Spaces:
Runtime error
Runtime error
File size: 8,223 Bytes
d03160a aab703c d03160a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 |
import torch, transformers
import numpy as np
from torch import nn
from torch.utils.data import DataLoader
import pytorch_lightning as pl
from transformers import AutoTokenizer, AutoModel
from sklearn.metrics import f1_score
from .util import mean_pooling, token_embeddings_filtering_padding, read_corpus, CEFRDataset, eval_multiclass
class LevelEstimaterBase(pl.LightningModule):
def __init__(self, corpus_path, test_corpus_path, pretrained_model, with_ib, attach_wlv,
num_labels,
word_num_labels, alpha,
batch_size,
learning_rate, warmup,
lm_layer):
super().__init__()
self.save_hyperparameters()
self.CEFR_lvs = 6
if attach_wlv and with_ib:
raise Exception('Information bottleneck and word labels cannot be used together!')
self.corpus_path = corpus_path
self.test_corpus_path = test_corpus_path
self.pretrained_model = pretrained_model
self.with_ib = with_ib
self.attach_wlv = attach_wlv
self.num_labels = num_labels
self.word_num_labels = word_num_labels
self.alpha = alpha
self.batch_size = batch_size
self.learning_rate = learning_rate
self.warmup = warmup
self.lm_layer = lm_layer
# Load pre-trained model
self.load_pretrained_lm()
def load_pretrained_lm(self):
if 'roberta' in self.pretrained_model:
self.tokenizer = AutoTokenizer.from_pretrained(self.pretrained_model, add_prefix_space=True)
else:
self.tokenizer = AutoTokenizer.from_pretrained(self.pretrained_model)
self.lm = AutoModel.from_pretrained(self.pretrained_model)
def precompute_loss_weights(self, epsilon=1e-5):
train_levels_a, train_levels_b, _ = read_corpus(self.corpus_path + '_train.txt', self.num_labels)
train_levels = np.concatenate((train_levels_a, train_levels_b[train_levels_b != train_levels_a]))
train_sentlv_ratio = np.array([np.sum(train_levels == lv) for lv in range(self.CEFR_lvs)])
train_sentlv_ratio = train_sentlv_ratio / np.sum(train_sentlv_ratio)
train_sentlv_weights = np.power(train_sentlv_ratio, self.alpha) / np.sum(
np.power(train_sentlv_ratio, self.alpha)) / (train_sentlv_ratio + epsilon)
return torch.Tensor(train_sentlv_weights)
def encode(self, batch):
outputs = self.lm(batch['input_ids'], attention_mask=batch['attention_mask'], output_hidden_states=True)
return outputs.hidden_states[self.lm_layer], None
def forward(self, inputs):
pass
def training_step(self, batch, batch_idx):
pass
def validation_step(self, batch, batch_idx):
pass
def test_step(self, batch, batch_idx):
pass
def get_gold_labels(self, predictions, lower_labels, higher_labels):
if torch.sum(predictions == lower_labels) >= torch.sum(predictions == higher_labels):
gold_labels = lower_labels
gold_labels[predictions == higher_labels] = higher_labels[predictions == higher_labels]
else:
gold_labels = higher_labels
gold_labels[predictions == lower_labels] = lower_labels[predictions == lower_labels]
return gold_labels
def evaluation(self, outputs, test=False):
pred_labels, gold_labels_low, gold_labels_high = [], [], []
for output in outputs:
gold_labels_low += output['gold_labels_low'].tolist()
gold_labels_high += output['gold_labels_high'].tolist()
pred_labels += output['pred_labels'].tolist()
gold_labels_high = np.array(gold_labels_high)
gold_labels_low = np.array(gold_labels_low)
pred_labels = np.array(pred_labels)
# pick higher or lower labels that the model performs better
gold_labels = self.get_gold_labels(torch.from_numpy(pred_labels), torch.from_numpy(gold_labels_low),
