind the perceptron and logistic regression, including mathematical explanations of how and why they are able to learn from examples.
In this chapter we will transition from math to co... | 18,305 | 18,377 | #!/usr/bin/env python
# coding: utf-8
# # Binary Text Classification with
# # Logistic Regression Implemented with PyTorch and BCE Loss
# In[1]:
import random
import numpy as np
import torch
from tqdm.notebook import tqdm
# set this variable to a number to be used as the random seed
# or to None if you don't want... | 3,564 | 3,597 | 13 |
chap04-14 | chap04-14 | 4
Implementing Text Classification Using Perceptron and Logistic Regression
In the previous chapters we have discussed the theory behind the perceptron and logistic regression, including mathematical explanations of how and why they are able to learn from examples.
In this chapter we will transition from math to co... | 18,886 | 18,986 | #!/usr/bin/env python
# coding: utf-8
# # Binary Text Classification with
# # Logistic Regression Implemented with PyTorch and BCE Loss
# In[1]:
import random
import numpy as np
import torch
from tqdm.notebook import tqdm
# set this variable to a number to be used as the random seed
# or to None if you don't want... | 3,539 | 3,549 | 14 |
chap04-15 | chap04-15 | 4
Implementing Text Classification Using Perceptron and Logistic Regression
In the previous chapters we have discussed the theory behind the perceptron and logistic regression, including mathematical explanations of how and why they are able to learn from examples.
In this chapter we will transition from math to co... | 30,574 | 30,675 | #!/usr/bin/env python
# coding: utf-8
# # Multiclass Text Classification with
# # Logistic Regression Implemented with PyTorch and CE Loss
# 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
tqd... | 2,912 | 3,016 | 15 |
chap04-16 | chap04-16 | 4
Implementing Text Classification Using Perceptron and Logistic Regression
In the previous chapters we have discussed the theory behind the perceptron and logistic regression, including mathematical explanations of how and why they are able to learn from examples.
In this chapter we will transition from math to co... | 35,150 | 35,323 | #!/usr/bin/env python
# coding: utf-8
# # Multiclass Text Classification with
# # Logistic Regression Implemented with PyTorch and CE Loss
# 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
tqd... | 4,091 | 4,140 | 16 |
chap04-17 | chap04-17 | 4
Implementing Text Classification Using Perceptron and Logistic Regression
In the previous chapters we have discussed the theory behind the perceptron and logistic regression, including mathematical explanations of how and why they are able to learn from examples.
In this chapter we will transition from math to co... | 10,416 | 10,481 | #!/usr/bin/env python
# coding: utf-8
# # Binary Text Classification with Perceptron
# In[1]:
import random
import numpy as np
from tqdm.notebook import tqdm
# set this variable to a number to be used as the random seed
# or to None if you don't want to set a random seed
seed = 1234
if seed is not None:
rando... | 3,880 | 3,945 | 17 |
chap04-18 | chap04-18 | 4
Implementing Text Classification Using Perceptron and Logistic Regression
In the previous chapters we have discussed the theory behind the perceptron and logistic regression, including mathematical explanations of how and why they are able to learn from examples.
In this chapter we will transition from math to co... | 6,675 | 7,004 | #!/usr/bin/env python
# coding: utf-8
# # Binary Text Classification with
# # Logistic Regression Implemented with PyTorch and BCE Loss
# In[1]:
import random
import numpy as np
import torch
from tqdm.notebook import tqdm
# set this variable to a number to be used as the random seed
# or to None if you don't want... | 1,938 | 1,996 | 18 |
chap04-19 | chap04-19 | 4
Implementing Text Classification Using Perceptron and Logistic Regression
In the previous chapters we have discussed the theory behind the perceptron and logistic regression, including mathematical explanations of how and why they are able to learn from examples.
In this chapter we will transition from math to co... | 10,941 | 11,003 | #!/usr/bin/env python
# coding: utf-8
# # Binary Text Classification with Perceptron
# In[1]:
import random
import numpy as np
from tqdm.notebook import tqdm
# set this variable to a number to be used as the random seed
# or to None if you don't want to set a random seed
seed = 1234
if seed is not None:
rando... | 4,054 | 4,278 | 19 |
chap04-20 | chap04-20 | 4
Implementing Text Classification Using Perceptron and Logistic Regression
In the previous chapters we have discussed the theory behind the perceptron and logistic regression, including mathematical explanations of how and why they are able to learn from examples.
In this chapter we will transition from math to co... | 31,560 | 31,649 | #!/usr/bin/env python
# coding: utf-8
# # Multiclass Text Classification with
# # Logistic Regression Implemented with PyTorch and CE Loss
# 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
tqd... | 3,250 | 3,293 | 20 |
chap04-21 | chap04-21 | 4
Implementing Text Classification Using Perceptron and Logistic Regression
In the previous chapters we have discussed the theory behind the perceptron and logistic regression, including mathematical explanations of how and why they are able to learn from examples.
In this chapter we will transition from math to co... | 12,663 | 12,972 | #!/usr/bin/env python
# coding: utf-8
# # Binary Text Classification with Perceptron
# In[1]:
import random
import numpy as np
from tqdm.notebook import tqdm
# set this variable to a number to be used as the random seed
# or to None if you don't want to set a random seed
seed = 1234
if seed is not None:
rando... | 5,806 | 5,856 | 21 |
chap04-22 | chap04-22 | 4
Implementing Text Classification Using Perceptron and Logistic Regression
In the previous chapters we have discussed the theory behind the perceptron and logistic regression, including mathematical explanations of how and why they are able to learn from examples.
