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Hafiz, Aqib (PG/T - Comp Sci & Elec Eng)
NLPPIPELINE
Commits
e73d5a70
Commit
e73d5a70
authored
1 year ago
by
Hafiz, Aqib (PG/T - Comp Sci & Elec Eng)
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ebb15888
%
pip
install
datasets
%
pip
install
transformers
%
pip
install
spacy
%
pip
install
torch
%
pip
install
spacy
-
transformers
%
pip
install
transformers
[
torch
]
%
pip
install
seqeval
from
datasets
import
load_dataset
,
load_metric
from
transformers
import
AutoTokenizer
,
AutoModelForTokenClassification
,
TrainingArguments
,
Trainer
,
EarlyStoppingCallback
from
transformers
import
DataCollatorForTokenClassification
import
numpy
as
np
from
seqeval.metrics
import
classification_report
,
f1_score
,
accuracy_score
import
matplotlib.pyplot
as
plt
import
seaborn
as
sns
import
torch
from
transformers
import
TrainingArguments
,
Trainer
,
EarlyStoppingCallback
import
numpy
as
np
# Function to load the dataset with a fallback option
def
load_dataset_with_fallback
(
dataset_name
,
fallback_name
):
try
:
dataset
=
load_dataset
(
dataset_name
,
download_mode
=
"
force_redownload
"
)
except
Exception
as
e
:
print
(
f
"
Error loading dataset
'
{
dataset_name
}
'
:
{
e
}
. Using
'
{
fallback_name
}
'
as a fallback.
"
)
dataset
=
load_dataset
(
fallback_name
,
download_mode
=
"
force_redownload
"
)
return
dataset
# Load the dataset with a fallback
dataset
=
load_dataset_with_fallback
(
"
surrey-nlp/PLOD-CW
"
,
"
conll2003
"
)
# Load the DistilBERT tokenizer and model
tokenizer
=
AutoTokenizer
.
from_pretrained
(
"
distilbert-base-uncased
"
)
model
=
AutoModelForTokenClassification
.
from_pretrained
(
"
distilbert-base-uncased
"
,
num_labels
=
4
)
# Extract the train, validation, and test datasets
short_dataset
=
dataset
[
"
train
"
]
val_dataset
=
dataset
[
"
validation
"
]
test_dataset
=
dataset
[
"
test
"
]
# Tokenize the input data
tokenized_input
=
tokenizer
(
short_dataset
[
"
tokens
"
],
is_split_into_words
=
True
)
# Example single sentence example
for
token
in
tokenized_input
[
"
input_ids
"
]:
print
(
tokenizer
.
convert_ids_to_tokens
(
token
))
break
# Define the label encoding
label_encoding
=
{
"
B-O
"
:
0
,
"
B-AC
"
:
1
,
"
B-LF
"
:
2
,
"
I-LF
"
:
3
}
# Create the label lists for train, validation, and test datasets
label_list
=
[[
label_encoding
.
get
(
tag
,
0
)
for
tag
in
sample
]
for
sample
in
short_dataset
[
"
ner_tags
"
]]
val_label_list
=
[[
label_encoding
.
get
(
tag
,
0
)
for
tag
in
sample
]
for
sample
in
val_dataset
[
"
ner_tags
"
]]
test_label_list
=
[[
label_encoding
.
get
(
tag
,
0
)
for
tag
in
sample
]
for
sample
in
test_dataset
[
"
ner_tags
"
]]
def
tokenize_and_align_labels
(
short_dataset
,
list_name
):
tokenized_inputs
=
tokenizer
(
short_dataset
[
"
tokens
"
],
truncation
=
True
,
is_split_into_words
=
True
)
labels
=
[]
for
i
,
label
in
enumerate
(
list_name
):
word_ids
=
tokenized_inputs
.
word_ids
(
batch_index
=
i
)
previous_word_idx
=
None
label_ids
=
[]
for
word_idx
in
word_ids
:
if
word_idx
is
None
:
label_ids
.
append
(
-
100
)
elif
word_idx
!=
previous_word_idx
:
label_ids
.
append
(
label
[
word_idx
])
else
:
label_ids
.
append
(
label
[
word_idx
])
previous_word_idx
=
word_idx
labels
.
