Fastai transforms¶
We directly copied and pasted part of the transforms.py
module from
the fastai
library (from an old version). The reason to do such a thing is because
pytorch_widedeep
only needs the Tokenizer
and the Vocab
classes
there. This way we avoid extra dependencies. Credit for all the code in the
fastai_transforms
module in this pytorch-widedeep
package goes to
Jeremy Howard and the fastai
team. I only include the documentation here for
completion, but I strongly advise the user to read the fastai
documentation.
Tokenizer ¶
Class to combine a series of rules and a tokenizer function to tokenize text with multiprocessing.
Setting some of the parameters of this class require perhaps some familiarity with the source code.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
tok_func
|
Callable
|
Tokenizer Object. See |
SpacyTokenizer
|
lang
|
str
|
Text's Language |
'en'
|
pre_rules
|
Optional[ListRules]
|
Custom type: |
None
|
post_rules
|
Optional[ListRules]
|
Custom type: |
None
|
special_cases
|
Optional[Collection[str]]
|
special cases to be added to the tokenizer via |
None
|
n_cpus
|
Optional[int]
|
number of CPUs to used during the tokenization process |
None
|
Source code in pytorch_widedeep/utils/fastai_transforms.py
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process_text ¶
process_text(t, tok)
Process and tokenize one text t
with tokenizer tok
.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
t
|
str
|
text to be processed and tokenized |
required |
tok
|
BaseTokenizer
|
Instance of |
required |
Returns:
Type | Description |
---|---|
List[str]
|
List of tokens |
Source code in pytorch_widedeep/utils/fastai_transforms.py
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|
process_all ¶
process_all(texts)
Process a list of texts. Parallel execution of process_text
.
Examples:
>>> from pytorch_widedeep.utils import Tokenizer
>>> texts = ['Machine learning is great', 'but building stuff is even better']
>>> tok = Tokenizer()
>>> tok.process_all(texts)
[['xxmaj', 'machine', 'learning', 'is', 'great'], ['but', 'building', 'stuff', 'is', 'even', 'better']]
NOTE:
Note the token TK_MAJ
(xxmaj
), used to indicate the
next word begins with a capital in the original text. For more
details of special tokens please see the fastai
docs.
Returns:
Type | Description |
---|---|
List[List[str]]
|
List containing lists of tokens. One list per "document" |
Source code in pytorch_widedeep/utils/fastai_transforms.py
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Vocab ¶
Contains the correspondence between numbers and tokens.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
max_vocab
|
int
|
maximum vocabulary size |
required |
min_freq
|
int
|
minimum frequency for a token to be considereds |
required |
pad_idx
|
Optional[int]
|
padding index. If |
None
|
Attributes:
Name | Type | Description |
---|---|---|
itos |
Collection
|
|
stoi |
defaultdict
|
|
Source code in pytorch_widedeep/utils/fastai_transforms.py
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|
create ¶
create(tokens)
Create a vocabulary object from a set of tokens.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
tokens
|
Tokens
|
Custom type: |
required |
Examples:
>>> from pytorch_widedeep.utils import Tokenizer, Vocab
>>> texts = ['Machine learning is great', 'but building stuff is even better']
>>> tokens = Tokenizer().process_all(texts)
>>> vocab = Vocab(max_vocab=18, min_freq=1).create(tokens)
>>> vocab.numericalize(['machine', 'learning', 'is', 'great'])
[10, 11, 9, 12]
>>> vocab.textify([10, 11, 9, 12])
'machine learning is great'
NOTE:
Note the many special tokens that fastai
's' tokenizer adds. These
are particularly useful when building Language models and/or in
classification/Regression tasks. Please see the fastai
docs.
Returns:
Type | Description |
---|---|
Vocab
|
An instance of a |
Source code in pytorch_widedeep/utils/fastai_transforms.py
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|
fit ¶
fit(tokens)
Calls the create
method. I simply want to honor fast ai naming, but
for consistency with the rest of the library I am including a fit method
Source code in pytorch_widedeep/utils/fastai_transforms.py
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numericalize ¶
numericalize(t)
Convert a list of tokens t
to their ids.
Returns:
Type | Description |
---|---|
List[int]
|
List of 'numericalsed' tokens |
Source code in pytorch_widedeep/utils/fastai_transforms.py
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|
transform ¶
transform(t)
Calls the numericalize
method. I simply want to honor fast ai naming,
but for consistency with the rest of the library I am including a
transform method
Source code in pytorch_widedeep/utils/fastai_transforms.py
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|
textify ¶
textify(nums, sep=' ')
Convert a list of nums
(or indexes) to their tokens.
Returns:
Type | Description |
---|---|
List[str]
|
List of tokens |
Source code in pytorch_widedeep/utils/fastai_transforms.py
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|
inverse_transform ¶
inverse_transform(nums, sep=' ')
Calls the textify
method. I simply want to honor fast ai naming, but
for consistency with the rest of the library I am including an
inverse_transform method
Source code in pytorch_widedeep/utils/fastai_transforms.py
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