# coding=utf-8 # Copyright 2021 The IDEA Authors. All rights reserved. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # http://www.apache.org/licenses/LICENSE-2.0 # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from logging import basicConfig import torch from torch import nn import json from tqdm import tqdm import os import numpy as np from transformers import BertTokenizer, AutoTokenizer import pytorch_lightning as pl from pytorch_lightning.callbacks import ModelCheckpoint from pytorch_lightning import loggers from torch.utils.data import Dataset, DataLoader from transformers.optimization import get_linear_schedule_with_warmup from transformers import BertForMaskedLM, AlbertTokenizer from transformers import AutoConfig from transformers import MegatronBertForMaskedLM from modeling_deberta_v2 import DebertaV2ForMaskedLM from modeling_albert import AlbertForMaskedLM import argparse import copy import streamlit as st import time # os.environ["CUDA_VISIBLE_DEVICES"] = '6' class UniMCDataset(Dataset): def __init__(self, data, yes_token, no_token, tokenizer, args, used_mask=True): super().__init__() self.tokenizer = tokenizer self.max_length = args.max_length self.num_labels = args.num_labels self.used_mask = used_mask self.data = data self.args = args self.yes_token = yes_token self.no_token = no_token def __len__(self): return len(self.data) def __getitem__(self, index): return self.encode(self.data[index], self.used_mask) def get_token_type(self, sep_idx, max_length): token_type_ids = np.zeros(shape=(max_length,)) for i in range(len(sep_idx)-1): if i % 2 == 0: ty = np.ones(shape=(sep_idx[i+1]-sep_idx[i],)) else: ty = np.zeros(shape=(sep_idx[i+1]-sep_idx[i],)) token_type_ids[sep_idx[i]:sep_idx[i+1]] = ty return token_type_ids def get_position_ids(self, label_idx, max_length, question_len): question_position_ids = np.arange(question_len) label_position_ids = np.arange(question_len, label_idx[-1]) for i in range(len(label_idx)-1): label_position_ids[label_idx[i]-question_len:label_idx[i+1]-question_len] = np.arange( question_len, question_len+label_idx[i+1]-label_idx[i]) max_len_label = max(label_position_ids) text_position_ids = np.arange( max_len_label+1, max_length+max_len_label+1-label_idx[-1]) position_ids = list(question_position_ids) + \ list(label_position_ids)+list(text_position_ids) if max_length <= 512: return position_ids[:max_length] else: for i in range(512, max_length): if position_ids[i] > 511: position_ids[i] = 511 return position_ids[:max_length] def get_att_mask(self, attention_mask, label_idx, question_len): max_length = len(attention_mask) attention_mask = np.array(attention_mask) attention_mask = np.tile(attention_mask[None, :], (max_length, 1)) zeros = np.zeros( shape=(label_idx[-1]-question_len, label_idx[-1]-question_len)) attention_mask[question_len:label_idx[-1], question_len:label_idx[-1]] = zeros for i in range(len(label_idx)-1): label_token_length = label_idx[i+1]-label_idx[i] if label_token_length <= 0: print('label_idx', label_idx) print('question_len', question_len) continue ones = np.ones(shape=(label_token_length, label_token_length)) attention_mask[label_idx[i]:label_idx[i+1], label_idx[i]:label_idx[i+1]] = ones return attention_mask def random_masking(self, token_ids, maks_rate, mask_start_idx, max_length, mask_id, tokenizer): rands = np.random.random(len(token_ids)) source, target = [], [] for i, (r, t) in enumerate(zip(rands, token_ids)): if i < mask_start_idx: source.append(t) target.append(-100) continue if