In this chapter we will transition from math to co... | 16,652 | 16,761 | #!/usr/bin/env python
# coding: utf-8
# # Binary Text Classification with
# # Logistic Regression Implemented from Scratch
# In[1]:
import random
import numpy as np
from tqdm.notebook import tqdm
# set this variable to a number to be used as the random seed
# or to None if you don't want to set a random seed
see... | 6,061 | 6,111 | 22 |
chap04-23 | chap04-23 | 4
Implementing Text Classification Using Perceptron and Logistic Regression
In the previous chapters we have discussed the theory behind the perceptron and logistic regression, including mathematical explanations of how and why they are able to learn from examples.
In this chapter we will transition from math to co... | 9,580 | 9,738 | #!/usr/bin/env python
# coding: utf-8
# # Binary Text Classification with Perceptron
# In[1]:
import random
import numpy as np
from tqdm.notebook import tqdm
# set this variable to a number to be used as the random seed
# or to None if you don't want to set a random seed
seed = 1234
if seed is not None:
rando... | 3,651 | 3,682 | 23 |
chap04-24 | chap04-24 | 4
Implementing Text Classification Using Perceptron and Logistic Regression
In the previous chapters we have discussed the theory behind the perceptron and logistic regression, including mathematical explanations of how and why they are able to learn from examples.
In this chapter we will transition from math to co... | 15,251 | 15,432 | #!/usr/bin/env python
# coding: utf-8
# # Binary Text Classification with
# # Logistic Regression Implemented from Scratch
# In[1]:
import random
import numpy as np
from tqdm.notebook import tqdm
# set this variable to a number to be used as the random seed
# or to None if you don't want to set a random seed
see... | 3,641 | 3,713 | 24 |
chap04-25 | chap04-25 | 4
Implementing Text Classification Using Perceptron and Logistic Regression
In the previous chapters we have discussed the theory behind the perceptron and logistic regression, including mathematical explanations of how and why they are able to learn from examples.
In this chapter we will transition from math to co... | 34,830 | 35,013 | #!/usr/bin/env python
# coding: utf-8
# # Multiclass Text Classification with
# # Logistic Regression Implemented with PyTorch and CE Loss
# 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
tqd... | 3,642 | 3,843 | 25 |
chap04-26 | chap04-26 | 4
Implementing Text Classification Using Perceptron and Logistic Regression
In the previous chapters we have discussed the theory behind the perceptron and logistic regression, including mathematical explanations of how and why they are able to learn from examples.
In this chapter we will transition from math to co... | 8,146 | 8,316 | #!/usr/bin/env python
# coding: utf-8
# # Binary Text Classification with
# # Logistic Regression Implemented from Scratch
# In[1]:
import random
import numpy as np
from tqdm.notebook import tqdm
# set this variable to a number to be used as the random seed
# or to None if you don't want to set a random seed
see... | 3,065 | 3,130 | 26 |
chap04-27 | chap04-27 | 4
Implementing Text Classification Using Perceptron and Logistic Regression
In the previous chapters we have discussed the theory behind the perceptron and logistic regression, including mathematical explanations of how and why they are able to learn from examples.
In this chapter we will transition from math to co... | 8,748 | 8,976 | #!/usr/bin/env python
# coding: utf-8
# # Binary Text Classification with Perceptron
# In[1]:
import random
import numpy as np
from tqdm.notebook import tqdm
# set this variable to a number to be used as the random seed
# or to None if you don't want to set a random seed
seed = 1234
if seed is not None:
rando... | 3,292 | 3,362 | 27 |
chap04-28 | chap04-28 | 4
Implementing Text Classification Using Perceptron and Logistic Regression
In the previous chapters we have discussed the theory behind the perceptron and logistic regression, including mathematical explanations of how and why they are able to learn from examples.
In this chapter we will transition from math to co... | 14,835 | 14,944 | #!/usr/bin/env python
# coding: utf-8
# # Binary Text Classification with
# # Logistic Regression Implemented from Scratch
# In[1]:
import random
import numpy as np
from tqdm.notebook import tqdm
# set this variable to a number to be used as the random seed
# or to None if you don't want to set a random seed
see... | 2,657 | 2,786 | 28 |
chap04-29 | chap04-29 | 4
Implementing Text Classification Using Perceptron and Logistic Regression
In the previous chapters we have discussed the theory behind the perceptron and logistic regression, including mathematical explanations of how and why they are able to learn from examples.
In this chapter we will transition from math to co... | 16,380 | 16,414 | #!/usr/bin/env python
# coding: utf-8
# # Binary Text Classification with
# # Logistic Regression Implemented from Scratch
# In[1]:
import random
import numpy as np
from tqdm.notebook import tqdm
# set this variable to a number to be used as the random seed
# or to None if you don't want to set a random seed
see... | 4,407 | 4,453 | 29 |
chap04-30 | chap04-30 | 4
Implementing Text Classification Using Perceptron and Logistic Regression
In the previous chapters we have discussed the theory behind the perceptron and logistic regression, including mathematical explanations of how and why they are able to learn from examples.
In this chapter we will transition from math to co... | 35,970 | 36,124 | #!/usr/bin/env python
# coding: utf-8
# # Multiclass Text Classification with
# # Logistic Regression Implemented with PyTorch and CE Loss
# 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
tqd... | 5,808 | 5,878 | 30 |
chap04-31 | chap04-31 | 4
Implementing Text Classification Using Perceptron and Logistic Regression
In the previous chapters we have discussed the theory behind the perceptron and logistic regression, including mathematical explanations of how and why they are able to learn from examples.
