From 7bf8383faab67eeadd342a9020588e46e1c45e0a Mon Sep 17 00:00:00 2001
From: JulianFP <julian@partanengroup.de>
Date: Thu, 27 Mar 2025 18:17:09 +0000
Subject: [PATCH] Apply finetuning config made previously by kupper to MLM
entity as well
---
src/experiments/finetune_T5/finetune_T5_MLM_entity.py | 6 +++---
src/models/T5_MLM_entity.py | 6 +++---
2 files changed, 6 insertions(+), 6 deletions(-)
diff --git a/src/experiments/finetune_T5/finetune_T5_MLM_entity.py b/src/experiments/finetune_T5/finetune_T5_MLM_entity.py
index c7ac094..3366c8e 100644
--- a/src/experiments/finetune_T5/finetune_T5_MLM_entity.py
+++ b/src/experiments/finetune_T5/finetune_T5_MLM_entity.py
@@ -3,7 +3,7 @@ from src.models.T5_MLM_entity import finetune_model, set_label_dict
def finetune_t5(dataset):
print("start")
- annotated_sentences = data_manager.get_annotated_sentences(dataset, 1000)
+ annotated_sentences = data_manager.get_annotated_sentences(dataset, 2000, train_test_split=0.5, train=True)
labels = data_manager.get_labels(dataset)
sentences = []
@@ -23,11 +23,11 @@ def finetune_t5(dataset):
for i in range(min(len(sentences), 50)):
print(f"sentence: {sentences[i]}, entity: {entities[i]}, label: {labels[i]}")
- epochs = 20
+ epochs = 150
label_dict = data_manager.get_label_dict(dataset, 1000)
set_label_dict(label_dict)
finetune_model(sentences, entities, labels, output_dir=f"./src/models/t5_mlm_entity_finetuned_model/pretrained_{dataset}_epoch{epochs}", epochs=epochs)
-finetune_t5("CoNLL")
+finetune_t5("FIGER-coarse")
diff --git a/src/models/T5_MLM_entity.py b/src/models/T5_MLM_entity.py
index f9604cb..f07add7 100644
--- a/src/models/T5_MLM_entity.py
+++ b/src/models/T5_MLM_entity.py
@@ -5,7 +5,7 @@ from torch.nn.functional import softmax
from transformers import T5ForConditionalGeneration, T5Tokenizer, Trainer, TrainingArguments, DataCollatorForSeq2Seq
from datasets import Dataset, DatasetDict
-model_name = "google-t5/t5-base"
+model_name = "google-t5/t5-large"
print("Loading model: T5 MLM entity")
tokenizer = T5Tokenizer.from_pretrained(model_name)
@@ -86,14 +86,14 @@ def finetune_model(sentences, entities, labels, output_dir, epochs=10):
dataset.set_format(type="torch", columns=["input_ids", "attention_mask", "labels"])
training_args = TrainingArguments(
- output_dir="./src/models/t5_nli_finetuned_model",
+ output_dir="./src/models/t5_nli_finetuned_model/checkpoints/",
eval_strategy="epoch",
learning_rate=5e-5,
per_device_train_batch_size=8,
per_device_eval_batch_size=8,
num_train_epochs=epochs,
weight_decay=0.01,
- save_strategy="no",
+ save_strategy="epoch",
push_to_hub=False,
logging_dir="./logs",
)
--
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