r < maks_rate * 0.8: source.append(mask_id) target.append(t) elif r < maks_rate * 0.9: source.append(t) target.append(t) elif r < maks_rate: source.append(np.random.choice(tokenizer.vocab_size - 1) + 1) target.append(t) else: source.append(t) target.append(-100) while len(source) < max_length: source.append(0) target.append(-100) return source[:max_length], target[:max_length] def encode(self, item, used_mask=False): while len(self.tokenizer.encode('[MASK]'.join(item['choice']))) > self.max_length-32: item['choice'] = [c[:int(len(c)/2)] for c in item['choice']] if 'textb' in item.keys() and item['textb'] != '': if 'question' in item.keys() and item['question'] != '': texta = '[MASK]' + '[MASK]'.join(item['choice']) + '[SEP]' + \ item['question'] + '[SEP]' + \ item['texta']+'[SEP]'+item['textb'] else: texta = '[MASK]' + '[MASK]'.join(item['choice']) + '[SEP]' + \ item['texta']+'[SEP]'+item['textb'] else: if 'question' in item.keys() and item['question'] != '': texta = '[MASK]' + '[MASK]'.join(item['choice']) + '[SEP]' + \ item['question'] + '[SEP]' + item['texta'] else: texta = '[MASK]' + '[MASK]'.join(item['choice']) + \ '[SEP]' + item['texta'] encode_dict = self.tokenizer.encode_plus(texta, max_length=self.max_length, padding='max_length', truncation='longest_first') encode_sent = encode_dict['input_ids'] token_type_ids = encode_dict['token_type_ids'] attention_mask = encode_dict['attention_mask'] sample_max_length = sum(encode_dict['attention_mask']) if 'label' not in item.keys(): item['label'] = 0 item['answer'] = '' question_len = 1 label_idx = [question_len] for choice in item['choice']: cur_mask_idx = label_idx[-1] + \ len(self.tokenizer.encode(choice, add_special_tokens=False))+1 label_idx.append(cur_mask_idx) token_type_ids = [0]*question_len+[1] * \ (label_idx[-1]-label_idx[0]+1)+[0]*self.max_length token_type_ids = token_type_ids[:self.max_length] attention_mask = self.get_att_mask( attention_mask, label_idx, question_len) position_ids = self.get_position_ids( label_idx, self.max_length, question_len) clslabels_mask = np.zeros(shape=(len(encode_sent),)) clslabels_mask[label_idx[:-1]] = 10000 clslabels_mask = clslabels_mask-10000 mlmlabels_mask = np.zeros(shape=(len(encode_sent),)) mlmlabels_mask[label_idx[0]] = 1 used_mask = False if used_mask: mask_rate = 0.1*np.random.choice(4, p=[0.3, 0.3, 0.25, 0.15]) source, target = self.random_masking(token_ids=encode_sent, maks_rate=mask_rate, mask_start_idx=label_idx[-1], max_length=self.max_length, mask_id=self.tokenizer.mask_token_id, tokenizer=self.tokenizer) else: source, target = encode_sent[:], encode_sent[:] source = np.array(source) target = np.array(target) source[label_idx[:-1]] = self.tokenizer.mask_token_id target[label_idx[:-1]] = self.no_token target[label_idx[item['label']]] = self.yes_token input_ids = source[:sample_max_length] token_type_ids = token_type_ids[:sample_max_length] attention_mask = attention_mask[:sample_max_length, :sample_max_length] position_ids = position_ids[:sample_max_length] mlmlabels = target[:sample_max_length] clslabels = label_idx[item['label']] clslabels_mask = clslabels_mask[:sample_max_length] mlmlabels_mask = mlmlabels_mask[:sample_max_length] return { "input_ids": torch.tensor(input_ids).long(), "token_type_ids": torch.tensor(token_type_ids).long(), "attention_mask": torch.tensor(attention_mask).float(), "position_ids": torch.tensor(position_ids).long(), "mlmlabels": torch.tensor(mlmlabels).long(), "clslabels": torch.tensor(clslabels).long(), "clslabels_mask": torch.tensor(clslabels_mask).float(), "mlmlabels_mask": torch.tensor(mlmlabels_mask).float(), } class