In this chapter we will transition from math to co... | 35,675 | 35,827 | #!/usr/bin/env python
# coding: utf-8
# # Multiclass Text Classification with
# # Logistic Regression Implemented with PyTorch and CE Loss
# 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
tqd... | 4,140 | 4,174 | 31 |
chap04-32 | chap04-32 | 4
Implementing Text Classification Using Perceptron and Logistic Regression
In the previous chapters we have discussed the theory behind the perceptron and logistic regression, including mathematical explanations of how and why they are able to learn from examples.
In this chapter we will transition from math to co... | 6,304 | 6,379 | #!/usr/bin/env python
# coding: utf-8
# # Binary Text Classification with
# # Logistic Regression Implemented from Scratch
# In[1]:
import random
import numpy as np
from tqdm.notebook import tqdm
# set this variable to a number to be used as the random seed
# or to None if you don't want to set a random seed
see... | 944 | 994 | 32 |
chap04-33 | chap04-33 | 4
Implementing Text Classification Using Perceptron and Logistic Regression
In the previous chapters we have discussed the theory behind the perceptron and logistic regression, including mathematical explanations of how and why they are able to learn from examples.
In this chapter we will transition from math to co... | 25,628 | 25,780 | #!/usr/bin/env python
# coding: utf-8
# # Multiclass Text Classification with
# # Logistic Regression Implemented with PyTorch and CE Loss
# 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
tqd... | 2,284 | 2,453 | 33 |
chap04-34 | chap04-34 | 4
Implementing Text Classification Using Perceptron and Logistic Regression
In the previous chapters we have discussed the theory behind the perceptron and logistic regression, including mathematical explanations of how and why they are able to learn from examples.
In this chapter we will transition from math to co... | 10,310 | 10,409 | #!/usr/bin/env python
# coding: utf-8
# # Binary Text Classification with Perceptron
# In[1]:
import random
import numpy as np
from tqdm.notebook import tqdm
# set this variable to a number to be used as the random seed
# or to None if you don't want to set a random seed
seed = 1234
if seed is not None:
rando... | 3,768 | 3,819 | 34 |
chap04-35 | chap04-35 | 4
Implementing Text Classification Using Perceptron and Logistic Regression
In the previous chapters we have discussed the theory behind the perceptron and logistic regression, including mathematical explanations of how and why they are able to learn from examples.
In this chapter we will transition from math to co... | 12,456 | 12,568 | #!/usr/bin/env python
# coding: utf-8
# # Binary Text Classification with Perceptron
# In[1]:
import random
import numpy as np
from tqdm.notebook import tqdm
# set this variable to a number to be used as the random seed
# or to None if you don't want to set a random seed
seed = 1234
if seed is not None:
rando... | 5,553 | 5,583 | 35 |
chap13-0 | chap13-0 | 13
Using Transformers with the Hugging Face Library
One of the key advantages of transformer networks is the ability to take a model that was pre-trained over vast quantities of text and fine-tune it for the task at hand.
Intuitively, this strategy allows transformer networks to achieve higher performance on smalle... | 10,884 | 10,987 | #!/usr/bin/env python
# coding: utf-8
# # Text Classification Using Transformer Networks (DistilBERT)
# 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 to use the gpu (if there is on... | 3,026 | 3,346 | 0 |
chap13-1 | chap13-1 | 13
Using Transformers with the Hugging Face Library
One of the key advantages of transformer networks is the ability to take a model that was pre-trained over vast quantities of text and fine-tune it for the task at hand.
Intuitively, this strategy allows transformer networks to achieve higher performance on smalle... | 21,067 | 21,185 | #!/usr/bin/env python
# coding: utf-8
# # Part-of-speech Tagging with Transformer Networks
# 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 to use the gpu (if there is one available... | 5,906 | 6,177 | 1 |
chap13-2 | chap13-2 | 13
Using Transformers with the Hugging Face Library
One of the key advantages of transformer networks is the ability to take a model that was pre-trained over vast quantities of text and fine-tune it for the task at hand.
Intuitively, this strategy allows transformer networks to achieve higher performance on smalle... | 20,650 | 20,717 | #!/usr/bin/env python
# coding: utf-8
# # Part-of-speech Tagging with Transformer Networks
# 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 to use the gpu (if there is one available... | 5,503 | 5,702 | 2 |
chap13-3 | chap13-3 | 13
Using Transformers with the Hugging Face Library
One of the key advantages of transformer networks is the ability to take a model that was pre-trained over vast quantities of text and fine-tune it for the task at hand.
Intuitively, this strategy allows transformer networks to achieve higher performance on smalle... | 8,763 | 8,789 | #!/usr/bin/env python
# coding: utf-8
# # Text Classification Using Transformer Networks (BERT)
# 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 to use the gpu (if there is one avai... | 2,992 | 3,054 | 3 |
chap13-4 | chap13-4 | 13
Using Transformers with the Hugging Face Library
One of the key advantages of transformer networks is the ability to take a model that was pre-trained over vast quantities of text and fine-tune it for the task at hand.
Intuitively, this strategy allows transformer networks to achieve higher performance on smalle... | 9,752 | 9,887 | #!/usr/bin/env python
# coding: utf-8
# # Text Classification Using Transformer Networks (BERT)
# 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 to use the gpu (if there is one avai... | 3,497 | 3,591 | 4 |
chap13-5 | chap13-5 | 13
Using Transformers with the Hugging Face Library
One of the key advantages of transformer networks is the ability to take a model that was pre-trained over vast quantities of text and fine-tune it for the task at hand.
