diff --git a/task3/NER/Config.py b/task3/NER/Config.py new file mode 100644 index 0000000..8151096 --- /dev/null +++ b/task3/NER/Config.py @@ -0,0 +1,46 @@ +import os + +class config: + root = os.getcwd() + dataset = 'chinese ner' + train_data_path = os.path.join(root, 'input/train.json') + dev_data_path = os.path.join(root, 'input/dev.json') + test_data_path = os.path.join(root, 'input/test.json') + + cache_path = os.path.join(root, 'cache/') + + save_path = os.path.join(root, 'saved_models/model.pt') + predict_path = os.path.join(root, 'output/predict.json') + + dist_emb_size = 20 + type_emb_size = 20 + lstm_hid_size = 512 + conv_hid_size = 96 + bert_hid_size = 768 + biaffine_size = 512 + ffnn_hid_size = 288 + + dilation = [1, 2, 3] + + emb_dropout = 0.5 + conv_dropout = 0.5 + out_dropout = 0.33 + + epochs = 10 + batch_size = 4 + checkout_params = {'batch_size': 1, 'shuffle': False} + train_params = {'batch_size': 1, 'shuffle': True} + dev_params = {'batch_size': 1, 'shuffle': False} + test_params = {'batch_size': 1, 'shuffle': False} + + learning_rate = 1e-3 + weight_decay = 0 + clip_grad_norm = 5.0 + bert_name = 'bert-base-uncased' + bert_learning_rate = 5e-6 + warm_factor = 0.1 + + use_bert_last_4_layers = True + + seed = 2022 + logger = None \ No newline at end of file diff --git a/task3/NER/Model.py b/task3/NER/Model.py new file mode 100644 index 0000000..f7e23b3 --- /dev/null +++ b/task3/NER/Model.py @@ -0,0 +1,253 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence +from transformers import AutoModel + + +class LayerNorm(nn.Module): + def __init__(self, input_dim, cond_dim=0, center=True, scale=True, epsilon=None, conditional=False, + hidden_units=None, hidden_activation='linear', hidden_initializer='xaiver', **kwargs): + super(LayerNorm, self).__init__() + """ + input_dim: inputs.shape[-1] + cond_dim: cond.shape[-1] + """ + self.center = center + self.scale = scale + self.conditional = conditional + self.hidden_units = hidden_units + self.hidden_initializer = hidden_initializer + self.epsilon = epsilon or 1e-12 + self.input_dim = input_dim + self.cond_dim = cond_dim + + if self.center: + self.beta = nn.Parameter(torch.zeros(input_dim)) + if self.scale: + self.gamma = nn.Parameter(torch.ones(input_dim)) + + if self.conditional: + if self.hidden_units is not None: + self.hidden_dense = nn.Linear(in_features=self.cond_dim, out_features=self.hidden_units, bias=False) + if self.center: + self.beta_dense = nn.Linear(in_features=self.cond_dim, out_features=input_dim, bias=False) + if self.scale: + self.gamma_dense = nn.Linear(in_features=self.cond_dim, out_features=input_dim, bias=False) + + self.initialize_weights() + + def initialize_weights(self): + + if self.conditional: + if self.hidden_units is not None: + if self.hidden_initializer == 'normal': + torch.nn.init.normal(self.hidden_dense.weight) + elif self.hidden_initializer == 'xavier': # glorot_uniform + torch.nn.init.xavier_uniform_(self.hidden_dense.weight) + + if self.center: + torch.nn.init.constant_(self.beta_dense.weight, 0) + if self.scale: + torch.nn.init.constant_(self.gamma_dense.weight, 0) + + def forward(self, inputs, cond=None): + if self.conditional: + if self.hidden_units is not None: + cond = self.hidden_dense(cond) + + for _ in range(len(inputs.shape) - len(cond.shape)): + cond = cond.unsqueeze(1) # cond = K.expand_dims(cond, 1) + + if self.center: + beta = self.beta_dense(cond) + self.beta + if self.scale: + gamma = self.gamma_dense(cond) + self.gamma + else: + if self.center: + beta = self.beta + if self.scale: + gamma = self.gamma + + outputs = inputs + if self.center: + mean = torch.mean(outputs, dim=-1).unsqueeze(-1) + outputs = outputs - mean + if self.scale: + variance = torch.mean(outputs ** 2, dim=-1).unsqueeze(-1) + std = (variance + self.epsilon) ** 0.5 + outputs = outputs / std + outputs = outputs * gamma + if self.center: + outputs = outputs + beta + + return outputs + + +class ConvolutionLayer(nn.Module): + def __init__(self, input_size, channels, dilation, dropout=0.1): + super(ConvolutionLayer, self).