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4 Commits

Author SHA1 Message Date
Jakub Pokrywka
4e6623c285 hf roberta large 2022-07-20 19:57:45 +00:00
jakubpokrywka@gmail.com
5e021fb1fd hf_roberta_large 2022-07-11 16:32:46 +00:00
Jakub Pokrywka
9af4dd453e hf roberta base regression layer on top 2022-07-04 18:31:40 +00:00
kubapok
33e11dad3d hf_roberta_base 2022-07-02 12:02:13 +02:00
14 changed files with 38851 additions and 38215 deletions

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for split in 'train', 'dev-0':
with open(f'../{split}/in.tsv') as f_in, open(f'../{split}/expected.tsv') as f_exp, open(f'./{split}_huggingface_format.csv', 'w') as f_hf:
f_hf.write('year_start_float\tyear_end_float\tyear_middle_float\tyear_middle_int\ttext\n')
for line_in, line_exp in zip(f_in, f_exp):
year_start_float, year_end_float = line_exp.rstrip().split(',')
year_middle_float = (float(year_start_float) + float(year_end_float)) / 2
year_middle_int = round(year_middle_float)
f_hf.write(f'{year_start_float}\t{year_end_float}\t{year_middle_float}\t{year_middle_int}\t{line_in}')
for split in ('test-A',):
with open(f'../{split}/in.tsv') as f_in, open(f'./{split}_huggingface_format.csv', 'w') as f_hf:
f_hf.write('year_start_float\tyear_end_float\tyear_middle_float\tyear_middle_int\ttext\n')
for line_in in f_in:
expected = '0.0\t0.0\t0.0\t0'
f_hf.write(expected + '\t' + line_in)

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from config import MODEL, TEST
import pickle
from datasets import load_dataset, Dataset
from transformers import AutoTokenizer
from tqdm import tqdm
from sklearn.preprocessing import MinMaxScaler
import numpy as np
values = ('year_start_float', 'year_end_float', 'year_middle_float', 'year_middle_int', 'text')
tokenizer = AutoTokenizer.from_pretrained(MODEL)
def tokenize_function(examples):
t = tokenizer(examples["text"], padding="max_length", truncation=True, max_length=512)
return t
def get_dataset_dict(dataset):
with open(dataset) as f_in:
next(f_in)
d = dict()
for v in values:
d[v] = list()
for l in f_in:
args = l.rstrip().split('\t')
for v, a in zip(values, args):
d[v].append(a)
return d
train_dataset = Dataset.from_dict(get_dataset_dict('train_huggingface_format.csv')).map(tokenize_function, batched=True).shuffle(seed=42)
eval_dataset_full = Dataset.from_dict(get_dataset_dict('dev-0_huggingface_format.csv')).map(tokenize_function, batched=True)
eval_dataset_small = eval_dataset_full.shuffle(seed=42).select(range(2000))
test_dataset_A = Dataset.from_dict(get_dataset_dict('test-A_huggingface_format.csv')).map(tokenize_function, batched=True)
if TEST:
train_dataset = train_dataset.select(range(500))
eval_dataset_full = eval_dataset_full.select(range(400))
eval_dataset_small = eval_dataset_small.select(range(50))
test_dataset_A = test_dataset_A.select(range(200))
scalers = dict()
values_to_scale = ('year_start_float', 'year_end_float', 'year_middle_float')
for v in values_to_scale:
scalers[v] = MinMaxScaler().fit(np.array(train_dataset[v]).reshape(-1, 1))
def add_scaled(example):
for factor in values_to_scale:
example[factor + '_scaled'] = scalers[factor].transform(np.array(example[factor]).reshape(-1,1)).reshape(1,-1)[0].item()
return example
train_dataset = train_dataset.map(add_scaled)
eval_dataset_full = eval_dataset_full.map(add_scaled)
eval_dataset_small = eval_dataset_small.map(add_scaled)
test_dataset_A = test_dataset_A.map(add_scaled)
with open('train_dataset.pickle', 'wb') as f_p:
pickle.dump(train_dataset, f_p)
with open('eval_dataset_small.pickle', 'wb') as f_p:
pickle.dump(eval_dataset_small, f_p)
with open('eval_dataset_full.pickle', 'wb') as f_p:
pickle.dump(eval_dataset_full, f_p)
with open('test_dataset_A.pickle', 'wb') as f_p:
pickle.dump(test_dataset_A, f_p)
with open('scalers.pickle', 'wb') as f_p:
pickle.dump(scalers, f_p)