Intuitively, this strategy allows transformer networks to achieve higher performance on smalle... | 5,001 | 5,151 | #!/usr/bin/env python
# coding: utf-8
# # Text Classification Using Transformer Networks (DistilBERT)
# 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 to use the gpu (if there is on... | 2,186 | 2,390 | 5 |
chap13-6 | chap13-6 | 13
Using Transformers with the Hugging Face Library
One of the key advantages of transformer networks is the ability to take a model that was pre-trained over vast quantities of text and fine-tune it for the task at hand.
Intuitively, this strategy allows transformer networks to achieve higher performance on smalle... | 22,245 | 22,352 | #!/usr/bin/env python
# coding: utf-8
# # Part-of-speech Tagging with Transformer Networks
# 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 to use the gpu (if there is one available... | 7,764 | 7,862 | 6 |
chap13-7 | chap13-7 | 13
Using Transformers with the Hugging Face Library
One of the key advantages of transformer networks is the ability to take a model that was pre-trained over vast quantities of text and fine-tune it for the task at hand.
Intuitively, this strategy allows transformer networks to achieve higher performance on smalle... | 15,154 | 15,245 | #!/usr/bin/env python
# coding: utf-8
# # Part-of-speech Tagging with Transformer Networks
# 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 to use the gpu (if there is one available... | 3,694 | 3,834 | 7 |
chap13-8 | chap13-8 | 13
Using Transformers with the Hugging Face Library
One of the key advantages of transformer networks is the ability to take a model that was pre-trained over vast quantities of text and fine-tune it for the task at hand.
Intuitively, this strategy allows transformer networks to achieve higher performance on smalle... | 12,449 | 12,650 | #!/usr/bin/env python
# coding: utf-8
# # Text Classification Using Transformer Networks (BERT)
# 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 to use the gpu (if there is one avai... | 4,700 | 4,732 | 8 |
chap13-9 | chap13-9 | 13
Using Transformers with the Hugging Face Library
One of the key advantages of transformer networks is the ability to take a model that was pre-trained over vast quantities of text and fine-tune it for the task at hand.
Intuitively, this strategy allows transformer networks to achieve higher performance on smalle... | 21,653 | 21,887 | #!/usr/bin/env python
# coding: utf-8
# # Part-of-speech Tagging with Transformer Networks
# 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 to use the gpu (if there is one available... | 6,962 | 7,024 | 9 |
chap13-10 | chap13-10 | 13
Using Transformers with the Hugging Face Library
One of the key advantages of transformer networks is the ability to take a model that was pre-trained over vast quantities of text and fine-tune it for the task at hand.
Intuitively, this strategy allows transformer networks to achieve higher performance on smalle... | 21,888 | 21,961 | #!/usr/bin/env python
# coding: utf-8
# # Part-of-speech Tagging with Transformer Networks
# 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 to use the gpu (if there is one available... | 7,025 | 7,253 | 10 |
chap13-11 | chap13-11 | 13
Using Transformers with the Hugging Face Library
One of the key advantages of transformer networks is the ability to take a model that was pre-trained over vast quantities of text and fine-tune it for the task at hand.
Intuitively, this strategy allows transformer networks to achieve higher performance on smalle... | 22,132 | 22,242 | #!/usr/bin/env python
# coding: utf-8
# # Part-of-speech Tagging with Transformer Networks
# 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 to use the gpu (if there is one available... | 7,379 | 7,413 | 11 |
chap13-12 | chap13-12 | 13
Using Transformers with the Hugging Face Library
One of the key advantages of transformer networks is the ability to take a model that was pre-trained over vast quantities of text and fine-tune it for the task at hand.
Intuitively, this strategy allows transformer networks to achieve higher performance on smalle... | 9,165 | 9,306 | #!/usr/bin/env python
# coding: utf-8
# # Text Classification Using Transformer Networks (BERT)
# 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 to use the gpu (if there is one avai... | 3,270 | 3,440 | 12 |
chap13-13 | chap13-13 | 13
Using Transformers with the Hugging Face Library
One of the key advantages of transformer networks is the ability to take a model that was pre-trained over vast quantities of text and fine-tune it for the task at hand.
Intuitively, this strategy allows transformer networks to achieve higher performance on smalle... | 4,505 | 4,835 | #!/usr/bin/env python
# coding: utf-8
# # Text Classification Using Transformer Networks (BERT)
# 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 to use the gpu (if there is one avai... | 1,983 | 2,080 | 13 |
chap13-14 | chap13-14 | 13
Using Transformers with the Hugging Face Library
One of the key advantages of transformer networks is the ability to take a model that was pre-trained over vast quantities of text and fine-tune it for the task at hand.
Intuitively, this strategy allows transformer networks to achieve higher performance on smalle... | 10,104 | 10,186 | #!/usr/bin/env python
# coding: utf-8
# # Text Classification Using Transformer Networks (BERT)
# 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 to use the gpu (if there is one avai... | 3,972 | 4,162 | 14 |
chap13-15 | chap13-15 | 13
Using Transformers with the Hugging Face Library
One of the key advantages of transformer networks is the ability to take a model that was pre-trained over vast quantities of text and fine-tune it for the task at hand.
Intuitively, this strategy allows transformer networks to achieve higher performance on smalle... | 13,115 | 13,198 | #!/usr/bin/env python
# coding: utf-8
# # Text Classification Using Transformer Networks (BERT)
# 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 to use the gpu (if there is one avai... | 5,116 | 5,132 | 15 |
chap13-16 | chap13-16 | 13
Using Transformers with the Hugging Face Library
One of the key advantages of transformer networks is the ability to take a model that was pre-trained over vast quantities of text and fine-tune it for the task at hand.
