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open(f'./test-A_huggingface_format_year_as_text.csv', 'w') as f_hf: + #f_hf.write('text\tyear_cont\tdate\tday_of_year\tday_of_month\tmonth\tyear\tweekday\tlabel\n') + f_hf.write('text\n') + for line_in in f_in: + year_cont, date, text = line_in.rstrip('\n').split('\t') + d = datetime.datetime.strptime(date,"%Y%m%d") + day_of_year = str(d.timetuple().tm_yday) + day_of_month = str(d.day) + month = str(d.month) + year = str(d.year) + weekday = str(d.weekday()) + day_of_year = str(d.timetuple().tm_yday) + #text = 'year: ' + year + ' month: ' + month + ' day: ' + day_of_month + ' weekday: ' + weekday + ' ' + text + text = 'day: ' + day_of_month + ' ' + text + #f_hf.write(text +'\t' +year_cont +'\t'+ date + '\t' + day_of_year + '\t' + day_of_month + '\t' + month + '\t' + year + '\t' + weekday + '\t' + str('0') + '\n') + f_hf.write(text + '\n') + + +for dataset in 'train', 'dev-0': + with open(f'../{dataset}/in.tsv') as f_in, open(f'../{dataset}/expected.tsv') as f_exp, open(f'./{dataset}_huggingface_format_year_as_text.csv','w') as f_hf: + #f_hf.write('text\tyear_cont\tdate\tday_of_year\tday_of_month\tmonth\tyear\tweekday\tlabel\n') + f_hf.write('text\tlabel\n') + for line_in, line_exp in zip(f_in, f_exp): + label = str(LABELS_DICT[line_exp.rstrip('\n')]) + year_cont,date,text = line_in.rstrip('\n').split('\t') + d = datetime.datetime.strptime(date,"%Y%m%d") + day_of_year = str(d.timetuple().tm_yday) + day_of_month = str(d.day) + month = str(d.month) + year = str(d.year) + weekday = str(d.weekday()) + day_of_year = str(d.timetuple().tm_yday) + #text = 'year: ' + year + ' month: ' + month + ' day: ' + day_of_month + ' weekday: ' + weekday + ' ' + text + text = 'day: ' + day_of_month + ' ' + text + #f_hf.write(text +'\t' +year_cont +'\t'+ date + '\t'+ day_of_year + '\t' + day_of_month + '\t' + month + '\t' + year + '\t' + weekday + '\t' + label + '\n') + f_hf.write(text + '\t' + label + '\n') + diff --git a/roberta_year_text_only_day/04_predict.py b/roberta_year_text_only_day/04_predict.py new file mode 100644 index 0000000..963fe5e --- /dev/null +++ b/roberta_year_text_only_day/04_predict.py @@ -0,0 +1,24 @@ +import pickle +from config import LABELS_LIST, MODEL +from transformers import AutoTokenizer +from tqdm import tqdm + +device = 'cuda' +model_path= './roberta-ireland' + +from transformers import AutoModelForSequenceClassification + +model = AutoModelForSequenceClassification.from_pretrained(model_path).cuda() +tokenizer = AutoTokenizer.from_pretrained(MODEL) + +for dataset in ('dev-0', 'test-A'): + with open(f'./{dataset}_huggingface_format_year_as_text.csv') as f_in, open(f'../{dataset}/out.tsv','w') as f_out: + next(f_in) + for line_in in tqdm(f_in, total=150_000): + text = line_in.rstrip('\n') + inputs = tokenizer(text, padding=True, truncation=True, return_tensors="pt").to(device) + outputs = model(**inputs) + probs = outputs[0].softmax(1) + prediction = LABELS_LIST[probs.argmax(1)] + f_out.write(prediction + '\n') + diff --git a/roberta_year_text_only_day/config.py b/roberta_year_text_only_day/config.py new file mode 100644 index 0000000..6992e1b --- /dev/null +++ b/roberta_year_text_only_day/config.py @@ -0,0 +1,8 @@ +LABELS_DICT = {'positive':0, + 'negative':1} + + +LABELS_LIST = ['positive', + 'negative'] + +MODEL = 'roberta-base' diff --git a/roberta_year_text_only_day/run.sh b/roberta_year_text_only_day/run.sh new file mode 100644 index 0000000..220cadf --- /dev/null +++ b/roberta_year_text_only_day/run.sh @@ -0,0 +1,12 @@ +python run_glue.py --model_name_or_path roberta-base \ + --train_file ./train_huggingface_format_year_as_text.csv \ + --validation_file ./dev-0_huggingface_format_year_as_text.csv \ + --do_train \ + --max_seq_length 64 \ + --per_device_train_batch_size 32 \ + --learning_rate 2e-5 \ + --num_train_epochs 3 \ + --output_dir ./roberta-ireland \ + --save_steps=10000 \ + --eval_steps=10000 \ + --evaluation_strategy steps diff --git a/test-A/out.tsv b/test-A/out.tsv index 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