code stringlengths 17 6.64M |
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def _CalACC(model, dataloader):
model.eval()
correct = 0
label_list = []
pred_list = []
with torch.no_grad():
for (i_batch, data) in enumerate(dataloader):
'Prediction'
(batch_input_tokens, batch_labels, batch_speaker_tokens) = data
(batch_input_tokens, ... |
def _SaveModel(model, path):
if (not os.path.exists(path)):
os.makedirs(path)
torch.save(model.state_dict(), os.path.join(path, 'model.bin'))
|
def encode_right_truncated(text, tokenizer, max_length=511):
tokenized = tokenizer.tokenize(text)
truncated = tokenized[(- max_length):]
ids = tokenizer.convert_tokens_to_ids(truncated)
return ([tokenizer.cls_token_id] + ids)
|
def padding(ids_list, tokenizer):
max_len = 0
for ids in ids_list:
if (len(ids) > max_len):
max_len = len(ids)
pad_ids = []
for ids in ids_list:
pad_len = (max_len - len(ids))
add_ids = [tokenizer.pad_token_id for _ in range(pad_len)]
pad_ids.append((ids + a... |
def encode_right_truncated_gpt(text, tokenizer, max_length=511):
tokenized = tokenizer.tokenize(text)
truncated = tokenized[(- max_length):]
ids = tokenizer.convert_tokens_to_ids(truncated)
return (ids + [tokenizer.cls_token_id])
|
def padding_gpt(ids_list, tokenizer):
max_len = 0
for ids in ids_list:
if (len(ids) > max_len):
max_len = len(ids)
pad_ids = []
for ids in ids_list:
pad_len = (max_len - len(ids))
add_ids = [tokenizer.pad_token_id for _ in range(pad_len)]
pad_ids.append((add... |
def make_batch_roberta(sessions):
(batch_input, batch_labels, batch_speaker_tokens) = ([], [], [])
for session in sessions:
data = session[0]
label_list = session[1]
(context_speaker, context, emotion, sentiment) = data
now_speaker = context_speaker[(- 1)]
speaker_utt_l... |
def make_batch_bert(sessions):
(batch_input, batch_labels, batch_speaker_tokens) = ([], [], [])
for session in sessions:
data = session[0]
label_list = session[1]
(context_speaker, context, emotion, sentiment) = data
now_speaker = context_speaker[(- 1)]
speaker_utt_list... |
def make_batch_gpt(sessions):
(batch_input, batch_labels, batch_speaker_tokens) = ([], [], [])
for session in sessions:
data = session[0]
label_list = session[1]
(context_speaker, context, emotion, sentiment) = data
now_speaker = context_speaker[(- 1)]
speaker_utt_list ... |
class MELD_loader(Dataset):
def __init__(self, txt_file, dataclass):
self.dialogs = []
f = open(txt_file, 'r')
dataset = f.readlines()
f.close()
temp_speakerList = []
context = []
context_speaker = []
self.speakerNum = []
emodict = {'anger':... |
class Emory_loader(Dataset):
def __init__(self, txt_file, dataclass):
self.dialogs = []
f = open(txt_file, 'r')
dataset = f.readlines()
f.close()
'sentiment'
pos = ['Joyful', 'Peaceful', 'Powerful']
neg = ['Mad', 'Sad', 'Scared']
neu = ['Neutral']
... |
class IEMOCAP_loader(Dataset):
def __init__(self, txt_file, dataclass):
self.dialogs = []
f = open(txt_file, 'r')
dataset = f.readlines()
f.close()
temp_speakerList = []
context = []
context_speaker = []
self.speakerNum = []
pos = ['exc', 'h... |
class DD_loader(Dataset):
def __init__(self, txt_file, dataclass):
self.dialogs = []
f = open(txt_file, 'r')
dataset = f.readlines()
f.close()
temp_speakerList = []
context = []
context_speaker = []
self.speakerNum = []
self.emoSet = set()
... |
def CELoss(pred_outs, labels):
'\n pred_outs: [batch, clsNum]\n labels: [batch]\n '
loss = nn.CrossEntropyLoss()
loss_val = loss(pred_outs, labels)
return loss_val
|
def main():
'Dataset Loading'
batch_size = args.batch