append
(
label_ids
)
tokenized_inputs
[
"
labels
"
]
=
labels
return
tokenized_inputs
# Tokenize and align labels for train, validation, and test datasets
tokenized_datasets
=
tokenize_and_align_labels
(
short_dataset
,
label_list
)
tokenized_val_datasets
=
tokenize_and_align_labels
(
val_dataset
,
val_label_list
)
tokenized_test_datasets
=
tokenize_and_align_labels
(
test_dataset
,
test_label_list
)
# Convert dictionary of lists into a list of dictionaries for training
def
turn_dict_to_list_of_dict
(
d
):
new_list
=
[]
for
labels
,
inputs
in
zip
(
d
[
"
labels
"
],
d
[
"
input_ids
"
]):
entry
=
{
"
input_ids
"
:
inputs
,
"
labels
"
:
labels
}
new_list
.
append
(
entry
)
return
new_list
# Convert tokenized datasets
tokenised_train
=
turn_dict_to_list_of_dict
(
tokenized_datasets
)
tokenised_val
=
turn_dict_to_list_of_dict
(
tokenized_val_datasets
)
tokenised_test
=
turn_dict_to_list_of_dict
(
tokenized_test_datasets
)
# Load the DataCollator
data_collator
=
DataCollatorForTokenClassification
(
tokenizer
)
# Load the metric
metric
=
load_metric
(
"
seqeval
"
)
def
compute_metrics
(
p
):
predictions
,
labels
=
p
predictions
=
np
.
argmax
(
predictions
,
axis
=
2
)
true_predictions
=
[
[
label_list
[
p
]
for
(
p
,
l
)
in
zip
(
prediction
,
label
)
if
l
!=
-
100
]
for
prediction
,
label
in
zip
(
predictions
,
labels
)
]
true_labels
=
[
[
label_list
[
l
]
for
(
p
,
l
)
in
zip
(
prediction
,
label
)
if
l
!=
-
100
]
for
prediction
,
label
in
zip
(
predictions
,
labels
)
]
results
=
metric
.
compute
(
predictions
=
true_predictions
,
references
=
true_labels
)
return
{
"
precision
"
:
results
[
"
overall_precision
"
],
"
recall
"
:
results
[
"
overall_recall
"
],
"
f1
"
:
results
[
"
overall_f1
"
],
"
accuracy
"
:
results
[
"
overall_accuracy
"
],
}
# Define training arguments
model_name
=
"
distilbert-base-uncased
"
epochs
=
6
batch_size
=
4
learning_rate
=
2e-5
args
=
TrainingArguments
(
f
"
DistilALBERT-finetuned-NER
"
,
evaluation_strategy
=
"
steps
"
,
eval_steps
=
7000
,
save_total_limit
=
3
,
learning_rate
=
learning_rate
,
per_device_train_batch_size
=
batch_size
,
per_device_eval_batch_size
=
batch_size
,
num_train_epochs
=
epochs
,
weight_decay
=
0.001
,
save_steps
=
35000
,
metric_for_best_model
=
'
f1
'
,
load_best_model_at_end
=
True
)
# Create the Trainer
trainer
=
Trainer
(
model
,
args
,
train_dataset
=
tokenised_train
,
eval_dataset
=
tokenised_val
,
data_collator
=
data_collator
,
tokenizer
=
tokenizer
,
compute_metrics
=
compute_metrics
,
callbacks
=
[
EarlyStoppingCallback
(
early_stopping_patience
=
3
)]
)
# Train the model
trainer
.
train
()
# Prepare the test data for evaluation
predictions
,
labels
,
_
=
trainer
.
predict
(
tokenised_test
)
predictions
=
np
.
argmax
(
predictions
,
axis
=
2
)
# Remove the predictions for the special tokens
true_predictions
=
[
[
label_list
[
p
]
for
(
p
,
l
)
in
zip
(
prediction
,
label
)
if
l
!=
-
100
]
for
prediction
,
label
in
zip
(
predictions
,
labels
)
]
true_labels
=
[
[
label_list
[
l
]
for
(
p
,
l
)
in
zip
(
prediction
,
label
)
if
l
!=
-
100
]
for
prediction
,
label
in
zip
(
predictions
,
labels
)
]
# Compute the metrics on the test results
results
=
metric
.
compute
(
predictions
=
true_predictions
,
references
=
true_labels
)
results
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