Precooked NERDA models
This sections covers NERDA Models that have been 'precooked' by Ekstra Bladet and are publicly available for download.
DA_BERT_ML
NERDA Multilingual BERT for Danish Finetuned on DaNE data set.
Inherits from NERDA.precooked.Precooked.
Examples:
>>> from NERDA.precooked import DA_BERT_ML()
>>> model = DA_BERT_ML()
>>> model.download_network()
>>> model.load_network()
>>> text = 'Jens Hansen har en bondegård'
>>> model.predict_text(text)
([['Jens', 'Hansen', 'har', 'en', 'bondegård']], [['B-PER', 'I-PER', 'O', 'O', 'O']])
__init__(self, device=None)
special
Initialize model
Source code in NERDA/precooked.py
def __init__(self, device: str = None) -> None:
"""Initialize model"""
super().__init__(transformer = 'bert-base-multilingual-uncased',
device = device,
tag_scheme = [
'B-PER',
'I-PER',
'B-ORG',
'I-ORG',
'B-LOC',
'I-LOC',
'B-MISC',
'I-MISC'
],
tag_outside = 'O',
max_len = 128,
dropout = 0.1,
hyperparameters = {'epochs' : 4,
'warmup_steps' : 500,
'train_batch_size': 13,
'learning_rate': 0.0001},
tokenizer_parameters = {'do_lower_case' : True})
DA_DISTILBERT_ML
NERDA Multilingual BERT for Danish Finetuned on DaNE data set.
Inherits from NERDA.precooked.Precooked.
Examples:
>>> from NERDA.precooked import DA_DISTILBERT_ML()
>>> model = DA_DISTILBERT_ML()
>>> model.download_network()
>>> model.load_network()
>>> text = 'Jens Hansen har en bondegård'
>>> model.predict_text(text)
([['Jens', 'Hansen', 'har', 'en', 'bondegård']], [['B-PER', 'I-PER', 'O', 'O', 'O']])
__init__(self, device=None)
special
Initialize model
Source code in NERDA/precooked.py
def __init__(self, device: str = None) -> None:
"""Initialize model"""
super().__init__(transformer = 'distilbert-base-multilingual-cased',
device = device,
tag_scheme = [
'B-PER',
'I-PER',
'B-ORG',
'I-ORG',
'B-LOC',
'I-LOC',
'B-MISC',
'I-MISC'
],
tag_outside = 'O',
max_len = 128,
dropout = 0.1,
hyperparameters = {'epochs' : 4,
'warmup_steps' : 500,
'train_batch_size': 13,
'learning_rate': 0.0001},
tokenizer_parameters = {'do_lower_case' : False})
DA_ELECTRA_DA
NERDA Danish ELECTRA for Danish finetuned on DaNE data set.
Inherits from NERDA.precooked.Precooked.
Examples:
>>> from NERDA.precooked import DA_ELECTRA_DA()
>>> model = DA_ELECTRA_DA()
>>> model.download_network()
>>> model.load_network()
>>> text = 'Jens Hansen har en bondegård'
>>> model.predict_text(text)
([['Jens', 'Hansen', 'har', 'en', 'bondegård']], [['B-PER', 'I-PER', 'O', 'O', 'O']])
__init__(self, device=None)
special
Initialize model
Source code in NERDA/precooked.py
def __init__(self, device: str = None) -> None:
"""Initialize model"""
super().__init__(transformer = 'Maltehb/-l-ctra-danish-electra-small-uncased',
device = device,
tag_scheme = [
'B-PER',
'I-PER',
'B-ORG',
'I-ORG',
'B-LOC',
'I-LOC',
'B-MISC',
'I-MISC'
],
tag_outside = 'O',
max_len = 128,
dropout = 0.1,
hyperparameters = {'epochs' : 5,
'warmup_steps' : 500,
'train_batch_size': 13,
'learning_rate': 0.0001},
tokenizer_parameters = {'do_lower_case' : True})
EN_BERT_ML
NERDA Multilingual BERT for English finetuned on CoNLL-2003 data set.
Inherits from NERDA.precooked.Precooked.