dataset = args.dataset
dataclass = args.cls
sample = args.sample
model_type = args.pretrained
dataType = 'multi'
if (dataset == 'MELD'):
if args.dyadic:
dataType = 'dyadic'
else:
dataType = 'm... |
def _CalACC(model, dataloader):
model.eval()
correct = 0
label_list = []
pred_list = []
(p1num, p2num, p3num) = (0, 0, 0)
with torch.no_grad():
for (i_batch, data) in enumerate(dataloader):
'Prediction'
(batch_input_tokens, batch_labels) = data
(batc... |
def _SaveModel(model, path):
if (not os.path.exists(path)):
os.makedirs(path)
torch.save(model.state_dict(), os.path.join(path, 'model.bin'))
|
def encode_right_truncated(text, tokenizer, max_length=511):
tokenized = tokenizer.tokenize(text)
truncated = tokenized[(- max_length):]
ids = tokenizer.convert_tokens_to_ids(truncated)
return ([tokenizer.cls_token_id] + ids)
|
def padding(ids_list, tokenizer):
max_len = 0
for ids in ids_list:
if (len(ids) > max_len):
max_len = len(ids)
pad_ids = []
for ids in ids_list:
pad_len = (max_len - len(ids))
add_ids = [tokenizer.pad_token_id for _ in range(pad_len)]
pad_ids.append((ids + a... |
def encode_right_truncated_gpt(text, tokenizer, max_length=511):
tokenized = tokenizer.tokenize(text)
truncated = tokenized[(- max_length):]
ids = tokenizer.convert_tokens_to_ids(truncated)
return (ids + [tokenizer.cls_token_id])
|
def padding_gpt(ids_list, tokenizer):
max_len = 0
for ids in ids_list:
if (len(ids) > max_len):
max_len = len(ids)
pad_ids = []
for ids in ids_list:
pad_len = (max_len - len(ids))
add_ids = [tokenizer.pad_token_id for _ in range(pad_len)]
pad_ids.append((add... |
def make_batch_roberta(sessions):
(batch_input, batch_labels) = ([], [])
for session in sessions:
data = session[0]
label_list = session[1]
(context_speaker, context, emotion, sentiment) = data
now_speaker = context_speaker[(- 1)]
speaker_utt_list = []
inputStri... |
def make_batch_bert(sessions):
(batch_input, batch_labels) = ([], [])
for session in sessions:
data = session[0]
label_list = session[1]
(context_speaker, context, emotion, sentiment) = data
now_speaker = context_speaker[(- 1)]
speaker_utt_list = []
inputString ... |
def make_batch_gpt(sessions):
(batch_input, batch_labels) = ([], [])
for session in sessions:
data = session[0]
label_list = session[1]
(context_speaker, context, emotion, sentiment) = data
now_speaker = context_speaker[(- 1)]
speaker_utt_list = []
inputString =... |
class MELD_loader(Dataset):
def __init__(self, txt_file, dataclass):
self.dialogs = []
f = open(txt_file, 'r')
dataset = f.readlines()
f.close()
temp_speakerList = []
context = []
context_speaker = []
self.speakerNum = []
emodict = {'anger':... |
class Emory_loader(Dataset):
def __init__(self, txt_file, dataclass):
self.dialogs = []
f = open(txt_file, 'r')
dataset = f.readlines()
f.close()
'sentiment'
pos = ['Joyful', 'Peaceful', 'Powerful']
neg = ['Mad', 'Sad', 'Scared']
neu = ['Neutral']
... |
class IEMOCAP_loader(Dataset):
def __init__(self, txt_file, dataclass):
self.dialogs = []
f = open(txt_file, 'r')
dataset = f.readlines()
f.close()
temp_speakerList = []
context = []
context_speaker = []
self.speakerNum = []
pos = ['exc', 'h... |
class DD_loader(Dataset):
def __init__(self, txt_file, dataclass):
self.dialogs = []
f = open(txt_file, 'r')
dataset = f.readlines()
f.close()
temp_speakerList = []
context = []
context_speaker = []
self.speakerNum = []
self.emoSet = set()
... |
def CELoss(pred_outs, labels):
'\n pred_outs: [batch, clsNum]\n labels: [batch]\n '
loss = nn.CrossEntropyLoss()