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from config import MODEL, TEST
import pickle
from datasets import load_dataset
from transformers import AutoTokenizer, RobertaModel, RobertaTokenizer
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification
from transformers import AdamW
from transformers import get_scheduler
import torch
from tqdm.auto import tqdm
BATCH_SIZE = 24
EARLY_STOPPING = 3
WARMUP_STEPS = 10_000
STEPS_EVAL = 5_000
if TEST:
STEPS_EVAL = 100
WARMUP_STEPS = 10
with open('train_dataset.pickle', 'rb') as f_p:
train_dataset = pickle.load(f_p)
with open('eval_dataset_small.pickle', 'rb') as f_p:
eval_dataset_small = pickle.load(f_p)
with open('eval_dataset_full.pickle', 'rb') as f_p:
eval_dataset_full = pickle.load(f_p)
train_dataloader = DataLoader(train_dataset, shuffle=True, batch_size=BATCH_SIZE)
eval_dataloader_small = DataLoader(eval_dataset_small, batch_size=BATCH_SIZE)
eval_dataloader_full = DataLoader(eval_dataset_full, batch_size=BATCH_SIZE)
model = AutoModelForSequenceClassification.from_pretrained(MODEL, num_labels=1)
optimizer = AdamW(model.parameters(), lr=1e-6)
num_epochs = 15
num_training_steps = num_epochs * len(train_dataloader)
lr_scheduler = get_scheduler(
"linear",
optimizer=optimizer,
num_warmup_steps=WARMUP_STEPS,
num_training_steps=num_training_steps
)
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model.to(device)
progress_bar = tqdm(range(num_training_steps))
model.train()
model.train()
model.to(device)
def transform_batch(batch):
batch['input_ids'] = torch.stack(batch['input_ids']).permute(1,0).to(device)
batch['attention_mask'] = torch.stack(batch['attention_mask']).permute(1,0).to(device)
batch['labels'] = batch['year_middle_float_scaled'].to(device).float()
batch['labels'].to(device)
batch['input_ids'].to(device)
batch['attention_mask'].to(device)
for c in set(batch.keys()) - {'input_ids', 'attention_mask', 'labels'}:
del batch[c]
return batch
def eval(full=False):
model.eval()
eval_loss = 0.0
dataloader = eval_dataloader_full if full else eval_dataloader_small
for i, batch in enumerate(dataloader):
batch = transform_batch(batch)
outputs = model(**batch)
loss = outputs.loss
eval_loss += loss.item()
print(f'epoch {epoch} eval loss: {eval_loss / i }')
model.train()
return eval_loss
best_eval_loss = 9999
epochs_without_progress = 0
for epoch in range(num_epochs):
train_loss = 0.0
for i, batch in enumerate(train_dataloader):
batch = transform_batch(batch)
outputs = model(**batch)
loss = outputs.loss
loss.backward()
train_loss += loss.item()
progress_bar.update(1)
# DELAYED UPDATE
#if i % 16 == 1 and i > 1:
# optimizer.step()
# #lr_scheduler.step()
# optimizer.zero_grad()
# DELAYED UPDATE
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
if i % STEPS_EVAL == 0 and i > 1:
print(f' epoch {epoch} train loss: {train_loss / STEPS_EVAL }', end='\t\t')
train_loss = 0.0
eval(full = False)
model.save_pretrained(f'roberta_year_prediction/epoch_{epoch}')
eval_loss = eval(full=True)
if eval_loss < best_eval_loss:
model.save_pretrained(f'roberta_year_prediction/epoch_best')
best_eval_loss = eval_loss
else:
epochs_without_progress += 1
if epochs_without_progress > EARLY_STOPPING:
break

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import pickle
import torch
from transformers import AutoModelForSequenceClassification
from torch.utils.data import DataLoader
from tqdm.auto import tqdm
with open('eval_dataset_full.pickle','rb') as f_p:
eval_dataset_full = pickle.load(f_p)
with open('test_dataset_A.pickle','rb') as f_p:
test_dataset = pickle.load(f_p)
device = 'cuda'
model = AutoModelForSequenceClassification.from_pretrained('./roberta_year_prediction/epoch_best')
model.eval()
model.to(device)
with open('scalers.pickle', 'rb') as f_scaler:
scalers = pickle.load(f_scaler)
def predict(dataset, out_f):
eval_dataloader = DataLoader(dataset, batch_size=1)
outputs = []
progress_bar = tqdm(range(len(eval_dataloader)))
for batch in eval_dataloader:
batch['input_ids'] = torch.stack(batch['input_ids']).permute(1,0).to(device)
batch['attention_mask'] = torch.stack(batch['attention_mask']).permute(1,0).to(device)
batch['labels'] = batch['year_middle_float_scaled'].to(device).float()
batch['labels'].to(device)
batch['input_ids'].to(device)
batch['attention_mask'].to(device)
for c in set(batch.keys()) - {'input_ids', 'attention_mask', 'labels'}:
del batch[c]
outputs.extend(model(**batch).logits.tolist())
progress_bar.update(1)
outputs_transformed = scalers['year_middle_float'].inverse_transform(outputs)
with open(out_f,'w') as f_out:
for o in outputs_transformed:
f_out.write(str(o[0]) + '\n')
predict(eval_dataset_full, '../dev-0/out.tsv')
predict(test_dataset, '../test-A/out.tsv')

