From d4f07c5f8bf3d0b97f085c591b08a540c2efe0b8 Mon Sep 17 00:00:00 2001
From: Ludovic Moncla <ludovic.moncla@insa-lyon.fr>
Date: Mon, 20 Sep 2021 08:28:33 +0000
Subject: [PATCH] Update training_bertFineTuning.py
---
training_bertFineTuning.py | 22 +++++++++++-----------
1 file changed, 11 insertions(+), 11 deletions(-)
diff --git a/training_bertFineTuning.py b/training_bertFineTuning.py
index afd9e7d..6608e80 100644
--- a/training_bertFineTuning.py
+++ b/training_bertFineTuning.py
@@ -60,7 +60,7 @@ def format_time(elapsed):
return str(datetime.timedelta(seconds=elapsed_rounded))
-def training_bertFineTuning(chosen_model, sentences, labels, max_len, batch_size, epochs = 4):
+def training_bertFineTuning(chosen_model, model_path, sentences, labels, max_len, batch_size, epochs = 4):
# If there's a GPU available...
if torch.cuda.is_available():
@@ -82,12 +82,12 @@ def training_bertFineTuning(chosen_model, sentences, labels, max_len, batch_si
###########################################################################################################
- if chosen_model == 'bert-base-multilingual-cased' :
+ if chosen_model == 'bert' :
print('Loading Bert Tokenizer...')
- tokenizer = BertTokenizer.from_pretrained(chosen_model, do_lower_case=True)
- elif chosen_model == 'camembert-base':
+ tokenizer = BertTokenizer.from_pretrained(model_path, do_lower_case=True)
+ elif chosen_model == 'camembert':
print('Loading Camembert Tokenizer...')
- tokenizer = CamembertTokenizer.from_pretrained(chosen_model , do_lower_case=True)
+ tokenizer = CamembertTokenizer.from_pretrained(model_path , do_lower_case=True)
@@ -192,18 +192,18 @@ def training_bertFineTuning(chosen_model, sentences, labels, max_len, batch_si
# Load BertForSequenceClassification, the pretrained BERT model with a single
# linear classification layer on top.
- if chosen_model == 'bert-base-multilingual-cased':
+ if chosen_model == 'bert':
model = BertForSequenceClassification.from_pretrained(
- chosen_model, # Use the 12-layer BERT model, with an uncased vocab.
+ model_path, # Use the 12-layer BERT model, with an uncased vocab.
num_labels = numberOfClasses, # The number of output labels--2 for binary classification.
# You can increase this for multi-class tasks.
output_attentions = False, # Whether the model returns attentions weights.
output_hidden_states = False, # Whether the model returns all hidden-states.
)
- elif chosen_model == 'camembert-base':
+ elif chosen_model == 'camembert':
model = CamembertForSequenceClassification.from_pretrained(
- chosen_model, # Use the 12-layer BERT model, with an uncased vocab.
+ model_path, # Use the 12-layer BERT model, with an uncased vocab.
num_labels = numberOfClasses, # The number of output labels--2 for binary classification.
# You can increase this for multi-class tasks.
output_attentions = False, # Whether the model returns attentions weights.
@@ -456,7 +456,7 @@ if __name__ == "__main__":
minOfInstancePerClass = int(config.get('general','minOfInstancePerClass'))
maxOfInstancePerClass = int(config.get('general','maxOfInstancePerClass'))
- chosen_tokeniser = config.get('model','tokeniser')
+ model_path = config.get('model','path')
chosen_model = config.get('model','model')
max_len = int(config.get('model','max_len_sequences'))
@@ -484,7 +484,7 @@ if __name__ == "__main__":
#call train method
- model = training_bertFineTuning(chosen_model, sentences, labels, max_len, batch_size, epochs)
+ model = training_bertFineTuning(chosen_model,model_path, sentences, labels, max_len, batch_size, epochs)
#save the model
--
GitLab