loss_val = loss(pred_outs, labels)
return loss_val
|
def main():
'Dataset Loading'
batch_size = args.batch
dataset = args.dataset
dataclass = args.cls
sample = args.sample
model_type = args.pretrained
dataType = 'multi'
if (dataset == 'MELD'):
if args.dyadic:
dataType = 'dyadic'
else:
dataType = 'm... |
def _CalACC(model, dataloader):
model.eval()
correct = 0
label_list = []
pred_list = []
(p1num, p2num, p3num) = (0, 0, 0)
with torch.no_grad():
for (i_batch, data) in enumerate(dataloader):
'Prediction'
(batch_input_tokens, batch_labels) = data
(batc... |
def _SaveModel(model, path):
if (not os.path.exists(path)):
os.makedirs(path)
torch.save(model.state_dict(), os.path.join(path, 'model.bin'))
|
def encode_right_truncated(text, tokenizer, max_length=511):
tokenized = tokenizer.tokenize(text)
truncated = tokenized[(- max_length):]
ids = tokenizer.convert_tokens_to_ids(truncated)
return ([tokenizer.cls_token_id] + ids)
|
def padding(ids_list, tokenizer):
max_len = 0
for ids in ids_list:
if (len(ids) > max_len):
max_len = len(ids)
pad_ids = []
for ids in ids_list:
pad_len = (max_len - len(ids))
add_ids = [tokenizer.pad_token_id for _ in range(pad_len)]
pad_ids.append((ids + a... |
def encode_right_truncated_gpt(text, tokenizer, max_length=511):
tokenized = tokenizer.tokenize(text)
truncated = tokenized[(- max_length):]
ids = tokenizer.convert_tokens_to_ids(truncated)
return (ids + [tokenizer.cls_token_id])
|
def padding_gpt(ids_list, tokenizer):
max_len = 0
for ids in ids_list:
if (len(ids) > max_len):
max_len = len(ids)
pad_ids = []
for ids in ids_list:
pad_len = (max_len - len(ids))
add_ids = [tokenizer.pad_token_id for _ in range(pad_len)]
pad_ids.append((add... |
def make_batch_roberta(sessions):
(batch_input, batch_labels) = ([], [])
for session in sessions:
data = session[0]
label_list = session[1]
(context_speaker, context, emotion, sentiment) = data
now_speaker = context_speaker[(- 1)]
speaker_utt_list = []
inputStri... |
def make_batch_bert(sessions):
(batch_input, batch_labels) = ([], [])
for session in sessions:
data = session[0]
label_list = session[1]
(context_speaker, context, emotion, sentiment) = data
now_speaker = context_speaker[(- 1)]
speaker_utt_list = []
inputString ... |
def make_batch_gpt(sessions):
(batch_input, batch_labels) = ([], [])
for session in sessions:
data = session[0]
label_list = session[1]
(context_speaker, context, emotion, sentiment) = data
now_speaker = context_speaker[(- 1)]
speaker_utt_list = []
inputString =... |
class MELD_loader(Dataset):
def __init__(self, txt_file, dataclass):
self.dialogs = []
f = open(txt_file, 'r')
dataset = f.readlines()
f.close()
temp_speakerList = []
context = []
context_speaker = []
self.speakerNum = []
emodict = {'anger':... |
class Emory_loader(Dataset):
def __init__(self, txt_file, dataclass):
self.dialogs = []
f = open(txt_file, 'r')
dataset = f.readlines()
f.close()
'sentiment'
pos = ['Joyful', 'Peaceful', 'Powerful']
neg = ['Mad', 'Sad', 'Scared']
neu = ['Neutral']
... |
class IEMOCAP_loader(Dataset):
def __init__(self, txt_file, dataclass):
self.dialogs = []
f = open(txt_file, 'r')
dataset = f.readlines()
f.close()
temp_speakerList = []
context = []
context_speaker = []
self.speakerNum = []
pos = ['exc', 'h... |
class DD_loader(Dataset):
def __init__(self, txt_file, dataclass):
self.dialogs = []
f = open(txt_file, 'r')
dataset = f.readlines()
f.close()
temp_speakerList = []
context = []
context_speaker = []
self.speakerNum = []
self.emoSet = set()
... |
def CELoss(pred_outs, labels):