Intuitively, this strategy allows transformer networks to achieve higher performance on smalle... | 21,188 | 21,383 | #!/usr/bin/env python
# coding: utf-8
# # Part-of-speech Tagging with Transformer Networks
# 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 to use the gpu (if there is one available... | 6,235 | 6,267 | 16 |
chap13-17 | chap13-17 | 13
Using Transformers with the Hugging Face Library
One of the key advantages of transformer networks is the ability to take a model that was pre-trained over vast quantities of text and fine-tune it for the task at hand.
Intuitively, this strategy allows transformer networks to achieve higher performance on smalle... | 8,794 | 8,860 | #!/usr/bin/env python
# coding: utf-8
# # Text Classification Using Transformer Networks (BERT)
# 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 to use the gpu (if there is one avai... | 3,054 | 3,129 | 17 |
chap13-18 | chap13-18 | 13
Using Transformers with the Hugging Face Library
One of the key advantages of transformer networks is the ability to take a model that was pre-trained over vast quantities of text and fine-tune it for the task at hand.
Intuitively, this strategy allows transformer networks to achieve higher performance on smalle... | 14,478 | 14,609 | #!/usr/bin/env python
# coding: utf-8
# # Part-of-speech Tagging with Transformer Networks
# 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 to use the gpu (if there is one available... | 2,532 | 2,570 | 18 |
chap13-19 | chap13-19 | 13
Using Transformers with the Hugging Face Library
One of the key advantages of transformer networks is the ability to take a model that was pre-trained over vast quantities of text and fine-tune it for the task at hand.
Intuitively, this strategy allows transformer networks to achieve higher performance on smalle... | 8,031 | 8,244 | #!/usr/bin/env python
# coding: utf-8
# # Text Classification Using Transformer Networks (BERT)
# 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 to use the gpu (if there is one avai... | 2,787 | 2,845 | 19 |
chap13-20 | chap13-20 | 13
Using Transformers with the Hugging Face Library
One of the key advantages of transformer networks is the ability to take a model that was pre-trained over vast quantities of text and fine-tune it for the task at hand.
Intuitively, this strategy allows transformer networks to achieve higher performance on smalle... | 8,703 | 8,761 | #!/usr/bin/env python
# coding: utf-8
# # Text Classification Using Transformer Networks (BERT)
# 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 to use the gpu (if there is one avai... | 2,954 | 2,992 | 20 |
chap13-21 | chap13-21 | 13
Using Transformers with the Hugging Face Library
One of the key advantages of transformer networks is the ability to take a model that was pre-trained over vast quantities of text and fine-tune it for the task at hand.
Intuitively, this strategy allows transformer networks to achieve higher performance on smalle... | 13,908 | 14,026 | #!/usr/bin/env python
# coding: utf-8
# # Part-of-speech Tagging with Transformer Networks
# 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 to use the gpu (if there is one available... | 1,570 | 1,724 | 21 |
chap13-22 | chap13-22 | 13
Using Transformers with the Hugging Face Library
One of the key advantages of transformer networks is the ability to take a model that was pre-trained over vast quantities of text and fine-tune it for the task at hand.
Intuitively, this strategy allows transformer networks to achieve higher performance on smalle... | 17,980 | 18,264 | #!/usr/bin/env python
# coding: utf-8
# # Part-of-speech Tagging with Transformer Networks
# 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 to use the gpu (if there is one available... | 4,233 | 4,301 | 22 |
chap13-23 | chap13-23 | 13
Using Transformers with the Hugging Face Library
One of the key advantages of transformer networks is the ability to take a model that was pre-trained over vast quantities of text and fine-tune it for the task at hand.
Intuitively, this strategy allows transformer networks to achieve higher performance on smalle... | 4,221 | 4,328 | #!/usr/bin/env python
# coding: utf-8
# # Text Classification Using Transformer Networks (BERT)
# 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 to use the gpu (if there is one avai... | 1,751 | 1,904 | 23 |
chap13-24 | chap13-24 | 13
Using Transformers with the Hugging Face Library
One of the key advantages of transformer networks is the ability to take a model that was pre-trained over vast quantities of text and fine-tune it for the task at hand.
Intuitively, this strategy allows transformer networks to achieve higher performance on smalle... | 8,972 | 9,164 | #!/usr/bin/env python
# coding: utf-8
# # Text Classification Using Transformer Networks (BERT)
# 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 to use the gpu (if there is one avai... | 3,166 | 3,270 | 24 |
chap13-25 | chap13-25 | 13
Using Transformers with the Hugging Face Library
One of the key advantages of transformer networks is the ability to take a model that was pre-trained over vast quantities of text and fine-tune it for the task at hand.
Intuitively, this strategy allows transformer networks to achieve higher performance on smalle... | 12,799 | 12,885 | #!/usr/bin/env python
# coding: utf-8
# # Text Classification Using Transformer Networks (DistilBERT)
# 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 to use the gpu (if there is on... | 3,628 | 3,647 | 25 |
chap09-0 | chap09-0 | 9
Implementing Text Classification Using Word Embeddings
In the previous chapter we introduced word embeddings, which are realvalued vectors that encode semantic representation of words.
We discussed how to learn them, and how they capture semantic information that makes them useful for downstream tasks.
In this ... | 8,905 | 9,068 | #!/usr/bin/env python
# coding: utf-8
# # Using Pre-trained Word Embeddings
#
# In this notebook we will show some operations on pre-trained word embeddings to gain an intuition about them.
#
# We will be using the pre-trained GloVe embeddings that can be found in the [official website](https://nlp.stanford.edu/proj... | 6,116 | 6,403 | 0 |
chap09-1 | chap09-1 | 9
Implementing Text Classification Using Word Embeddings
In the previous chapter we introduced word embeddings, which are realvalued vectors that encode semantic representation of words.