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import pickle
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from torch.utils.data import DataLoader
from tqdm.auto import tqdm
#with open('train_dataset.pickle','rb') as f_p:
# train_dataset = pickle.load(f_p)
#
#with open('eval_dataset_small.pickle','rb') as f_p:
# eval_dataset_small = pickle.load(f_p)
#
#with open('eval_dataset_full.pickle','rb') as f_p:
# eval_dataset_full = pickle.load(f_p)
#
#with open('test_dataset_A.pickle','rb') as f_p:
# test_dataset_A = pickle.load(f_p)
with open('dev-0_huggingface_format.csv','r') as f_p:
eval_dataset_full = f_p.readlines()
with open('test-A_huggingface_format.csv','r') as f_p:
test_dataset = f_p.readlines()
device = 'cuda'
model = AutoModelForSequenceClassification.from_pretrained('./roberta_year_prediction/epoch_best')
tokenizer = AutoTokenizer.from_pretrained('roberta-base')
model.eval()
model.to(device)
with open('scalers.pickle', 'rb') as f_scaler:
scalers = pickle.load(f_scaler)
tokenizer = AutoTokenizer.from_pretrained('roberta-base')
def predict(dataset, out_f):
outputs = []
for sample in tqdm(dataset[1:]):
y, t = sample.split('\t')
t = t.rstrip()
t = tokenizer(t, padding="max_length", truncation=True, max_length=512, return_tensors='pt').to('cuda')
outputs.extend(model(**t).logits.tolist())
outputs_transformed = scalers['year'].inverse_transform(outputs)
with open(out_f,'w') as f_out:
for o in outputs_transformed:
f_out.write(str(o[0]) + '\n')
predict(eval_dataset_full, '../dev-0/out.tsv')
predict(test_dataset, '../test-A/out.tsv')

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#MODEL = '/home/wmi/RoBERTa/without_date/checkpoint-1325000'
MODEL = 'roberta-base'
TEST=False

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for split in 'train', 'dev-0':
with open(f'../{split}/in.tsv') as f_in, open(f'../{split}/expected.tsv') as f_exp, open(f'./{split}_huggingface_format.csv', 'w') as f_hf:
f_hf.write('year_start_float\tyear_end_float\tyear_middle_float\tyear_middle_int\ttext\n')
for line_in, line_exp in zip(f_in, f_exp):
year_start_float, year_end_float = line_exp.rstrip().split(',')
year_middle_float = (float(year_start_float) + float(year_end_float)) / 2
year_middle_int = round(year_middle_float)
f_hf.write(f'{year_start_float}\t{year_end_float}\t{year_middle_float}\t{year_middle_int}\t{line_in}')
for split in ('test-A',):
with open(f'../{split}/in.tsv') as f_in, open(f'./{split}_huggingface_format.csv', 'w') as f_hf:
f_hf.write('year_start_float\tyear_end_float\tyear_middle_float\tyear_middle_int\ttext\n')
for line_in in f_in:
expected = '0.0\t0.0\t0.0\t0'
f_hf.write(expected + '\t' + line_in)