'\n pred_outs: [batch, clsNum]\n labels: [batch]\n '
loss = nn.CrossEntropyLoss()
loss_val = loss(pred_outs, labels)
return loss_val
|
def main():
'Dataset Loading'
batch_size = args.batch
dataset = args.dataset
dataclass = args.cls
sample = args.sample
context_type = args.context_type
speaker_type = args.speaker_type
freeze = args.freeze
dataType = 'multi'
if (dataset == 'MELD'):
if args.dyadic:
... |
def _CalACC(model, dataloader):
model.eval()
correct = 0
label_list = []
pred_list = []
with torch.no_grad():
for (i_batch, data) in enumerate(dataloader):
'Prediction'
(batch_input_tokens, batch_labels, batch_speaker_tokens) = data
(batch_input_tokens, ... |
def _SaveModel(model, path):
if (not os.path.exists(path)):
os.makedirs(path)
torch.save(model.state_dict(), os.path.join(path, 'model.bin'))
|
def encode_right_truncated(text, tokenizer, max_length=511):
tokenized = tokenizer.tokenize(text)
truncated = tokenized[(- max_length):]
ids = tokenizer.convert_tokens_to_ids(truncated)
return ([tokenizer.cls_token_id] + ids)
|
def padding(ids_list, tokenizer):
max_len = 0
for ids in ids_list:
if (len(ids) > max_len):
max_len = len(ids)
pad_ids = []
for ids in ids_list:
pad_len = (max_len - len(ids))
add_ids = [tokenizer.pad_token_id for _ in range(pad_len)]
pad_ids.append((ids + a... |
def encode_right_truncated_gpt(text, tokenizer, max_length=511):
tokenized = tokenizer.tokenize(text)
truncated = tokenized[(- max_length):]
ids = tokenizer.convert_tokens_to_ids(truncated)
return (ids + [tokenizer.cls_token_id])
|
def padding_gpt(ids_list, tokenizer):
max_len = 0
for ids in ids_list:
if (len(ids) > max_len):
max_len = len(ids)
pad_ids = []
for ids in ids_list:
pad_len = (max_len - len(ids))
add_ids = [tokenizer.pad_token_id for _ in range(pad_len)]
pad_ids.append((add... |
def make_batch_roberta(sessions):
(batch_input, batch_labels, batch_speaker_tokens) = ([], [], [])
for session in sessions:
data = session[0]
label_list = session[1]
(context_speaker, context, emotion, sentiment) = data
now_speaker = context_speaker[(- 1)]
speaker_utt_l... |
def make_batch_bert(sessions):
(batch_input, batch_labels, batch_speaker_tokens) = ([], [], [])
for session in sessions:
data = session[0]
label_list = session[1]
(context_speaker, context, emotion, sentiment) = data
now_speaker = context_speaker[(- 1)]
speaker_utt_list... |
def make_batch_gpt(sessions):
(batch_input, batch_labels, batch_speaker_tokens) = ([], [], [])
for session in sessions:
data = session[0]
label_list = session[1]
(context_speaker, context, emotion, sentiment) = data
now_speaker = context_speaker[(- 1)]
speaker_utt_list ... |
def make_batch_roberta_bert(sessions):
(batch_input, batch_labels, batch_speaker_tokens) = ([], [], [])
for session in sessions:
data = session[0]
label_list = session[1]
(context_speaker, context, emotion, sentiment) = data
now_speaker = context_speaker[(- 1)]
speaker_... |
def make_batch_roberta_gpt(sessions):
(batch_input, batch_labels, batch_speaker_tokens) = ([], [], [])
for session in sessions:
data = session[0]
label_list = session[1]
(context_speaker, context, emotion, sentiment) = data
now_speaker = context_speaker[(- 1)]
speaker_u... |
def make_batch_bert_roberta(sessions):
(batch_input, batch_labels, batch_speaker_tokens) = ([], [], [])
for session in sessions:
data = session[0]
label_list = session[1]
(context_speaker, context, emotion, sentiment) = data
now_speaker = context_speaker[(- 1)]
speaker_... |
class MELD_loader(Dataset):
def __init__(self, txt_file, dataclass):