We discussed how to learn them, and how they capture semantic information that makes them useful for downstream tasks.
In this ... | 12,872 | 12,940 | #!/usr/bin/env python
# coding: utf-8
# # Multiclass Text Classification with
# # Feed-forward Neural Networks and Word Embeddings
# 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... | 6,683 | 6,709 | 1 |
chap09-2 | chap09-2 | 9
Implementing Text Classification Using Word Embeddings
In the previous chapter we introduced word embeddings, which are realvalued vectors that encode semantic representation of words.
We discussed how to learn them, and how they capture semantic information that makes them useful for downstream tasks.
In this ... | 12,705 | 12,869 | #!/usr/bin/env python
# coding: utf-8
# # Multiclass Text Classification with
# # Feed-forward Neural Networks and Word Embeddings
# 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... | 5,410 | 5,538 | 2 |
chap09-3 | chap09-3 | 9
Implementing Text Classification Using Word Embeddings
In the previous chapter we introduced word embeddings, which are realvalued vectors that encode semantic representation of words.
We discussed how to learn them, and how they capture semantic information that makes them useful for downstream tasks.
In this ... | 7,625 | 7,862 | #!/usr/bin/env python
# coding: utf-8
# # Using Pre-trained Word Embeddings
#
# In this notebook we will show some operations on pre-trained word embeddings to gain an intuition about them.
#
# We will be using the pre-trained GloVe embeddings that can be found in the [official website](https://nlp.stanford.edu/proj... | 5,543 | 5,619 | 3 |
chap09-4 | chap09-4 | 9
Implementing Text Classification Using Word Embeddings
In the previous chapter we introduced word embeddings, which are realvalued vectors that encode semantic representation of words.
We discussed how to learn them, and how they capture semantic information that makes them useful for downstream tasks.
In this ... | 8,624 | 8,814 | #!/usr/bin/env python
# coding: utf-8
# # Using Pre-trained Word Embeddings
#
# In this notebook we will show some operations on pre-trained word embeddings to gain an intuition about them.
#
# We will be using the pre-trained GloVe embeddings that can be found in the [official website](https://nlp.stanford.edu/proj... | 5,934 | 5,977 | 4 |
chap09-5 | chap09-5 | 9
Implementing Text Classification Using Word Embeddings
In the previous chapter we introduced word embeddings, which are realvalued vectors that encode semantic representation of words.
We discussed how to learn them, and how they capture semantic information that makes them useful for downstream tasks.
In this ... | 5,800 | 6,076 | #!/usr/bin/env python
# coding: utf-8
# # Using Pre-trained Word Embeddings
#
# In this notebook we will show some operations on pre-trained word embeddings to gain an intuition about them.
#
# We will be using the pre-trained GloVe embeddings that can be found in the [official website](https://nlp.stanford.edu/proj... | 3,119 | 3,184 | 5 |
chap09-6 | chap09-6 | 9
Implementing Text Classification Using Word Embeddings
In the previous chapter we introduced word embeddings, which are realvalued vectors that encode semantic representation of words.
We discussed how to learn them, and how they capture semantic information that makes them useful for downstream tasks.
In this ... | 14,409 | 14,731 | #!/usr/bin/env python
# coding: utf-8
# # Multiclass Text Classification with
# # Feed-forward Neural Networks and Word Embeddings
# 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... | 8,036 | 8,223 | 6 |
chap09-7 | chap09-7 | 9
Implementing Text Classification Using Word Embeddings
In the previous chapter we introduced word embeddings, which are realvalued vectors that encode semantic representation of words.
We discussed how to learn them, and how they capture semantic information that makes them useful for downstream tasks.
In this ... | 11,746 | 11,808 | #!/usr/bin/env python
# coding: utf-8
# # Multiclass Text Classification with
# # Feed-forward Neural Networks and Word Embeddings
# 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... | 4,803 | 4,840 | 7 |
chap09-8 | chap09-8 | 9
Implementing Text Classification Using Word Embeddings
In the previous chapter we introduced word embeddings, which are realvalued vectors that encode semantic representation of words.
We discussed how to learn them, and how they capture semantic information that makes them useful for downstream tasks.
In this ... | 9,339 | 9,440 | #!/usr/bin/env python
# coding: utf-8
# # Using Pre-trained Word Embeddings
#
# In this notebook we will show some operations on pre-trained word embeddings to gain an intuition about them.
#
# We will be using the pre-trained GloVe embeddings that can be found in the [official website](https://nlp.stanford.edu/proj... | 6,471 | 6,583 | 8 |
chap09-9 | chap09-9 | 9
Implementing Text Classification Using Word Embeddings
In the previous chapter we introduced word embeddings, which are realvalued vectors that encode semantic representation of words.
We discussed how to learn them, and how they capture semantic information that makes them useful for downstream tasks.
In this ... | 13,150 | 13,264 | #!/usr/bin/env python
# coding: utf-8
# # Multiclass Text Classification with
# # Feed-forward Neural Networks and Word Embeddings
# 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... | 7,474 | 7,552 | 9 |
chap09-10 | chap09-10 | 9
Implementing Text Classification Using Word Embeddings
In the previous chapter we introduced word embeddings, which are realvalued vectors that encode semantic representation of words.
We discussed how to learn them, and how they capture semantic information that makes them useful for downstream tasks.