torch.from_numpy(gold_labels_high))
gold_labels = gold_labels.numpy()
eval_score = f1_score(gold_labels, pred_labels, average='macro')
logs = {"score": eval_score}
if test:
eval_multiclass(self.logger.log_dir + '/sentence', gold_labels, pred_labels)
with open(self.logger.log_dir + '/test_predictions.txt', 'w') as fw:
fw.write('Sentence_Lv\n')
for sent_lv in pred_labels:
fw.write('{0}\n'.format(sent_lv))
return logs
def configure_optimizers(self):
optimizer = transformers.AdamW(self.parameters(), lr=self.learning_rate)
# Warm-up scheduler
if self.warmup > 0:
scheduler = transformers.get_constant_schedule_with_warmup(optimizer, num_warmup_steps=self.warmup)
return {"optimizer": optimizer, "lr_scheduler": scheduler}
else:
return optimizer
def prepare_data(self):
self.train_levels_a, self.train_levels_b, self.train_sents = read_corpus(
self.corpus_path + '_train.txt', self.num_labels)
self.dev_levels_a, self.dev_levels_b, self.dev_sents = read_corpus(
self.corpus_path + '_dev.txt', self.num_labels)
self.test_levels_a, self.test_levels_b, self.test_sents = read_corpus(
self.test_corpus_path + '_test.txt', self.num_labels)
# return the dataloader for each split
def train_dataloader(self):
data_type = torch.float if self.num_labels == 1 else torch.long
y_sent_a = torch.tensor(self.train_levels_a, dtype=data_type).unsqueeze(1)
y_sent_b = torch.tensor(self.train_levels_b, dtype=data_type).unsqueeze(1)
inputs = self.my_tokenize(self.train_sents)
return DataLoader(CEFRDataset(inputs, y_sent_a, y_sent_b), batch_size=self.batch_size, shuffle=True)
def val_dataloader(self):
data_type = torch.float if self.num_labels == 1 else torch.long
y_sent_a = torch.tensor(self.dev_levels_a, dtype=data_type).unsqueeze(1)
y_sent_b = torch.tensor(self.dev_levels_b, dtype=data_type).unsqueeze(1)
inputs = self.my_tokenize(self.dev_sents)
return DataLoader(CEFRDataset(inputs, y_sent_a, y_sent_b), batch_size=self.batch_size, shuffle=False)
def test_dataloader(self):
data_type = torch.float if self.num_labels == 1 else torch.long
y_sent_a = torch.tensor(self.test_levels_a, dtype=data_type).unsqueeze(1)
y_sent_b = torch.tensor(self.test_levels_b, dtype=data_type).unsqueeze(1)
inputs = self.my_tokenize(self.test_sents)
return DataLoader(CEFRDataset(inputs, y_sent_a, y_sent_b), batch_size=self.batch_size, shuffle=False)
def my_tokenize(self, sents):
inputs = self.tokenizer(sents, return_tensors="pt", padding=True, is_split_into_words=True,
return_offsets_mapping=True)
return inputs
def retokenize_with_wordlvs(self, sents, wlvs):
wlv_sequences = [[self.word_lv_dic[lv] for lv in wlv_list if lv >= 0 and lv < self.word_num_labels] for wlv_list
in
wlvs.clone().detach().numpy()]
inputs = self.tokenizer(sents, text_pair=wlv_sequences, return_tensors="pt", padding=True,
is_split_into_words=True,
return_offsets_mapping=True)
return inputs
def wordlabel_to_tokenlabel(self, all_token_ids, all_offsets_mapping, labels):
token_labels = torch.zeros_like(all_token_ids)
for sid in range(all_token_ids.shape[0]):
wid = -1
for i, offset in enumerate(all_offsets_mapping[sid]):
if offset[1] == 0: # Special tokens like CLS, PAD # Faster but cannot handle self-added [SEP] token
# if all_token_ids[sid][i] in self.tokenizer.all_special_ids: # Special tokens like CLS, PAD: Much slower
token_labels[sid, i] = -1
continue
if offset[0] == 0: # New word starts
wid += 1
token_labels[sid, i] = labels[sid][wid]
return token_labels
|