self.dialogs = []
f = open(txt_file, 'r')
dataset = f.readlines()
f.close()
temp_speakerList = []
context = []
context_speaker = []
self.speakerNum = []
emodict = {'anger':... |
class Emory_loader(Dataset):
def __init__(self, txt_file, dataclass):
self.dialogs = []
f = open(txt_file, 'r')
dataset = f.readlines()
f.close()
'sentiment'
pos = ['Joyful', 'Peaceful', 'Powerful']
neg = ['Mad', 'Sad', 'Scared']
neu = ['Neutral']
... |
class IEMOCAP_loader(Dataset):
def __init__(self, txt_file, dataclass):
self.dialogs = []
f = open(txt_file, 'r')
dataset = f.readlines()
f.close()
temp_speakerList = []
context = []
context_speaker = []
self.speakerNum = []
pos = ['exc', 'h... |
class DD_loader(Dataset):
def __init__(self, txt_file, dataclass):
self.dialogs = []
f = open(txt_file, 'r')
dataset = f.readlines()
f.close()
temp_speakerList = []
context = []
context_speaker = []
self.speakerNum = []
self.emoSet = set()
... |
def CELoss(pred_outs, labels):
'\n pred_outs: [batch, clsNum]\n labels: [batch]\n '
loss = nn.CrossEntropyLoss()
loss_val = loss(pred_outs, labels)
return loss_val
|
def main():
'Dataset Loading'
batch_size = args.batch
dataset = args.dataset
dataclass = args.cls
sample = args.sample
context_type = args.context_type
speaker_type = args.speaker_type
freeze = args.freeze
dataType = 'multi'
if (dataset == 'MELD'):
if args.dyadic:
... |
def _CalACC(model, dataloader):
model.eval()
correct = 0
label_list = []
pred_list = []
with torch.no_grad():
for (i_batch, data) in enumerate(dataloader):
'Prediction'
(batch_input_tokens, batch_labels, batch_speaker_tokens) = data
(batch_input_tokens, ... |
def _SaveModel(model, path):
if (not os.path.exists(path)):
os.makedirs(path)
torch.save(model.state_dict(), os.path.join(path, 'model.bin'))
|
def encode_right_truncated(text, tokenizer, max_length=511):
tokenized = tokenizer.tokenize(text)
truncated = tokenized[(- max_length):]
ids = tokenizer.convert_tokens_to_ids(truncated)
return ([tokenizer.cls_token_id] + ids)
|
def padding(ids_list, tokenizer):
max_len = 0
for ids in ids_list:
if (len(ids) > max_len):
max_len = len(ids)
pad_ids = []
for ids in ids_list:
pad_len = (max_len - len(ids))
add_ids = [tokenizer.pad_token_id for _ in range(pad_len)]
pad_ids.append((ids + a... |
def encode_right_truncated_gpt(text, tokenizer, max_length=511):
tokenized = tokenizer.tokenize(text)
truncated = tokenized[(- max_length):]
ids = tokenizer.convert_tokens_to_ids(truncated)
return (ids + [tokenizer.cls_token_id])
|
def padding_gpt(ids_list, tokenizer):
max_len = 0
for ids in ids_list:
if (len(ids) > max_len):
max_len = len(ids)
pad_ids = []
for ids in ids_list:
pad_len = (max_len - len(ids))
add_ids = [tokenizer.pad_token_id for _ in range(pad_len)]
pad_ids.append((add... |
def make_batch_roberta(sessions):
(batch_input, batch_labels, batch_speaker_tokens) = ([], [], [])
for session in sessions:
data = session[0]
label_list = session[1]
(context_speaker, context, emotion, sentiment) = data
now_speaker = context_speaker[(- 1)]
speaker_utt_l... |
def make_batch_bert(sessions):
(batch_input, batch_labels, batch_speaker_tokens) = ([], [], [])
for session in sessions:
data = session[0]
label_list = session[1]
(context_speaker, context, emotion, sentiment) = data
now_speaker = context_speaker[(- 1)]
speaker_utt_list... |
def make_batch_gpt(sessions):
(batch_input, batch_labels, batch_speaker_tokens) = ([], [], [])
for session in sessions:
data = session[0]
label_list = session[1]
(context_speaker, context, emotion, sentiment) = data
