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.gitignore
vendored
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.gitignore
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@ -10,3 +10,4 @@ mlruns
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results
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logs
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.idea
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bert*
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45
bert.py
45
bert.py
@ -1,5 +1,5 @@
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import torch
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from transformers.file_utils import is_tf_available, is_torch_available, is_torch_tpu_available
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from transformers.file_utils import is_tf_available, is_torch_available
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from transformers import BertTokenizerFast, BertForSequenceClassification
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from transformers import Trainer, TrainingArguments
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import numpy as np
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@ -46,18 +46,18 @@ def compute_metrics(pred):
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def get_prediction(text):
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inputs = tokenizer(text, padding=True, truncation=True, max_length=max_length, return_tensors="pt").to("cuda")
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outputs = model(**inputs)
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probs = outputs[0].softmax(1)
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return probs.argmax()
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return outputs[0].softmax(1).argmax()
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set_seed(1)
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SAMPLES = 2000
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train_texts = \
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pd.read_csv('train/in.tsv.xz', compression='xz', sep='\t', header=None, error_bad_lines=False, quoting=3)[0].tolist()
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train_labels = pd.read_csv('train/expected.tsv', sep='\t', header=None, quoting=3)[0].tolist()
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pd.read_csv('train/in.tsv.xz', compression='xz',
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sep='\t', header=None, error_bad_lines=False, quoting=3)[0][:SAMPLES].tolist()
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train_labels = pd.read_csv('train/expected.tsv', sep='\t', header=None, quoting=3)[0][:SAMPLES].tolist()
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dev_texts = pd.read_csv('dev-0/in.tsv.xz', compression='xz', sep='\t', header=None, quoting=3)[0].tolist()
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dev_labels = pd.read_csv('dev-0/expected.tsv', sep='\t', header=None, quoting=3)[0].tolist()
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# test_texts = pd.read_table('test-A/in.tsv.xz', compression='xz', sep='\t', header=None, quoting=3)
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model_name = "bert-base-uncased"
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max_length = 512
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@ -73,31 +73,30 @@ model = BertForSequenceClassification.from_pretrained(
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model_name, num_labels=len(pd.unique(train_labels))).to("cuda")
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training_args = TrainingArguments(
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output_dir='./results', # output directory
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num_train_epochs=3, # total number of training epochs
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per_device_train_batch_size=1, # batch size per device during training
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per_device_eval_batch_size=1, # batch size for evaluation
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warmup_steps=500, # number of warmup steps for learning rate scheduler
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weight_decay=0.01, # strength of weight decay
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logging_dir='./logs', # directory for storing logs
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load_best_model_at_end=True, # load the best model when finished training (default metric is loss)
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# but you can specify `metric_for_best_model` argument to change to accuracy or other metric
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logging_steps=200, # log & save weights each logging_steps
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evaluation_strategy="steps", # evaluate each `logging_steps`
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output_dir='./results',
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num_train_epochs=1,
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per_device_train_batch_size=4,
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per_device_eval_batch_size=4,
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warmup_steps=500,
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weight_decay=0.005,
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logging_dir='./logs',
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load_best_model_at_end=True,
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logging_steps=250,
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evaluation_strategy="steps",
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)
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trainer = Trainer(
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model=model, # the instantiated Transformers model to be trained
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args=training_args, # training arguments, defined above
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train_dataset=train_dataset, # training dataset
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eval_dataset=valid_dataset, # evaluation dataset
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compute_metrics=compute_metrics, # the callback that computes metrics of interest
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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=valid_dataset,
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compute_metrics=compute_metrics,
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)
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trainer.train()
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trainer.evaluate()
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model_path = "bert-base-uncased"
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model_path = "bert-base-uncased-2k"
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model.save_pretrained(model_path)
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tokenizer.save_pretrained(model_path)
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35
bert_infer.py
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35
bert_infer.py
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import pandas as pd
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from transformers import BertForSequenceClassification, BertTokenizerFast
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model_path = "bert-base-uncased-2k"
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max_length = 512
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DEV = 'dev-0'
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TEST = 'test-A'
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model = BertForSequenceClassification.from_pretrained(model_path, num_labels=2).to("cuda")
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tokenizer = BertTokenizerFast.from_pretrained(model_path)
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def get_prediction(text):
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inputs = tokenizer(text, padding=True, truncation=True, max_length=max_length, return_tensors="pt").to("cuda")
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outputs = model(**inputs)
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return outputs[0].softmax(1).argmax()
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def get_predictions_for(dataset):
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test = pd.read_csv(f'{dataset}/in.tsv.xz', compression='xz', sep='\t',
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error_bad_lines=False, header=None, quoting=3)[0].tolist()
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test_infers = []
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for row in test:
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test_infers.append(get_prediction(row))
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with open(f'{dataset}/out.tsv', 'w') as file:
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for infer in test_infers:
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file.write(str(infer.item()) + '\n')
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get_predictions_for(DEV)
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get_predictions_for(TEST)
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dev-0/out.tsv
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dev-0/out.tsv
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5152
test-A/out.tsv
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5152
test-A/out.tsv
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