UniMCDataModel(pl.LightningDataModule): @staticmethod def add_data_specific_args(parent_args): parser = parent_args.add_argument_group('TASK NAME DataModel') parser.add_argument('--num_workers', default=8, type=int) parser.add_argument('--batchsize', default=16, type=int) parser.add_argument('--max_length', default=512, type=int) return parent_args def __init__(self, train_data, val_data, yes_token, no_token, tokenizer, args): super().__init__() self.batchsize = args.batchsize self.train_data = UniMCDataset( train_data, yes_token, no_token, tokenizer, args, True) self.valid_data = UniMCDataset( val_data, yes_token, no_token, tokenizer, args, False) def train_dataloader(self): return DataLoader(self.train_data, shuffle=True, collate_fn=self.collate_fn, batch_size=self.batchsize, pin_memory=False) def val_dataloader(self): return DataLoader(self.valid_data, shuffle=False, collate_fn=self.collate_fn, batch_size=self.batchsize, pin_memory=False) def collate_fn(self, batch): ''' Aggregate a batch data. batch = [ins1_dict, ins2_dict, ..., insN_dict] batch_data = {'sentence':[ins1_sentence, ins2_sentence...], 'input_ids':[ins1_input_ids, ins2_input_ids...], ...} ''' batch_data = {} for key in batch[0]: batch_data[key] = [example[key] for example in batch] batch_data['input_ids'] = nn.utils.rnn.pad_sequence(batch_data['input_ids'], batch_first=True, padding_value=0) batch_data['clslabels_mask'] = nn.utils.rnn.pad_sequence(batch_data['clslabels_mask'], batch_first=True, padding_value=-10000) batch_size, batch_max_length = batch_data['input_ids'].shape for k, v in batch_data.items(): if k == 'input_ids' or k == 'clslabels_mask': continue if k == 'clslabels': batch_data[k] = torch.tensor(v).long() continue if k != 'attention_mask': batch_data[k] = nn.utils.rnn.pad_sequence(v, batch_first=True, padding_value=0) else: attention_mask = torch.zeros( (batch_size, batch_max_length, batch_max_length)) for i, att in enumerate(v): sample_length, _ = att.shape attention_mask[i, :sample_length, :sample_length] = att batch_data[k] = attention_mask return batch_data class UniMCModel(nn.Module): def __init__(self, pre_train_dir, yes_token): super().__init__() self.config = AutoConfig.from_pretrained(pre_train_dir) if self.config.model_type == 'megatron-bert': self.bert = MegatronBertForMaskedLM.from_pretrained(pre_train_dir) elif self.config.model_type == 'deberta-v2': self.bert = DebertaV2ForMaskedLM.from_pretrained(pre_train_dir) elif self.config.model_type == 'albert': self.bert = AlbertForMaskedLM.from_pretrained(pre_train_dir) else: self.bert = BertForMaskedLM.from_pretrained(pre_train_dir) self.loss_func = torch.nn.CrossEntropyLoss() self.yes_token = yes_token def forward(self, input_ids, attention_mask, token_type_ids, position_ids=None, mlmlabels=None, clslabels=None, clslabels_mask=None, mlmlabels_mask=None): batch_size, seq_len = input_ids.shape outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, token_type_ids=token_type_ids, labels=mlmlabels) # (bsz, seq, dim) mask_loss = outputs.loss mlm_logits = outputs.logits cls_logits = mlm_logits[:, :, self.yes_token].view(-1, seq_len)+clslabels_mask if mlmlabels == None: return 0, mlm_logits, cls_logits else: cls_loss = self.loss_func(cls_logits, clslabels) all_loss = mask_loss+cls_loss return all_loss, mlm_logits, cls_logits class UniMCLitModel(pl.LightningModule): @staticmethod def add_model_specific_args(parent_args): parser = parent_args.add_argument_group('BaseModel') parser.add_argument('--learning_rate', default=1e-5, type=float) parser.add_argument('--weight_decay', default=0.1, type=float) parser.add_argument('--warmup', default=0.01, type=float) parser.add_argument('--num_labels', default=2, type=int) return parent_args def __init__(self, args, yes_token, num_data=100): super().