now_speaker = context_speaker[(- 1)]
speaker_utt_list ... |
def make_batch_roberta_bert(sessions):
(batch_input, batch_labels, batch_speaker_tokens) = ([], [], [])
for session in sessions:
data = session[0]
label_list = session[1]
(context_speaker, context, emotion, sentiment) = data
now_speaker = context_speaker[(- 1)]
speaker_... |
def make_batch_roberta_gpt(sessions):
(batch_input, batch_labels, batch_speaker_tokens) = ([], [], [])
for session in sessions:
data = session[0]
label_list = session[1]
(context_speaker, context, emotion, sentiment) = data
now_speaker = context_speaker[(- 1)]
speaker_u... |
def make_batch_bert_roberta(sessions):
(batch_input, batch_labels, batch_speaker_tokens) = ([], [], [])
for session in sessions:
data = session[0]
label_list = session[1]
(context_speaker, context, emotion, sentiment) = data
now_speaker = context_speaker[(- 1)]
speaker_... |
class MELD_loader(Dataset):
def __init__(self, txt_file, dataclass):
self.dialogs = []
f = open(txt_file, 'r')
dataset = f.readlines()
f.close()
temp_speakerList = []
context = []
context_speaker = []
self.speakerNum = []
emodict = {'anger':... |
class Emory_loader(Dataset):
def __init__(self, txt_file, dataclass):
self.dialogs = []
f = open(txt_file, 'r')
dataset = f.readlines()
f.close()
'sentiment'
pos = ['Joyful', 'Peaceful', 'Powerful']
neg = ['Mad', 'Sad', 'Scared']
neu = ['Neutral']
... |
class IEMOCAP_loader(Dataset):
def __init__(self, txt_file, dataclass):
self.dialogs = []
f = open(txt_file, 'r')
dataset = f.readlines()
f.close()
temp_speakerList = []
context = []
context_speaker = []
self.speakerNum = []
pos = ['ang', 'e... |
class DD_loader(Dataset):
def __init__(self, txt_file, dataclass):
self.dialogs = []
f = open(txt_file, 'r')
dataset = f.readlines()
f.close()
temp_speakerList = []
context = []
context_speaker = []
self.speakerNum = []
self.emoSet = set()
... |
class MELD_loader(Dataset):
def __init__(self, txt_file, dataclass):
self.dialogs = []
f = open(txt_file, 'r')
dataset = f.readlines()
f.close()
temp_speakerList = []
self.speakerNum = []
emodict = {'anger': 'anger', 'disgust': 'disgust', 'fear': 'fear', 'j... |
class Emory_loader(Dataset):
def __init__(self, txt_file, dataclass):
self.dialogs = []
f = open(txt_file, 'r')
dataset = f.readlines()
f.close()
'sentiment'
pos = ['Joyful', 'Peaceful', 'Powerful']
neg = ['Mad', 'Sad', 'Scared']
neu = ['Neutral']
... |
class IEMOCAP_loader(Dataset):
def __init__(self, txt_file, dataclass):
self.dialogs = []
f = open(txt_file, 'r')
dataset = f.readlines()
f.close()
self.speakerNum = []
pos = ['exc', 'hap']
neg = ['ang', 'fru', 'sad']
neu = ['neu']
emodict =... |
class DD_loader(Dataset):
def __init__(self, txt_file, dataclass):
self.dialogs = []
f = open(txt_file, 'r')
dataset = f.readlines()
f.close()
self.speakerNum = []
self.emoSet = set()
self.sentiSet = set()
pos = ['happiness']
neg = ['anger',... |
def CELoss(pred_outs, labels):
'\n pred_outs: [batch, clsNum]\n labels: [batch]\n '
loss = nn.CrossEntropyLoss()
loss_val = loss(pred_outs, labels)
return loss_val
|
def main():
'Dataset Loading'
batch_size = args.batch
dataset = args.dataset
dataclass = args.cls
sample = args.sample
model_type = args.pretrained
dataType = 'multi'
if (dataset == 'MELD'):
if args.dyadic:
dataType = 'dyadic'
else:
dataType = 'm... |
def _CalACC(model, dataloader):
model.eval()
correct = 0
label_list = []
pred_list = []
with torch.no_grad():
for (i_batch, data) in enumerate(dataloader):
'Prediction'