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from config import MODEL, TEST
import pickle
from datasets import load_dataset, Dataset
from transformers import AutoTokenizer
from tqdm import tqdm
from sklearn.preprocessing import MinMaxScaler
import numpy as np
values = ('year_start_float', 'year_end_float', 'year_middle_float', 'year_middle_int', 'text')
tokenizer = AutoTokenizer.from_pretrained(MODEL)
def tokenize_function(examples):
t = tokenizer(examples["text"], padding="max_length", truncation=True, max_length=512)
return t
def get_dataset_dict(dataset):
with open(dataset) as f_in:
next(f_in)
d = dict()
for v in values:
d[v] = list()
for l in f_in:
args = l.rstrip().split('\t')
for v, a in zip(values, args):
d[v].append(a)
return d
train_dataset = Dataset.from_dict(get_dataset_dict('train_huggingface_format.csv')).map(tokenize_function, batched=True).shuffle(seed=42)
eval_dataset_full = Dataset.from_dict(get_dataset_dict('dev-0_huggingface_format.csv')).map(tokenize_function, batched=True)
eval_dataset_small = eval_dataset_full.shuffle(seed=42).select(range(2000))
test_dataset_A = Dataset.from_dict(get_dataset_dict('test-A_huggingface_format.csv')).map(tokenize_function, batched=True)
if TEST:
train_dataset = train_dataset.select(range(500))
eval_dataset_full = eval_dataset_full.select(range(400))
eval_dataset_small = eval_dataset_small.select(range(50))
test_dataset_A = test_dataset_A.select(range(200))
scalers = dict()
values_to_scale = ('year_start_float', 'year_end_float', 'year_middle_float')
for v in values_to_scale:
scalers[v] = MinMaxScaler().fit(np.array(train_dataset[v]).reshape(-1, 1))
def add_scaled(example):
for factor in values_to_scale:
example[factor + '_scaled'] = scalers[factor].transform(np.array(example[factor]).reshape(-1,1)).reshape(1,-1)[0].item()
return example
train_dataset = train_dataset.map(add_scaled)
eval_dataset_full = eval_dataset_full.map(add_scaled)
eval_dataset_small = eval_dataset_small.map(add_scaled)
test_dataset_A = test_dataset_A.map(add_scaled)
with open('train_dataset.pickle', 'wb') as f_p:
pickle.dump(train_dataset, f_p)
with open('eval_dataset_small.pickle', 'wb') as f_p:
pickle.dump(eval_dataset_small, f_p)
with open('eval_dataset_full.pickle', 'wb') as f_p:
pickle.dump(eval_dataset_full, f_p)
with open('test_dataset_A.pickle', 'wb') as f_p:
pickle.dump(test_dataset_A, f_p)
with open('scalers.pickle', 'wb') as f_p:
pickle.dump(scalers, f_p)

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from config import MODEL, TEST
import pickle
from datasets import load_dataset
from transformers import AutoTokenizer, RobertaModel, RobertaTokenizer
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification
from transformers import AdamW
from transformers import get_scheduler
import torch
from tqdm.auto import tqdm
BATCH_SIZE = 6
EARLY_STOPPING = 3
WARMUP_STEPS = 10_000
STEPS_EVAL = 5_000
if TEST:
STEPS_EVAL = 100
WARMUP_STEPS = 10
with open('train_dataset.pickle', 'rb') as f_p:
train_dataset = pickle.load(f_p)
with open('eval_dataset_small.pickle', 'rb') as f_p:
eval_dataset_small = pickle.load(f_p)
with open('eval_dataset_full.pickle', 'rb') as f_p:
eval_dataset_full = pickle.load(f_p)
train_dataloader = DataLoader(train_dataset, shuffle=True, batch_size=BATCH_SIZE)
eval_dataloader_small = DataLoader(eval_dataset_small, batch_size=BATCH_SIZE)
eval_dataloader_full = DataLoader(eval_dataset_full, batch_size=BATCH_SIZE)
model = AutoModelForSequenceClassification.from_pretrained(MODEL, num_labels=1)
optimizer = AdamW(model.parameters(), lr=1e-6)
num_epochs = 15
num_training_steps = num_epochs * len(train_dataloader)
lr_scheduler = get_scheduler(
"linear",
optimizer=optimizer,
num_warmup_steps=WARMUP_STEPS,
num_training_steps=num_training_steps
)
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model.to(device)
progress_bar = tqdm(range(num_training_steps))
model.train()
model.train()
model.to(device)
def transform_batch(batch):
batch['input_ids'] = torch.stack(batch['input_ids']).permute(1,0).to(device)
batch['attention_mask'] = torch.stack(batch['attention_mask']).permute(1,0).to(device)
batch['labels'] = batch['year_middle_float_scaled'].to(device).float()
batch['labels'].to(device)
batch['input_ids'].to(device)
batch['attention_mask'].to(device)
for c in set(batch.keys()) - {'input_ids', 'attention_mask', 'labels'}:
del batch[c]
return batch
def eval(full=False):
model.eval()
eval_loss = 0.0
dataloader = eval_dataloader_full if full else eval_dataloader_small
for i, batch in enumerate(dataloader):
batch = transform_batch(batch)
outputs = model(**batch)
loss = outputs.loss
eval_loss += loss.item()
print(f'epoch {epoch} eval loss: {eval_loss / i }')
model.train()
return eval_loss
best_eval_loss = 9999
epochs_without_progress = 0
for epoch in range(num_epochs):
train_loss = 0.0
for i, batch in enumerate(train_dataloader):
batch = transform_batch(batch)
outputs = model(**batch)
loss = outputs.loss
loss.backward()
train_loss += loss.item()
progress_bar.update(1)
# DELAYED UPDATE
#if i % 16 == 1 and i > 1:
# optimizer.step()
# #lr_scheduler.step()
# optimizer.zero_grad()
# DELAYED UPDATE
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
if i % STEPS_EVAL == 0 and i > 1:
print(f' epoch {epoch} train loss: {train_loss / STEPS_EVAL }', end='\t\t')
train_loss = 0.0
eval(full = False)
model.save_pretrained(f'roberta_year_prediction/epoch_{epoch}')
eval_loss = eval(full=True)
if eval_loss < best_eval_loss:
model.save_pretrained(f'roberta_year_prediction/epoch_best')
best_eval_loss = eval_loss
else:
epochs_without_progress += 1
if epochs_without_progress > EARLY_STOPPING:
break