__init__() self.args = args self.num_data = num_data self.model = UniMCModel(self.args.pretrained_model_path, yes_token) def setup(self, stage) -> None: if stage == 'fit': num_gpus = self.trainer.gpus if self.trainer.gpus is not None else 0 self.total_step = int(self.trainer.max_epochs * self.num_data / (max(1, num_gpus) * self.trainer.accumulate_grad_batches)) print('Total training step:', self.total_step) def training_step(self, batch, batch_idx): loss, logits, cls_logits = self.model(**batch) cls_acc = self.comput_metrix( cls_logits, batch['clslabels'], batch['mlmlabels_mask']) self.log('train_loss', loss) self.log('train_acc', cls_acc) return loss def validation_step(self, batch, batch_idx): loss, logits, cls_logits = self.model(**batch) cls_acc = self.comput_metrix( cls_logits, batch['clslabels'], batch['mlmlabels_mask']) self.log('val_loss', loss) self.log('val_acc', cls_acc) def configure_optimizers(self): no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight'] paras = list( filter(lambda p: p[1].requires_grad, self.named_parameters())) paras = [{ 'params': [p for n, p in paras if not any(nd in n for nd in no_decay)], 'weight_decay': self.args.weight_decay }, { 'params': [p for n, p in paras if any(nd in n for nd in no_decay)], 'weight_decay': 0.0 }] optimizer = torch.optim.AdamW(paras, lr=self.args.learning_rate) scheduler = get_linear_schedule_with_warmup( optimizer, int(self.total_step * self.args.warmup), self.total_step) return [{ 'optimizer': optimizer, 'lr_scheduler': { 'scheduler': scheduler, 'interval': 'step', 'frequency': 1 } }] def comput_metrix(self, logits, labels, mlmlabels_mask): logits = torch.nn.functional.softmax(logits, dim=-1) logits = torch.argmax(logits, dim=-1) y_pred = logits.view(size=(-1,)) y_true = labels.view(size=(-1,)) corr = torch.eq(y_pred, y_true).float() return torch.sum(corr.float())/labels.size(0) class TaskModelCheckpoint: @staticmethod def add_argparse_args(parent_args): parser = parent_args.add_argument_group('BaseModel') parser.add_argument('--monitor', default='val_acc', type=str) parser.add_argument('--mode', default='max', type=str) parser.add_argument('--dirpath', default='./log/', type=str) parser.add_argument( '--filename', default='model-{epoch:02d}-{val_acc:.4f}', type=str) parser.add_argument('--save_top_k', default=3, type=float) parser.add_argument('--every_n_epochs', default=1, type=float) parser.add_argument('--every_n_train_steps', default=100, type=float) parser.add_argument('--save_weights_only', default=True, type=bool) return parent_args def __init__(self, args): self.callbacks = ModelCheckpoint(monitor=args.monitor, save_top_k=args.save_top_k, mode=args.mode, save_last=True, every_n_train_steps=args.every_n_train_steps, save_weights_only=args.save_weights_only, dirpath=args.dirpath, filename=args.filename) class UniMCPredict: def __init__(self, yes_token, no_token, model, tokenizer, args): self.tokenizer = tokenizer self.args = args self.data_model = UniMCDataModel( [], [], yes_token, no_token, tokenizer, args) self.model = model def predict(self, batch_data): batch = [self.data_model.train_data.encode( sample) for