(batch_input_tokens, batch_labels) = data
(batch_input_tokens, batch_labels) = (batch... |
def _SaveModel(model, path):
if (not os.path.exists(path)):
os.makedirs(path)
torch.save(model.state_dict(), os.path.join(path, 'model.bin'))
|
def encode_right_truncated(text, tokenizer, max_length=511):
tokenized = tokenizer.tokenize(text)
truncated = tokenized[(- max_length):]
ids = tokenizer.convert_tokens_to_ids(truncated)
return ([tokenizer.cls_token_id] + ids)
|
def padding(ids_list, tokenizer):
max_len = 0
for ids in ids_list:
if (len(ids) > max_len):
max_len = len(ids)
pad_ids = []
for ids in ids_list:
pad_len = (max_len - len(ids))
add_ids = [tokenizer.pad_token_id for _ in range(pad_len)]
pad_ids.append((ids + a... |
def encode_right_truncated_gpt(text, tokenizer, max_length=511):
tokenized = tokenizer.tokenize(text)
truncated = tokenized[(- max_length):]
ids = tokenizer.convert_tokens_to_ids(truncated)
return (ids + [tokenizer.cls_token_id])
|
def padding_gpt(ids_list, tokenizer):
max_len = 0
for ids in ids_list:
if (len(ids) > max_len):
max_len = len(ids)
pad_ids = []
for ids in ids_list:
pad_len = (max_len - len(ids))
add_ids = [tokenizer.pad_token_id for _ in range(pad_len)]
pad_ids.append((add... |
def make_batch_roberta(sessions):
(batch_input, batch_labels) = ([], [])
for session in sessions:
data = session[0]
label_list = session[1]
(utt, emotion, sentiment) = data
batch_input.append(encode_right_truncated(utt.strip(), roberta_tokenizer))
if (len(label_list) > ... |
def make_batch_bert(sessions):
(batch_input, batch_labels) = ([], [])
for session in sessions:
data = session[0]
label_list = session[1]
(utt, emotion, sentiment) = data
batch_input.append(encode_right_truncated(utt.strip(), bert_tokenizer))
if (len(label_list) > 3):
... |
def make_batch_gpt(sessions):
(batch_input, batch_labels) = ([], [])
for session in sessions:
data = session[0]
label_list = session[1]
(utt, emotion, sentiment) = data
batch_input.append(encode_right_truncated_gpt(utt.strip(), gpt_tokenizer, max_length=511))
if (len(la... |
class MELD_loader(Dataset):
def __init__(self, txt_file, dataclass):
self.dialogs = []
f = open(txt_file, 'r')
dataset = f.readlines()
f.close()
temp_speakerList = []
self.speakerNum = []
emodict = {'anger': 'anger', 'disgust': 'disgust', 'fear': 'fear', 'j... |
class Emory_loader(Dataset):
def __init__(self, txt_file, dataclass):
self.dialogs = []
f = open(txt_file, 'r')
dataset = f.readlines()
f.close()
'sentiment'
pos = ['Joyful', 'Peaceful', 'Powerful']
neg = ['Mad', 'Sad', 'Scared']
neu = ['Neutral']
... |
class IEMOCAP_loader(Dataset):
def __init__(self, txt_file, dataclass):
self.dialogs = []
f = open(txt_file, 'r')
dataset = f.readlines()
f.close()
self.speakerNum = []
pos = ['exc', 'hap']
neg = ['ang', 'fru', 'sad']
neu = ['neu']
emodict =... |
class DD_loader(Dataset):
def __init__(self, txt_file, dataclass):
self.dialogs = []
f = open(txt_file, 'r')
dataset = f.readlines()
f.close()
self.speakerNum = []
self.emoSet = set()
self.sentiSet = set()
pos = ['happiness']
neg = ['anger',... |
def CELoss(pred_outs, labels):
'\n pred_outs: [batch, clsNum]\n labels: [batch]\n '
loss = nn.CrossEntropyLoss()
loss_val = loss(pred_outs, labels)
return loss_val
|
def main():
'Dataset Loading'
batch_size = args.batch
dataset = args.dataset
dataclass = args.cls
sample = args.sample
model_type = args.pretrained
dataType = 'multi'
if (dataset == 'MELD'):
if args.dyadic:
dataType = 'dyadic'
else:
dataType = 'm... |
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