roberta_year_text_only_day

roberta_year_text_only_day/01_create_datasets.py

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+import datetime
+from config import LABELS_DICT
+
+with open('../test-A/in.tsv','r') as f_in, 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')
+

roberta_year_text_only_day/04_predict.py

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+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')
+

roberta_year_text_only_day/config.py

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+LABELS_DICT = {'positive':0,
+        'negative':1}
+
+
+LABELS_LIST = ['positive',
+        'negative']
+
+MODEL = 'roberta-base'

roberta_year_text_only_day/run.sh

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+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