sample in batch_data] batch = self.data_model.collate_fn(batch) batch = {k: v.to(self.model.device) for k, v in batch.items()} _, _, logits = self.model.model(**batch) soft_logits = torch.nn.functional.softmax(logits, dim=-1) logits = torch.argmax(soft_logits, dim=-1).detach().cpu().numpy() soft_logits = soft_logits.detach().cpu().numpy() clslabels_mask = batch['clslabels_mask'].detach( ).cpu().numpy().tolist() clslabels = batch['clslabels'].detach().cpu().numpy().tolist() for i, v in enumerate(batch_data): label_idx = [idx for idx, v in enumerate( clslabels_mask[i]) if v == 0.] label = label_idx.index(logits[i]) answer = batch_data[i]['choice'][label] score = {} for c in range(len(batch_data[i]['choice'])): score[batch_data[i]['choice'][c]] = float( soft_logits[i][label_idx[c]]) batch_data[i]['label_ori'] = copy.deepcopy(batch_data[i]['label']) batch_data[i]['label'] = label batch_data[i]['answer'] = answer batch_data[i]['score'] = score return batch_data class UniMCPipelines: @staticmethod def pipelines_args(parent_args): total_parser = parent_args.add_argument_group("pipelines args") total_parser.add_argument( '--pretrained_model_path', default='', type=str) total_parser.add_argument('--load_checkpoints_path', default='', type=str) total_parser.add_argument('--train', action='store_true') total_parser.add_argument('--language', default='chinese', type=str) total_parser = UniMCDataModel.add_data_specific_args(total_parser) total_parser = TaskModelCheckpoint.add_argparse_args(total_parser) total_parser = UniMCLitModel.add_model_specific_args(total_parser) total_parser = pl.Trainer.add_argparse_args(parent_args) return parent_args def __init__(self, args): self.args = args self.checkpoint_callback = TaskModelCheckpoint(args).callbacks self.logger = loggers.TensorBoardLogger(save_dir=args.default_root_dir) self.trainer = pl.Trainer.from_argparse_args(args, logger=self.logger, callbacks=[self.checkpoint_callback]) self.config = AutoConfig.from_pretrained(args.pretrained_model_path) if self.config.model_type == 'albert': self.tokenizer = AlbertTokenizer.from_pretrained( args.pretrained_model_path) else: if args.language == 'chinese': self.tokenizer = BertTokenizer.from_pretrained( args.pretrained_model_path) else: self.tokenizer = AutoTokenizer.from_pretrained( args.pretrained_model_path, is_split_into_words=True, add_prefix_space=True) if args.language == 'chinese': self.yes_token = self.tokenizer.encode('是')[1] self.no_token = self.tokenizer.encode('非')[1] else: self.yes_token = self.tokenizer.encode('yes')[1] self.no_token = self.tokenizer.encode('no')[1] if args.load_checkpoints_path != '': self.model = UniMCLitModel.load_from_checkpoint( args.load_checkpoints_path, args=args, yes_token=self.yes_token) print('load model from: ', args.load_checkpoints_path) else: self.model = UniMCLitModel(args, yes_token=self.yes_token) def fit(self, train_data, dev_data, process=True): if process: train_data = self.preprocess(train_data) dev_data = self.preprocess(dev_data) data_model = UniMCDataModel( train_data, dev_data, self.yes_token, self.no_token, self.tokenizer, self.args) self.model.num_data = len(train_data) self.trainer.fit(self.model, data_model) def predict(self, test_data, cuda=True, process=True): if process: test_data = self.preprocess(test_data) result = [] start = 0 if cuda: self.model = self.model.cuda() self.model.model.eval() predict_model = UniMCPredict( self.yes_token, self.no_token, self.model, self.tokenizer, self.args) while start < len(test_data): batch_data = test_data[start:start+self.args.batchsize] start += self.args.batchsize batch_result = predict_model.predict(batch_data) result.extend(batch_result) if process: result = self.postprocess(result) return result def preprocess(self, data): for i, line in enumerate(data): if 'task_type' in line.keys() and line['task_type'] == '语义匹配': data[i]['choice'] = ['不能理解为：'+data[i] ['textb'], '可以理解为：'+data[i]['textb']] # data[i]['question']='怎么理解这段话？' data[i]['textb'] = '' if 'task_type' in line.keys() and line['task_type'] == '自然语言推理': data[i]['choice'] = ['不能推断出：'+data[i]['textb'], '很难推断出：'+data[i]['textb'], '可以推断出：'+data[i]['textb']] # data[i]['question']='根据这段话' data[i]['textb'] = '' return data def postprocess(self, data): for i, line in enumerate(data): if 'task_type' in line.keys() and line['task_type'] == '语义匹配': data[i]['textb'] = data[i]['choice'][0].replace('不能理解为：', '') data[i]['choice'] = ['不相似', '相似'] ns = {} for k, v in data[i]['score'].items(): if '不能' in k: k = '不相似' if '可以' in k: k = '相似' ns[k] = v data[i]['score'] = ns data[i]['answer'] = data[i]['choice'][data[i]['label']] if 'task_type' in line.keys() and line['task_type'] == '自然语言推理': data[i]['textb'] = data[i]['choice'][0].replace('不能推断出：', '') data[i]['choice'] = ['矛盾', '自然', '蕴含'] ns = {} for k, v in data[i]['score'].items(): if '不能' in k: k = '矛盾' if '很难' in k: k = '自然' if '可以' in k: k = '蕴含' ns[k] = v data[i]['score'] = ns data[i]['answer'] = data[i]['choice'][data[i]['label']] return data def load_data(data_path): with open(data_path, 'r', encoding='utf8') as f: lines = f.readlines() samples = [json.loads(line) for line in tqdm(lines)] return samples def comp_acc(pred_data, test_data): corr = 0 for i in range(len(pred_data)): if pred_data[i]['label'] == test_data[i]['label']: corr += 1 return corr/len(pred_data) @st.experimental_memo() def load_model(model_path): total_parser = argparse.ArgumentParser("TASK NAME") total_parser = UniMCPipelines.pipelines_args(total_parser) args = total_parser.parse_args() args.pretrained_model_path = model_path args.max_length = 512 args.batchsize = 8 args.default_root_dir = './' model = UniMCPipelines(args) return model def main(): text_dict={ 'Text classification「文本分类」':"彭于晏不着急，胡歌不着急，那我也不着急", 'Sentiment「情感分析」':"刚买iphone13 pro 还不到一个月，天天死机最差的一次购物体验", 'Similarity「语义匹配」':"今天心情不好", 'NLI 「自然语言推理」':"小明正在上高中", 'Multiple Choice「多项式阅读理解」':"女：您看这件衣服挺不错的，质量好，价钱也不贵。\n男：再看看吧。", } question_dict={ 'Text classification「文本分类」':"这是什么类型的新闻？", 'Sentiment「情感分析」':"", 'Similarity「语义匹配」':"", 'NLI 「自然语言推理」':"", 'Multiple Choice「多项式阅读理解」':"这个男的是什么意思?", } choice_dict={ 'Text classification「文本分类」':"故事；文化；娱乐；体育；财经；房产；汽车；教育；科技", 'Sentiment「情感分析」':"这是一条好评；这是一条差评", 'Similarity「语义匹配」':"可以理解为：我很不开心；不能理解为：我很不开心", 'NLI 「自然语言推理」':"可以推断出：小明是一个初中生；不能推断出：小明是一个初中生；很难推断出：小明是一个初中生", 'Multiple Choice「多项式阅读理解」':"不想要这件；衣服挺好的；衣服质量不好", } text_dict_en={ 'Text classification「文本分类」':"Henkel AG & Company KGaA operates worldwide with leading brands and technologies in three business areas: Laundry & Home Care Beauty Care and Adhesive Technologies. Henkel is the name behind some of America’s favorite brands.", 'Sentiment「情感分析」':"a gorgeous , high-spirited musical from india that exquisitely blends music , dance , song , and high drama . ", 'Similarity「语义匹配」':"Ricky Clemons ' brief , troubled Missouri basketball career is over .", 'NLI 「自然语言推理」':"That was then, and then's gone. It's now now. I don't mean I 've done a sudden transformation.", 'Multiple Choice「多项式阅读理解」':"A huge crowd is in the