From 9517173bdfe9a1262eadf685854384f5979586dd Mon Sep 17 00:00:00 2001
From: Caroline DE POURTALES <caroline.de-pourtales@irit.fr>
Date: Mon, 27 Mar 2023 15:13:14 +0200
Subject: [PATCH] change for flaubert
---
SuperTagger/SuperTagger/SuperTagger.py | 10 +++++++---
predict_links.py | 2 +-
train_neuralproofnet.py | 4 ++--
train_supertagger.py | 4 ++--
utils.py | 9 +++++++++
5 files changed, 21 insertions(+), 8 deletions(-)
diff --git a/SuperTagger/SuperTagger/SuperTagger.py b/SuperTagger/SuperTagger/SuperTagger.py
index 4f405a0..670849d 100644
--- a/SuperTagger/SuperTagger/SuperTagger.py
+++ b/SuperTagger/SuperTagger/SuperTagger.py
@@ -98,7 +98,9 @@ class SuperTagger:
self.num_label = len(self.index_to_tags)
self.model = Tagging_bert_model(self.bert_name, self.num_label)
self.tags_tokenizer = SymbolTokenizer(self.index_to_tags)
- self.sent_tokenizer = SentencesTokenizer(transformers.AutoTokenizer.from_pretrained(self.bert_name,do_lower_case=True),
+ self.sent_tokenizer = SentencesTokenizer(transformers.AutoTokenizer.from_pretrained(
+ self.bert_name,
+ do_lower_case=True),
self.max_len_sentence)
self.model.load_state_dict(params['state_dict'])
self.optimizer = params['optimizer']
@@ -138,9 +140,11 @@ class SuperTagger:
self.index_to_tags = index_to_tags
self.bert_name = bert_name
- self.sent_tokenizer = SentencesTokenizer(AutoTokenizer.from_pretrained(bert_name,do_lower_case=True),
+ self.sent_tokenizer = SentencesTokenizer(AutoTokenizer.from_pretrained(
+ bert_name,
+ do_lower_case=True),
self.max_len_sentence)
- self.optimizer = Adam(params=self.model.parameters(), lr=2e-4, eps=1e-8)
+ self.optimizer = Adam(params=self.model.parameters(), lr=1e-3, eps=1e-8)
self.tags_tokenizer = SymbolTokenizer(index_to_tags)
self.trainable = True
self.model_load = True
diff --git a/predict_links.py b/predict_links.py
index b52982a..28336ea 100644
--- a/predict_links.py
+++ b/predict_links.py
@@ -13,7 +13,7 @@ if __name__== '__main__':
# region model
model_tagger = "models/flaubert_super_98_V2_50e.pt"
neuralproofnet = NeuralProofNet(model_tagger)
- model = "Output/saved_linker.pt"
+ model = "models/saved_linker.pt"
neuralproofnet.linker.load_weights(model)
# endregion
diff --git a/train_neuralproofnet.py b/train_neuralproofnet.py
index f3c2284..dd39d91 100644
--- a/train_neuralproofnet.py
+++ b/train_neuralproofnet.py
@@ -7,7 +7,7 @@ torch.cuda.empty_cache()
# region data
file_path_axiom_links = 'Datasets/gold_dataset_links.csv'
-df_axiom_links = read_links_csv(file_path_axiom_links)[:32]
+df_axiom_links = read_links_csv(file_path_axiom_links)
# endregion
@@ -16,7 +16,7 @@ print("#" * 20)
print("#" * 20)
model_tagger = "models/flaubert_super_98_V2_50e.pt"
neural_proof_net = NeuralProofNet(model_tagger)
-neural_proof_net.train_neuralproofnet(df_axiom_links, validation_rate=0, epochs=5, pretrain_linker_epochs=5, batch_size=16,
+neural_proof_net.train_neuralproofnet(df_axiom_links, validation_rate=0.1, epochs=25, pretrain_linker_epochs=25, batch_size=16,
checkpoint=True, tensorboard=True)
print("#" * 20)
print("#" * 20)
diff --git a/train_supertagger.py b/train_supertagger.py
index 2ea2aee..571edc6 100644
--- a/train_supertagger.py
+++ b/train_supertagger.py
@@ -15,10 +15,10 @@ index_to_super = load_obj('SuperTagger/Datasets/index_to_super')
# region model
tagger = SuperTagger()
-tagger.create_new_model(len(index_to_super),'camembert-base',index_to_super)
+tagger.create_new_model(len(index_to_super),"flaubert/flaubert_base_cased",index_to_super)
## If you want to upload a pretrained model
# tagger.load_weights("models/model_check.pt")
-tagger.train(texts, tags, epochs=40, batch_size=16, validation_rate=0.1,
+tagger.train(texts, tags, epochs=60, batch_size=16, validation_rate=0.1,
tensorboard=True, checkpoint=True)
# endregion
diff --git a/utils.py b/utils.py
index 9640a0b..b1c9f85 100644
--- a/utils.py
+++ b/utils.py
@@ -20,8 +20,12 @@ def read_links_csv(csv_path, nrows=float('inf'), chunksize=100):
print("\n" + "#" * 20)
print("Loading csv...")
+ rows = sum(1 for _ in open(csv_path, 'r', encoding="utf8")) - 1 # minus the header
chunk_list = []
+ if rows > nrows:
+ rows = nrows
+
with tqdm(total=nrows, desc='Rows read: ') as bar:
for chunk in pd.read_csv(csv_path, header=0, converters={'Y': pd.eval, 'Z': pd.eval},
chunksize=chunksize, nrows=nrows):
@@ -45,7 +49,12 @@ def read_supertags_csv(csv_path, nrows=float('inf'), chunksize=100):
print("\n" + "#" * 20)
print("Loading csv...")
+ rows = sum(1 for _ in open(csv_path, 'r', encoding="utf8")) - 1 # minus the header
chunk_list = []
+
+ if rows > nrows:
+ rows = nrows
+
with tqdm(total=nrows, desc='Rows read: ') as bar:
for chunk in pd.read_csv(csv_path, header=0, converters={'Y1': pd.eval, 'Y2': pd.eval, 'Z': pd.eval},
chunksize=chunksize, nrows=nrows):
--
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