Examples:
>>> from NERDA.precooked import EN_BERT_ML()
>>> model = EN_BERT_ML()
>>> model.download_network()
>>> model.load_network()
>>> text = 'Old MacDonald had a farm'
>>> model.predict_text(text)
([['Old', 'MacDonald', 'had', 'a', 'farm']], [['B-PER', 'I-PER', 'O', 'O', 'O']])
__init__(self, device=None)
special
Initialize model
Source code in NERDA/precooked.py
def __init__(self, device: str = None) -> None:
"""Initialize model"""
super().__init__(transformer = 'bert-base-multilingual-uncased',
device = device,
tag_scheme = [
'B-PER',
'I-PER',
'B-ORG',
'I-ORG',
'B-LOC',
'I-LOC',
'B-MISC',
'I-MISC'
],
tag_outside = 'O',
max_len = 128,
dropout = 0.1,
hyperparameters = {'epochs' : 4,
'warmup_steps' : 500,
'train_batch_size': 13,
'learning_rate': 0.0001},
tokenizer_parameters = {'do_lower_case' : True})
EN_ELECTRA_EN
NERDA English ELECTRA for English finetuned on CoNLL-2003 data set.
Inherits from NERDA.precooked.Precooked.
Examples:
>>> from NERDA.precooked import EN_ELECTRA_EN()
>>> model = EN_ELECTRA_EN()
>>> model.download_network()
>>> model.load_network()
>>> text = 'Old MacDonald had a farm'
>>> model.predict_text(text)
([['Old', 'MacDonald', 'had', 'a', 'farm']], [['B-PER', 'I-PER', 'O', 'O', 'O']])
__init__(self, device=None)
special
Initialize model
Source code in NERDA/precooked.py
def __init__(self, device: str = None) -> None:
"""Initialize model"""
super().__init__(transformer = 'google/electra-small-discriminator',
device = device,
tag_scheme = [
'B-PER',
'I-PER',
'B-ORG',
'I-ORG',
'B-LOC',
'I-LOC',
'B-MISC',
'I-MISC'
],
tag_outside = 'O',
max_len = 128,
dropout = 0.1,
hyperparameters = {'epochs' : 4,
'warmup_steps' : 250,
'train_batch_size': 13,
'learning_rate': 8e-05},
tokenizer_parameters = {'do_lower_case' : True})
Precooked
Precooked NERDA Model
NERDA model specification that has been precooked/pretrained and is available for download.
Inherits from NERDA.models.NERDA.
__init__(self, **kwargs)
special
Initialize Precooked NERDA Model
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
kwargs |
|
all arguments for NERDA Model. |
{} |
Source code in NERDA/precooked.py
def __init__(self, **kwargs) -> None:
"""Initialize Precooked NERDA Model
Args:
kwargs: all arguments for NERDA Model.
"""
super().__init__(**kwargs)
download_network(self, dir=None)
Download Precooked Network from Web
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
dir |
str |
Directory where the model file will be saved. Defaults to None, in which case the model will be saved in a folder '.nerda' in your home directory. |
None |
Returns:
| Type | Description |
|---|---|
None |
str: Message saying if the download was successfull. Model is downloaded as a side-effect. |
Source code in NERDA/precooked.py
def download_network(self, dir = None) -> None:
"""Download Precooked Network from Web
Args:
dir (str, optional): Directory where the model file
will be saved. Defaults to None, in which case
the model will be saved in a folder '.nerda' in
your home directory.
Returns:
str: Message saying if the download was successfull.
Model is downloaded as a side-effect.
"""
model_name = type(self).__name__
# url for public S3 bucket with NERDA models.
url_s3 = 'https://nerda.s3-eu-west-1.amazonaws.com'
url_model = f'{url_s3}/{model_name}.bin'
if dir is None:
dir = os.path.join(str(Path.home()), '.nerda')
if not os.path.exists(dir):
os.mkdir(dir)
file_path = os.path.join(dir, f'{model_name}.bin')
print(
"""
Please make sure, that you're running the latest version of 'NERDA'
otherwise the model is not guaranteed to work.
"""
)
print(f'Downloading {url_model} to {file_path}')
urllib.request.urlretrieve(url_model, file_path, show_progress)
return "Network downloaded successfully. Load network with 'load_network'."
load_network(self, file_path=None)
Load Pretrained Network
Loads pretrained network from file.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
file_path |
str |
Path to model file. Defaults to None, in which case, the function points to the '.nerda' folder the home directory. |
None |
Source code in NERDA/precooked.py
def load_network(self, file_path: str = None) -> None:
"""Load Pretrained Network
Loads pretrained network from file.
Args:
file_path (str, optional): Path to model file. Defaults to None,
in which case, the function points to the '.nerda' folder
the home directory.
"""
model_name = type(self).__name__
if file_path is None:
file_path = os.path.join(str(Path.home()), '.nerda', f'{model_name}.bin')
assert os.path.exists(file_path), "File does not exist! You can download network with download_network()"
print(
"""
Model loaded. Please make sure, that you're running the latest version
of 'NERDA' otherwise the model is not guaranteed to work.
"""
)
self.load_network_from_file(file_path)