stands in an arena. A man throws a javelin. Photographers take pictures in the background. several men", } question_dict_en={ 'Text classification「文本分类」':"", 'Sentiment「情感分析」':"", 'Similarity「语义匹配」':"", 'NLI 「自然语言推理」':"", 'Multiple Choice「多项式阅读理解」':"", } choice_dict_en={ 'Text classification「文本分类」':"Company;Educational Institution;Artist;Athlete;Office Holder", 'Sentiment「情感分析」':"it's great;it's terrible", 'Similarity「语义匹配」':"That can be interpreted as Missouri kicked Ricky Clemons off its team , ending his troubled career there .;That cannot be interpreted as Missouri kicked Ricky Clemons off its team , ending his troubled career there .", 'NLI 「自然语言推理」':"we can infer that she has done a sudden transformation;we can not infer that she has done a sudden transformation;it is diffcult for us to infer that she has done a sudden transformation", 'Multiple Choice「多项式阅读理解」':"are water boarding in a river.;are shown throwing balls.;challenge the man to jump onto the rope.;run to where the javelin lands.", } st.subheader("UniMC Zero-shot 体验") st.sidebar.header("Configuration「参数配置」") sbform = st.sidebar.form("固定参数设置") language = sbform.selectbox('Select a language「选择语言」', ['中文「Chinese」', 'English「英文」']) sbform.form_submit_button("Submit configuration「提交配置」") if '中文' in language: model = load_model('IDEA-CCNL/Erlangshen-UniMC-RoBERTa-110M-Chinese') else: model = load_model('IDEA-CCNL/Erlangshen-UniMC-Albert-235M-English') st.markdown(""" UniMC 核心思想是将自然语言理解任务转化为 multiple choice 任务，其通过控制位置编码和attention mask来让模型可以直接复用 MaskLM head 的参数。这使得 UniMC 仅仅使用 multiple choice 数据集训练就可以超越千亿参数模型在zero-shot场景下。在中文数据集中，UniMC 同样超越了其他模型，获得了FewCLUE和ZeroCLUE两个榜单的第一。 The core idea of UniMC is to convert the natural language understanding task into a multiple choice task, which allows the model to directly reuse the parameters of the MaskLM head by controlling the position encoding and attention mask. This enables UniMC to surpass 100 billion parameter models in zero-shot scenarios just by training with multiple choice datasets. In the Chinese dataset, UniMC also surpassed other models and won the first place in both FewCLUE and ZeroCLUE. """) st.info("Please input the following information to experiencing UniMC「请输入以下信息开始体验 UniMC...」") model_type = st.selectbox('Select task type「选择任务类型」',['Text classification「文本分类」','Sentiment「情感分析」','Similarity「语义匹配」','NLI 「自然语言推理」','Multiple Choice「多项式阅读理解」']) form = st.form("参数设置") if '中文' in language: sentences = form.text_area("Please input the context「请输入句子」", text_dict[model_type]) question = form.text_input("Please input the question「请输入问题（不输入问题也可以）」", question_dict[model_type]) choice = form.text_input("Please input the label「输入标签（以中文；分割）」", choice_dict[model_type]) else: sentences = form.text_area("Please input the context「请输入句子」", text_dict_en[model_type]) question = form.text_input("Please input the question「请输入问题（不输入问题也可以）」", question_dict_en[model_type]) choice = form.text_input("Please input the label(split by ‘;’)「输入标签（以英文;分割）」", choice_dict_en[model_type]) form.form_submit_button("Submit「点击一下，开始预测！」") if '中文' in language: choice = choice.split('；') else: choice = choice.split(';') data = [{"texta": sentences, "textb": "", "question": question, "choice": choice, "answer": "", "label": 0, "id": 0}] start=time.time() is_cuda= True if torch.cuda.is_available() else False result = model.predict(data, cuda=is_cuda) st.success(f"Prediction is successful, consumes {str(time.time()-start)} seconds") st.json(result[0]) if __name__ == "__main__": main()