In this ... | 13,851 | 14,021 | #!/usr/bin/env python
# coding: utf-8
# # Multiclass Text Classification with
# # Feed-forward Neural Networks and Word Embeddings
# 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... | 7,581 | 7,817 | 10 |
chap09-11 | chap09-11 | 9
Implementing Text Classification Using Word Embeddings
In the previous chapter we introduced word embeddings, which are realvalued vectors that encode semantic representation of words.
We discussed how to learn them, and how they capture semantic information that makes them useful for downstream tasks.
In this ... | 14,346 | 14,408 | #!/usr/bin/env python
# coding: utf-8
# # Multiclass Text Classification with
# # Feed-forward Neural Networks and Word Embeddings
# 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... | 7,826 | 7,852 | 11 |
chap09-12 | chap09-12 | 9
Implementing Text Classification Using Word Embeddings
In the previous chapter we introduced word embeddings, which are realvalued vectors that encode semantic representation of words.
We discussed how to learn them, and how they capture semantic information that makes them useful for downstream tasks.
In this ... | 8,105 | 8,197 | #!/usr/bin/env python
# coding: utf-8
# # Using Pre-trained Word Embeddings
#
# In this notebook we will show some operations on pre-trained word embeddings to gain an intuition about them.
#
# We will be using the pre-trained GloVe embeddings that can be found in the [official website](https://nlp.stanford.edu/proj... | 5,830 | 5,863 | 12 |
chap09-13 | chap09-13 | 9
Implementing Text Classification Using Word Embeddings
In the previous chapter we introduced word embeddings, which are realvalued vectors that encode semantic representation of words.
We discussed how to learn them, and how they capture semantic information that makes them useful for downstream tasks.
In this ... | 4,512 | 4,666 | #!/usr/bin/env python
# coding: utf-8
# # Using Pre-trained Word Embeddings
#
# In this notebook we will show some operations on pre-trained word embeddings to gain an intuition about them.
#
# We will be using the pre-trained GloVe embeddings that can be found in the [official website](https://nlp.stanford.edu/proj... | 2,221 | 2,250 | 13 |
chap09-14 | chap09-14 | 9
Implementing Text Classification Using Word Embeddings
In the previous chapter we introduced word embeddings, which are realvalued vectors that encode semantic representation of words.
We discussed how to learn them, and how they capture semantic information that makes them useful for downstream tasks.
In this ... | 8,815 | 8,904 | #!/usr/bin/env python
# coding: utf-8
# # Using Pre-trained Word Embeddings
#
# In this notebook we will show some operations on pre-trained word embeddings to gain an intuition about them.
#
# We will be using the pre-trained GloVe embeddings that can be found in the [official website](https://nlp.stanford.edu/proj... | 6,000 | 6,041 | 14 |
chap09-15 | chap09-15 | 9
Implementing Text Classification Using Word Embeddings
In the previous chapter we introduced word embeddings, which are realvalued vectors that encode semantic representation of words.
We discussed how to learn them, and how they capture semantic information that makes them useful for downstream tasks.
In this ... | 9,918 | 10,014 | #!/usr/bin/env python
# coding: utf-8
# # Using Pre-trained Word Embeddings
#
# In this notebook we will show some operations on pre-trained word embeddings to gain an intuition about them.
#
# We will be using the pre-trained GloVe embeddings that can be found in the [official website](https://nlp.stanford.edu/proj... | 7,688 | 7,774 | 15 |
chap09-16 | chap09-16 | 9
Implementing Text Classification Using Word Embeddings
In the previous chapter we introduced word embeddings, which are realvalued vectors that encode semantic representation of words.
We discussed how to learn them, and how they capture semantic information that makes them useful for downstream tasks.
In this ... | 13,061 | 13,149 | #!/usr/bin/env python
# coding: utf-8
# # Multiclass Text Classification with
# # Feed-forward Neural Networks and Word Embeddings
# 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... | 7,140 | 7,164 | 16 |
chap09-17 | chap09-17 | 9
Implementing Text Classification Using Word Embeddings
In the previous chapter we introduced word embeddings, which are realvalued vectors that encode semantic representation of words.
We discussed how to learn them, and how they capture semantic information that makes them useful for downstream tasks.
In this ... | 7,933 | 8,007 | #!/usr/bin/env python
# coding: utf-8
# # Using Pre-trained Word Embeddings
#
# In this notebook we will show some operations on pre-trained word embeddings to gain an intuition about them.
#
# We will be using the pre-trained GloVe embeddings that can be found in the [official website](https://nlp.stanford.edu/proj... | 5,670 | 5,703 | 17 |
chap09-18 | chap09-18 | 9
Implementing Text Classification Using Word Embeddings
In the previous chapter we introduced word embeddings, which are realvalued vectors that encode semantic representation of words.
We discussed how to learn them, and how they capture semantic information that makes them useful for downstream tasks.
In this ... | 11,071 | 11,197 | #!/usr/bin/env python
# coding: utf-8
# # Multiclass Text Classification with
# # Feed-forward Neural Networks and Word Embeddings
# 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... | 3,402 | 3,564 | 18 |
chap09-19 | chap09-19 | 9
Implementing Text Classification Using Word Embeddings
In the previous chapter we introduced word embeddings, which are realvalued vectors that encode semantic representation of words.
We discussed how to learn them, and how they capture semantic information that makes them useful for downstream tasks.
In this ... | 6,751 | 6,861 | #!/usr/bin/env python
# coding: utf-8
# # Using Pre-trained Word Embeddings
#
# In this notebook we will show some operations on pre-trained word embeddings to gain an intuition about them.
#
# We will be using the pre-trained GloVe embeddings that can be found in the [official website](https://nlp.stanford.edu/proj... | 4,732 | 4,776 | 19 |
chap09-20 | chap09-20 | 9
Implementing Text Classification Using Word Embeddings
In the previous chapter we introduced word embeddings, which are realvalued vectors that encode semantic representation of words.
