roberta base with no year emb
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13172
dev-0/out.tsv
13172
dev-0/out.tsv
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@ -16,14 +16,14 @@ test_tokenized_datasets = test_dataset.map(tokenize_function, batched=True)
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train_dataset = tokenized_datasets["train"].shuffle(seed=42)
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eval_dataset_full = tokenized_datasets["test"]
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eval_dataset = tokenized_datasets["test"].select(range(2000))
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eval_dataset_small = tokenized_datasets["test"].select(range(2000))
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test_dataset = test_tokenized_datasets["train"]
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with open('train_dataset.pickle','wb') as f_p:
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pickle.dump(train_dataset, f_p)
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with open('eval_dataset.pickle','wb') as f_p:
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pickle.dump(eval_dataset, f_p)
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with open('eval_dataset_small.pickle','wb') as f_p:
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pickle.dump(eval_dataset_small, f_p)
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with open('eval_dataset_full.pickle','wb') as f_p:
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pickle.dump(eval_dataset_full, f_p)
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@ -4,8 +4,8 @@ from config import LABELS_LIST, MODEL
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with open('train_dataset.pickle','rb') as f_p:
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train_dataset = pickle.load(f_p)
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with open('eval_dataset.pickle','rb') as f_p:
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eval_dataset = pickle.load(f_p)
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with open('eval_dataset_small.pickle','rb') as f_p:
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eval_dataset_small = pickle.load(f_p)
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with open('eval_dataset_full.pickle','rb') as f_p:
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eval_dataset_full = pickle.load(f_p)
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@ -25,10 +25,15 @@ training_args = TrainingArguments("test_trainer",
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per_device_train_batch_size=4,
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per_device_eval_batch_size=4,
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evaluation_strategy='steps',
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eval_steps=2_000,
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gradient_accumulation_steps=10,
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#eval_steps=2_000,
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#save_steps=2_000,
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eval_steps=20_000,
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save_steps=20_000,
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num_train_epochs=1,
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gradient_accumulation_steps=2,
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learning_rate = 1e-6,
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warmup_steps=4_000,
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#warmup_steps=4_000,
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warmup_steps=4,
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load_best_model_at_end=True,
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)
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@ -49,10 +54,11 @@ trainer = Trainer(
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model=model,
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args=training_args,
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train_dataset=train_dataset,
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eval_dataset=eval_dataset,
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eval_dataset=eval_dataset_small,
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compute_metrics=compute_metrics,
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)
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#trainer.train(resume_from_checkpoint=True)
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trainer.train()
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trainer.save_model("./roberta-retrained")
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trainer.evaluate()
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@ -8,7 +8,7 @@ device = 'cpu'
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from transformers import AutoModelForSequenceClassification
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model = AutoModelForSequenceClassification.from_pretrained('test_trainer/checkpoint-82000/')
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model = AutoModelForSequenceClassification.from_pretrained('test_trainer/checkpoint-80/')
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tokenizer = AutoTokenizer.from_pretrained(MODEL)
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for dataset in ('dev-0', 'test-A'):
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13310
test-A/out.tsv
13310
test-A/out.tsv
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