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import pickle
import torch
from transformers import AutoModelForSequenceClassification
from torch.utils.data import DataLoader
from tqdm.auto import tqdm
with open('eval_dataset_full.pickle','rb') as f_p:
eval_dataset_full = pickle.load(f_p)
with open('test_dataset_A.pickle','rb') as f_p:
test_dataset = pickle.load(f_p)
device = 'cuda'
model = AutoModelForSequenceClassification.from_pretrained('./roberta_year_prediction/epoch_best')
model.eval()
model.to(device)
with open('scalers.pickle', 'rb') as f_scaler:
scalers = pickle.load(f_scaler)
def predict(dataset, out_f):
eval_dataloader = DataLoader(dataset, batch_size=1)
outputs = []
progress_bar = tqdm(range(len(eval_dataloader)))
for batch in eval_dataloader:
batch['input_ids'] = torch.stack(batch['input_ids']).permute(1,0).to(device)
batch['attention_mask'] = torch.stack(batch['attention_mask']).permute(1,0).to(device)
batch['labels'] = batch['year_middle_float_scaled'].to(device).float()
batch['labels'].to(device)
batch['input_ids'].to(device)
batch['attention_mask'].to(device)
for c in set(batch.keys()) - {'input_ids', 'attention_mask', 'labels'}:
del batch[c]
outputs.extend(model(**batch).logits.tolist())
progress_bar.update(1)
outputs_transformed = scalers['year_middle_float'].inverse_transform(outputs)
with open(out_f,'w') as f_out:
for o in outputs_transformed:
f_out.write(str(o[0]) + '\n')
predict(eval_dataset_full, '../dev-0/out.tsv')
predict(test_dataset, '../test-A/out.tsv')

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import pickle
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from torch.utils.data import DataLoader
from tqdm.auto import tqdm
#with open('train_dataset.pickle','rb') as f_p:
# train_dataset = pickle.load(f_p)
#
#with open('eval_dataset_small.pickle','rb') as f_p:
# eval_dataset_small = pickle.load(f_p)
#
#with open('eval_dataset_full.pickle','rb') as f_p:
# eval_dataset_full = pickle.load(f_p)
#
#with open('test_dataset_A.pickle','rb') as f_p:
# test_dataset_A = pickle.load(f_p)
with open('dev-0_huggingface_format.csv','r') as f_p:
eval_dataset_full = f_p.readlines()
with open('test-A_huggingface_format.csv','r') as f_p:
test_dataset = f_p.readlines()
device = 'cuda'
model = AutoModelForSequenceClassification.from_pretrained('./roberta_year_prediction/epoch_best')
tokenizer = AutoTokenizer.from_pretrained('roberta-base')
model.eval()
model.to(device)
with open('scalers.pickle', 'rb') as f_scaler:
scalers = pickle.load(f_scaler)
tokenizer = AutoTokenizer.from_pretrained('roberta-base')
def predict(dataset, out_f):
outputs = []
for sample in tqdm(dataset[1:]):
y, t = sample.split('\t')
t = t.rstrip()
t = tokenizer(t, padding="max_length", truncation=True, max_length=512, return_tensors='pt').to('cuda')
outputs.extend(model(**t).logits.tolist())
outputs_transformed = scalers['year'].inverse_transform(outputs)
with open(out_f,'w') as f_out:
for o in outputs_transformed:
f_out.write(str(o[0]) + '\n')
predict(eval_dataset_full, '../dev-0/out.tsv')
predict(test_dataset, '../test-A/out.tsv')

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#MODEL = '/home/wmi/RoBERTa/without_date/checkpoint-1325000'
MODEL = 'roberta-large'
TEST=False

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