We discussed how to learn them, and how they capture semantic information that makes them useful for downstream tasks.
In this ... | 7,099 | 7,302 | #!/usr/bin/env python
# coding: utf-8
# # Using Pre-trained Word Embeddings
#
# In this notebook we will show some operations on pre-trained word embeddings to gain an intuition about them.
#
# We will be using the pre-trained GloVe embeddings that can be found in the [official website](https://nlp.stanford.edu/proj... | 5,129 | 5,183 | 20 |
chap09-21 | chap09-21 | 9
Implementing Text Classification Using Word Embeddings
In the previous chapter we introduced word embeddings, which are realvalued vectors that encode semantic representation of words.
We discussed how to learn them, and how they capture semantic information that makes them useful for downstream tasks.
In this ... | 10,015 | 10,100 | #!/usr/bin/env python
# coding: utf-8
# # Using Pre-trained Word Embeddings
#
# In this notebook we will show some operations on pre-trained word embeddings to gain an intuition about them.
#
# We will be using the pre-trained GloVe embeddings that can be found in the [official website](https://nlp.stanford.edu/proj... | 7,806 | 7,886 | 21 |
chap09-22 | chap09-22 | 9
Implementing Text Classification Using Word Embeddings
In the previous chapter we introduced word embeddings, which are realvalued vectors that encode semantic representation of words.
We discussed how to learn them, and how they capture semantic information that makes them useful for downstream tasks.
In this ... | 11,907 | 11,965 | #!/usr/bin/env python
# coding: utf-8
# # Multiclass Text Classification with
# # Feed-forward Neural Networks and Word Embeddings
# 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... | 4,840 | 4,864 | 22 |
chap09-23 | chap09-23 | 9
Implementing Text Classification Using Word Embeddings
In the previous chapter we introduced word embeddings, which are realvalued vectors that encode semantic representation of words.
We discussed how to learn them, and how they capture semantic information that makes them useful for downstream tasks.
In this ... | 3,607 | 3,794 | #!/usr/bin/env python
# coding: utf-8
# # Using Pre-trained Word Embeddings
#
# In this notebook we will show some operations on pre-trained word embeddings to gain an intuition about them.
#
# We will be using the pre-trained GloVe embeddings that can be found in the [official website](https://nlp.stanford.edu/proj... | 1,269 | 1,308 | 23 |
chap09-24 | chap09-24 | 9
Implementing Text Classification Using Word Embeddings
In the previous chapter we introduced word embeddings, which are realvalued vectors that encode semantic representation of words.
We discussed how to learn them, and how they capture semantic information that makes them useful for downstream tasks.
In this ... | 8,013 | 8,087 | #!/usr/bin/env python
# coding: utf-8
# # Using Pre-trained Word Embeddings
#
# In this notebook we will show some operations on pre-trained word embeddings to gain an intuition about them.
#
# We will be using the pre-trained GloVe embeddings that can be found in the [official website](https://nlp.stanford.edu/proj... | 5,743 | 5,770 | 24 |
chap09-25 | chap09-25 | 9
Implementing Text Classification Using Word Embeddings
In the previous chapter we introduced word embeddings, which are realvalued vectors that encode semantic representation of words.
We discussed how to learn them, and how they capture semantic information that makes them useful for downstream tasks.
In this ... | 9,647 | 9,764 | #!/usr/bin/env python
# coding: utf-8
# # Using Pre-trained Word Embeddings
#
# In this notebook we will show some operations on pre-trained word embeddings to gain an intuition about them.
#
# We will be using the pre-trained GloVe embeddings that can be found in the [official website](https://nlp.stanford.edu/proj... | 7,370 | 7,449 | 25 |
chap15-0 | chap15-0 | 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 ... | 4,780 | 5,004 | #!/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... | 2,097 | 2,191 | 0 |
chap15-1 | chap15-1 | 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 ... | 15,290 | 15,435 | #!/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... | 2,412 | 2,529 | 1 |
chap15-2 | chap15-2 | 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 ... | 13,298 | 13,371 | #!/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... | 2,070 | 2,273 | 2 |
chap15-3 | chap15-3 | 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 ... | 2,741 | 3,052 | #!/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... | 1,129 | 1,184 | 3 |
chap15-4 | chap15-4 | 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 ... | 3,908 | 3,959 | #!/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... | 1,721 | 1,821 | 4 |
chap15-5 | chap15-5 | 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 ... | 18,161 | 18,623 | #!/usr/bin/env python
# coding: utf-8
# # Load and Use a Previously-trained Ro-to-En T5 Model
# 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 = True
# select device
device = torch.device('cuda' if u... | 845 | 1,104 | 5 |
chap15-6 | chap15-6 | 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 ... | 1,847 | 2,011 | #!/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... | 653 | 678 | 6 |
chap15-7 | chap15-7 | 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 ... | 17,694 | 17,934 | #!/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... | 5,145 | 5,437 | 7 |
chap15-8 | chap15-8 | 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 ... | 12,139 | 12,249 | #!/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... | 874 | 930 | 8 |
chap15-9 | chap15-9 | 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 ... | 6,284 | 6,488 | #!/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... | 2,702 | 2,736 | 9 |
chap15-10 | chap15-10 | 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 ... | 15,789 | 15,918 | #!/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... | 3,847 | 4,075 | 10 |
chap15-11 | chap15-11 | 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 ... | 16,413 | 16,607 | #!/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... | 4,388 | 4,457 | 11 |
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