roberta_year_as_text_better_finetunning
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dev-0/out.tsv
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dev-0/out.tsv
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@ -21,7 +21,7 @@ model = AutoModelForSequenceClassification.from_pretrained('roberta-ireland').cu
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from transformers import TrainingArguments
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from transformers import TrainingArguments
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training_args = TrainingArguments("test_trainer",
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training_args = TrainingArguments("roberta-ireland",
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per_device_train_batch_size=4,
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per_device_train_batch_size=4,
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per_device_eval_batch_size=4,
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per_device_eval_batch_size=4,
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evaluation_strategy='steps',
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evaluation_strategy='steps',
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@ -1 +1 @@
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python run_glue.py --model_name_or_path roberta-base --train_file /media/kuba/ssdsam/gonito/ireland-news/train/huggingface_format_year.csv --validation_file /media/kuba/ssdsam/gonito/ireland-news/dev-0/huggingface_format_year.csv --do_train --do_eval --max_seq_length 64 --per_device_train_batch_size 32 --learning_rate 2e-5 --num_train_epochs 3 --save_steps=5000 --eval_steps=5000 --evaluation_strategy steps --output_dir ./roberta-ireland
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python run_glue.py --model_name_or_path roberta-base --train_file /media/kuba/ssdsam/gonito/ireland-news/train/huggingface_format_year_as_text.csv --validation_file /media/kuba/ssdsam/gonito/ireland-news/dev-0/huggingface_format_year_as_text.csv --do_train --do_eval --max_seq_length 64 --per_device_train_batch_size 32 --learning_rate 2e-5 --num_train_epochs 3 --save_steps=5000 --eval_steps=5000 --evaluation_strategy steps --output_dir ./roberta-ireland
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25842
test-A/out.tsv
25842
test-A/out.tsv
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