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# -*- coding: utf-8 -*-
import os
from parser.utils import Embedding
from parser.utils.alg import eisner
from parser.utils.common import bos, pad, unk
from parser.utils.corpus import CoNLL, Corpus
from parser.utils.field import BertField, CharField, Field
from parser.utils.fn import ispunct
from parser.utils.metric import Metric
import torch
import torch.nn as nn
from transformers import AutoTokenizer, BertTokenizer
class CMD(object):
def __call__(self, args):
self.args = args
if not os.path.exists(args.file):
os.mkdir(args.file)
if not os.path.exists(args.fields) or args.preprocess:
print("Preprocess the data")
self.WORD = Field('words', pad=pad, unk=unk, bos=bos, lower=True)
if args.feat == 'char':
self.FEAT = CharField('chars', pad=pad, unk=unk, bos=bos,
fix_len=args.fix_len, tokenize=list)
elif args.feat == 'bert':
tokenizer = BertTokenizer.from_pretrained(args.bert_model)
#tokenizer = AutoTokenizer.from_pretrained("sailen7/finetuning-sentiment-model-3000-samples")
self.FEAT = BertField('bert', pad='[PAD]', bos='[CLS]',
tokenize=tokenizer.encode)
else:
self.FEAT = Field('tags', bos=bos)
self.HEAD = Field('heads', bos=bos, use_vocab=False, fn=int)
self.REL = Field('rels', bos=bos)
if args.feat in ('char', 'bert'):
self.fields = CoNLL(FORM=(self.WORD, self.FEAT),
HEAD=self.HEAD, DEPREL=self.REL)
else:
self.fields = CoNLL(FORM=self.WORD, CPOS=self.FEAT,
HEAD=self.HEAD, DEPREL=self.REL)
train = Corpus.load(args.ftrain, self.fields)
# if args.fembed:
# embed = Embedding.load(args.fembed, args.unk)
# else:
embed = None
self.WORD.build(train, args.min_freq, embed)
self.FEAT.build(train)
self.REL.build(train)
torch.save(self.fields, args.fields)
else:
self.fields = torch.load(args.fields)
if args.feat in ('char', 'bert'):
self.WORD, self.FEAT = self.fields.FORM
else:
self.WORD, self.FEAT = self.fields.FORM, self.fields.CPOS
self.HEAD, self.REL = self.fields.HEAD, self.fields.DEPREL
self.puncts = torch.tensor([i for s, i in self.WORD.vocab.stoi.items()
if ispunct(s)]).to(args.device)
self.criterion = nn.CrossEntropyLoss()
print(f"{self.WORD}\n{self.FEAT}\n{self.HEAD}\n{self.REL}")
args.update({
'n_words': self.WORD.vocab.n_init,
'n_feats': len(self.FEAT.vocab),
'n_rels': len(self.REL.vocab),
'pad_index': self.WORD.pad_index,
'unk_index': self.WORD.unk_index,
'bos_index': self.WORD.bos_index
})
def train(self, loader):
self.model.train()
for words, feats, arcs, rels in loader:
self.optimizer.zero_grad()
mask = words.ne(self.args.pad_index)
# ignore the first token of each sentence
mask[:, 0] = 0
arc_scores, rel_scores = self.model(words, feats)
loss = self.get_loss(arc_scores, rel_scores, arcs, rels, mask)
loss.backward()
nn.utils.clip_grad_norm_(self.model.parameters(),
self.args.clip)
self.optimizer.step()
self.scheduler.step()
@torch.no_grad()
def evaluate(self, loader):
self.model.eval()
loss, metric = 0, Metric()
for words, feats, arcs, rels in loader:
mask = words.ne(self.args.pad_index)
# ignore the first token of each sentence
mask[:, 0] = 0
arc_scores, rel_scores = self.model(words, feats)
loss += self.get_loss(arc_scores, rel_scores, arcs, rels, mask)
arc_preds, rel_preds = self.decode(arc_scores, rel_scores, mask)
# ignore all punctuation if not specified
if not self.args.punct:
mask &= words.unsqueeze(-1).ne(self.puncts).all(-1)
metric(arc_preds, rel_preds, arcs, rels, mask)
loss /= len(loader)
return loss, metric
@torch.no_grad()
def predict(self, loader):
self.model.eval()
all_arcs, all_rels = [], []
for words, feats in loader:
print("words ->", words, " ", "features -> ",feats )
mask = words.ne(self.args.pad_index)
# ignore the first token of each sentence
mask[:, 0] = 0
lens = mask.sum(1).tolist()
arc_scores, rel_scores = self.model(words, feats)
arc_preds, rel_preds = self.decode(arc_scores, rel_scores, mask)
all_arcs.extend(arc_preds[mask].split(lens))
all_rels.extend(rel_preds[mask].split(lens))
all_arcs = [seq.tolist() for seq in all_arcs]
all_rels = [self.REL.vocab.id2token(seq.tolist()) for seq in all_rels]
return all_arcs, all_rels
def get_loss(self, arc_scores, rel_scores, arcs, rels, mask):
arc_scores, arcs = arc_scores[mask], arcs[mask]
rel_scores, rels = rel_scores[mask], rels[mask]
rel_scores = rel_scores[torch.arange(len(arcs)), arcs]
arc_loss = self.criterion(arc_scores, arcs)
rel_loss = self.criterion(rel_scores, rels)
loss = arc_loss + rel_loss
return loss
def decode(self, arc_scores, rel_scores, mask):
if self.args.tree:
arc_preds = eisner(arc_scores, mask)
else:
arc_preds = arc_scores.argmax(-1)
rel_preds = rel_scores.argmax(-1)
rel_preds = rel_preds.gather(-1, arc_preds.unsqueeze(-1)).squeeze(-1)
return arc_preds, rel_preds
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