__init__() + self.base = nn.Sequential( + nn.Dropout2d(dropout), + nn.Conv2d(input_size, channels, kernel_size=1), + nn.GELU(), + ) + + self.convs = nn.ModuleList( + [nn.Conv2d(channels, channels, kernel_size=3, groups=channels, dilation=d, padding=d) for d in dilation]) + + def forward(self, x): + x = x.permute(0, 3, 1, 2).contiguous() + x = self.base(x) + + outputs = [] + for conv in self.convs: + x = conv(x) + x = F.gelu(x) + outputs.append(x) + outputs = torch.cat(outputs, dim=1) + outputs = outputs.permute(0, 2, 3, 1).contiguous() + return outputs + + +class Biaffine(nn.Module): + def __init__(self, n_in, n_out=1, bias_x=True, bias_y=True): + super(Biaffine, self).__init__() + + self.n_in = n_in + self.n_out = n_out + self.bias_x = bias_x + self.bias_y = bias_y + weight = torch.zeros((n_out, n_in + int(bias_x), n_in + int(bias_y))) + nn.init.xavier_normal_(weight) + self.weight = nn.Parameter(weight, requires_grad=True) + + def extra_repr(self): + s = f"n_in={self.n_in}, n_out={self.n_out}" + if self.bias_x: + s += f", bias_x={self.bias_x}" + if self.bias_y: + s += f", bias_y={self.bias_y}" + + return s + + def forward(self, x, y): + if self.bias_x: + x = torch.cat((x, torch.ones_like(x[..., :1])), -1) + if self.bias_y: + y = torch.cat((y, torch.ones_like(y[..., :1])), -1) + # [batch_size, n_out, seq_len, seq_len] + s = torch.einsum('bxi,oij,byj->boxy', x, self.weight, y) + # remove dim 1 if n_out == 1 + s = s.permute(0, 2, 3, 1) + + return s + + +class MLP(nn.Module): + def __init__(self, n_in, n_out, dropout=0): + super().__init__() + + self.linear = nn.Linear(n_in, n_out) + self.activation = nn.GELU() + self.dropout = nn.Dropout(dropout) + + def forward(self, x): + x = self.dropout(x) + x = self.linear(x) + x = self.activation(x) + return x + + +class CoPredictor(nn.Module): + def __init__(self, cls_num, hid_size, biaffine_size, channels, ffnn_hid_size, dropout=0): + super().__init__() + self.mlp1 = MLP(n_in=hid_size, n_out=biaffine_size, dropout=dropout) + self.mlp2 = MLP(n_in=hid_size, n_out=biaffine_size, dropout=dropout) + self.biaffine = Biaffine(n_in=biaffine_size, n_out=cls_num, bias_x=True, bias_y=True) + self.mlp_rel = MLP(channels, ffnn_hid_size, dropout=dropout) + self.linear = nn.Linear(ffnn_hid_size, cls_num) + self.dropout = nn.Dropout(dropout) + + def forward(self, x, y, z): + h = self.dropout(self.mlp1(x)) + t = self.dropout(self.mlp2(y)) + o1 = self.biaffine(h, t) + + z = self.dropout(self.mlp_rel(z)) + o2 = self.linear(z) + return o1 + o2 + + +class Model(nn.Module): + def __init__(self, config): + super(Model, self).__init__() + self.use_bert_last_4_layers = config.use_bert_last_4_layers + + self.lstm_hid_size = config.lstm_hid_size + self.conv_hid_size = config.conv_hid_size + + lstm_input_size = 0 + + self.bert = AutoModel.from_pretrained(config.bert_name, cache_dir="./cache/", output_hidden_states=True) + lstm_input_size += config.bert_hid_size + + self.dis_embs = nn.Embedding(20, config.dist_emb_size) + self.reg_embs = nn.Embedding(3, config.type_emb_size) + + self.encoder = nn.LSTM(lstm_input_size, config.lstm_hid_size // 2, num_layers=1, batch_first=True, + bidirectional=True) + + conv_input_size = config.lstm_hid_size + config.dist_emb_size + config.type_emb_size + + self.convLayer = ConvolutionLayer(conv_input_size, config.conv_hid_size, config.dilation, config.conv_dropout) + self.dropout = nn.Dropout(config.emb_dropout) + self.predictor = CoPredictor(config.label_num, config.lstm_hid_size, config.biaffine_size, + config.conv_hid_size * len(config.dilation), config.ffnn_hid_size, + config.out_dropout) + + self.cln = LayerNorm(config.lstm_hid_size, config.lstm_hid_size, conditional=True) + + def forward(self, bert_inputs, grid_mask2d, dist_inputs, pieces2word, sent_length): + ''' + :param bert_inputs: [B, L'] + :param grid_mask2d: [B, L, L] + :param dist_inputs: [B, L, L] + :param pieces2word: [B, L, L'] + :param sent_length: [B] + :return: + ''' + bert_embs = self.bert(input_ids=bert_inputs, attention_mask=bert_inputs.ne(0).float()) + if self.use_bert_last_4_layers: + bert_embs = torch.stack(bert_embs[2][-4:], dim=-1).mean(-1) + else: + bert_embs = bert_embs[0] + + length = pieces2word.size(1) + + min_value = torch.min(bert_embs).item() + + # Max pooling word representations from pieces + _bert_embs = bert_embs.unsqueeze(1).expand(-1, length, -1, -1) + _bert_embs = torch.masked_fill(_bert_embs, pieces2word.eq(0).unsqueeze(-1), min_value) + word_reps, _ = torch.max(_bert_embs, dim=2) + + word_reps = self.dropout(word_reps) + packed_embs = pack_padded_sequence(word_reps, sent_length.cpu(), batch_first=True, enforce_sorted=False) + packed_outs, (hidden, _) = self.encoder(packed_embs) + word_reps, _ = pad_packed_sequence(packed_outs, batch_first=True, total_length=sent_length.max()) + + cln = self.cln(word_reps.unsqueeze(2), word_reps) + + dis_emb = self.dis_embs(dist_inputs) + tril_mask = torch.tril(grid_mask2d.clone().long()) + reg_inputs = tril_mask + grid_mask2d.clone().long() + reg_emb = self.reg_embs(reg_inputs) + + conv_inputs = torch.cat([dis_emb, reg_emb, cln], dim=-1) + conv_inputs = torch.masked_fill(conv_inputs, grid_mask2d.eq(0).unsqueeze(-1), 0.0) + conv_outputs = self.convLayer(conv_inputs) + conv_outputs = torch.masked_fill(conv_outputs, grid_mask2d.eq(0).unsqueeze(-1), 0.0) + outputs = self.predictor(word_reps, word_reps, conv_outputs) + + return outputs diff --git a/task3/NER/README.md b/task3/NER/README.md new file mode 100644 index 0000000..ad8b934 --- /dev/null +++ b/task3/NER/README.md @@ -0,0 +1,44 @@ +# W2NER + +本代码参考 W2NER论文及模型编写，论文链接[AAAI Press Formatting Instructions for Authors Using LaTeX -- A Guide (arxiv.org)](https://arxiv.org/pdf/2112.10070.pdf) + + + +## 代码运行 + +在colab上运行main.py即可(或许上传空文件夹时colab会忽略，需手动创建，比如output、saved_models等) + + + +## 代码结构 + +由于本人并不是一开始就使用了W2NER模型，各个模型之间的代码也是有所不同的，我将代码分为utils、Model、Config、Trainer、main，只需重写utils内部的APIs和Model(或许有部分Trainer)即可迁移使用不同的模型。 + +### utils + +分为common和DataProcess以及对不同文本的接口APIs + +#### common + +提供logger、read_from_file、write_to_file函数 + +#### DataProcess + +提供Process类，是数据预处理的接口，分为encode预处理和decode格式化 + +#### APIs + +需重写APIDataset、api_encode、api_decode，分别对应不同的接口，其中api_encode接收原始json数据，返回APIDataset所接收的数据；APIDataset接收api_encode的数据，返回Model所需的Dataset；api_decode接收Model输出的output，返回格式化的字典(json)数据 + +### Model + +模型主体 + +### Config + +通用配置 + +### Trainer + +训练类，分为train、eval、predict + diff --git a/task3/NER/Trainer.py b/task3/NER/Trainer.py new file mode 100644 index 0000000..9d9ce9e --- /dev/null +++ b/task3/NER/Trainer.py @@ -0,0 +1,186 @@ +import numpy as np +import prettytable as pt +import torch +import torch.autograd +import torch.nn as nn +import transformers +from sklearn.metrics import precision_recall_fscore_support, f1_score + +def cal_f1(c, p, r): + if r == 0 or p == 0: + return 0, 0, 0 + + r = c / r if r else 0 + p = c / p if p else 0 + + if r and p: + return 2 * p * r / (p + r), p, r + return 0, p, r + + +class Trainer(object): + def __init__(self, model, processor, config, updates_total): + self.model = model + self.processor = processor + self.config = config + self.criterion = nn.CrossEntropyLoss() + + bert_params = set(self.model.bert.parameters()) + other_params = list(set(self.model.parameters()) - bert_params) + no_decay = ['bias', 'LayerNorm.weight'] + params = [ + {'params': [p for n, p in model.bert.named_parameters() if not any(nd in n for nd in no_decay)], + 'lr': config.bert_learning_rate, 'weight_decay': config.weight_decay}, + {'params': [p for n, p in model.bert.named_parameters() if any(nd in n for nd in no_decay)], + 'lr': config.bert_learning_rate, 'weight_decay': 0.0}, + {'params': other_params, + 'lr': config.learning_rate, 'weight_decay': config.weight_decay}, + ] + + self.optimizer = transformers.AdamW(params, lr=config.learning_rate, weight_decay=config.weight_decay) + self.scheduler = transformers.get_linear_schedule_with_warmup( + self.optimizer, num_warmup_steps=config.warm_factor * updates_total, + num_training_steps=updates_total + ) + + def train(self, epoch, data_loader): + self.model.train() + loss_list = [] + pred_result = [] + label_result = [] + + for i, data_batch in enumerate(data_loader): + data_batch = [data.to(self.config.device) for data in data_batch[:-1]] + + bert_inputs, grid_labels, grid_mask2d, pieces2word, dist_inputs, sent_length = data_batch + + outputs = self.model(bert_inputs, grid_mask2d, dist_inputs, pieces2word, sent_length) + + grid_mask2d = grid_mask2d.clone() + loss = self.criterion(outputs[grid_mask2d], grid_labels[grid_mask2d]) + + loss.backward() + torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.config.clip_grad_norm) + self.optimizer.step() + self.optimizer.zero_grad() + + loss_list.append(loss.cpu().item()) + + outputs = torch.argmax(outputs, -1) + grid_labels = grid_labels[grid_mask2d].contiguous().view(-1) + outputs = outputs[grid_mask2d].contiguous().view(-1) + + label_result.append(grid_labels.cpu()) + pred_result.append(outputs.cpu()) + + self.scheduler.step() + + label_result = torch.cat(label_result) + pred_result = torch.cat(pred_result) + + p, r, f1, _ = precision_recall_fscore_support( + label_result.numpy(), pred_result.numpy(), average="macro" + ) + + table = pt.PrettyTable(["Train {}".format(epoch), "Loss", "F1", "Precision", "Recall"]) + table.add_row(["Label", "{:.4f}".format(np.mean(loss_list))] + + ["{:3.4f}".format(x) for x in [f1, p, r]]) + self.config.logger.info("\n{}".format(table)) + return f1 + + def eval(self, epoch, data_loader, is_test=False): + self.model.eval() + + pred_result = [] + label_result = [] + + total_ent_r = 0 + total_ent_p = 0 + total_ent_c = 0 + with torch.no_grad(): + for i, data_batch in enumerate(data_loader): + entity_text = data_batch[-1] + data_batch = [data.cuda() for data in data_batch[:-1]] + bert_inputs, grid_labels, grid_mask2d, pieces2word, dist_inputs, sent_length = data_batch + + outputs = self.model(bert_inputs, grid_mask2d, dist_inputs, pieces2word, sent_length) + length = sent_length + + grid_mask2d = grid_mask2d.clone() + + outputs = torch.argmax(outputs, -1) + ent_c, ent_p, ent_r, _ = self.processor.decode( + outputs.cpu().numpy(), entity_text, length.cpu().numpy()) + + total_ent_r += ent_r + total_ent_p += ent_p + total_ent_c += ent_c + + grid_labels = grid_labels[grid_mask2d].contiguous().view(-1) + outputs = outputs[grid_mask2d].contiguous().view(-1) + + label_result.append(grid_labels.cpu()) + pred_result.append(outputs.cpu()) + + label_result = torch.cat(label_result) + pred_result = torch.cat(pred_result) + + p, r, f1, _ = precision_recall_fscore_support(label_result.numpy(), + pred_result.numpy(), + average="macro") + e_f1, e_p, e_r = cal_f1(total_ent_c, total_ent_p, total_ent_r) + + title = "EVAL" if not is_test else "TEST" + self.config.logger.info('{} Label F1 {}'.format(title, f1_score(label_result.numpy(), + pred_result.numpy(), + average=None))) + + table = pt.PrettyTable(["{} {}".format(title, epoch), 'F1', "Precision", "Recall"]) + table.add_row(["Label"] + ["{:3.4f}".format(x) for x in [f1, p, r]]) + table.add_row(["Entity"] + ["{:3.4f}".format(x) for x in [e_f1, e_p, e_r]]) + + self.config.logger.info("\n{}".format(table)) + return e_f1 + + def predict(self, data_loader, data): + self.model.eval() + result = [] + i = 0 + with torch.no_grad(): + for data_batch in data_loader: + sentence_batch = data[i:i+self.config.batch_size] + i += self.config.test_params['batch_size'] + + entity_text = data_batch[-1] + data_batch = [data.cuda() for data in data_batch[:-1]] + bert_inputs, grid_labels, grid_mask2d, pieces2word, dist_inputs, sent_length = data_batch + + outputs = self.model(bert_inputs, grid_mask2d, dist_inputs, pieces2word, sent_length) + length = sent_length + + grid_mask2d = grid_mask2d.clone() + + outputs = torch.argmax(outputs, -1) + _, _, _, decode_entities = self.processor.decode( + outputs.cpu().numpy(), entity_text, length.cpu().numpy()) + + for ent_list, sentence in zip(decode_entities, sentence_batch): + sentence = sentence["sentence"] + instance = {"sentence": sentence, "pred-entity-mentions": []} + for ent in ent_list: + instance["pred-entity-mentions"].append({ + "text": [sentence[x] for x in ent[0]], + "entity-type": self.config.vocab.id_to_label(ent[1]), + "index": ent[0], + # "start": ent[0][0], + # "end": ent[0][-1] + 1 + }) + result.append(instance) + + return result + + def save(self, path): + torch.save(self.model.state_dict(), path) + + def load(self, path): + self.model.load_state_dict(torch.load(path, map_location=torch.device('cpu'))) \ No newline at end of file diff --git a/task3/NER/input/Code and Named Entity Recognition in StackOverflow/dev.json b/task3/NER/input/Code and Named Entity Recognition in StackOverflow/dev.json new file mode 100644 index 0000000..5e524c0 --- /dev/null +++ b/task3/NER/input/Code and Named Entity Recognition in StackOverflow/dev.json @@ -0,0 +1 @@ +[{"sentence": ["Why", "does", ":"], "golden-entity-mentions": []}, {"sentence": ["not", "compile", "but", ":"], "golden-entity-mentions": []}, {"sentence": ["compiles", "."], "golden-entity-mentions": []}, {"sentence": ["In", "Java", "+", "=", "operator", "has", "an", "implicit", "cast", "to", "the", "left", "hand", "type", "."], "golden-entity-mentions": [{"text": ["Java"], "entity-type": "Language", "index": [1]}, {"text": ["+", "="], "entity-type": "Code_Block", "index": [2, 3]}]}, {"sentence": ["This", "goes", "for", "all", "composed", "operators", "."], "golden-entity-mentions": []}, {"sentence": ["As", "everyone", "already", "stated", ",", "the", "+", "=", "has", "an", "implicit", "cast", "."], "golden-entity-mentions": [{"text": ["+", "="], "entity-type": "Code_Block", "index": [6, 7]}]}, {"sentence": ["To", "help", "illustrate", "that", ",", "I", "'m", "going", "to", "use", "an", "app", "I", "wrote", "a", "while", "back", "that", "is", "perfect", "for", "these", "types", "of", "questions", "."], "golden-entity-mentions": []}, {"sentence": ["It", "'s", "an", "online", "disassembler", "so", "you", "can", "check", "out", "the", "actual", "bytecode", "that", "'s", "being", "produced", ":", "http://javabytes.herokuapp.com/"], "golden-entity-mentions": []}, {"sentence": ["And", "a", "table", "of", "their", "meanings", ":"], "golden-entity-mentions": [{"text": ["table"], "entity-type": "User_Interface_Element", "index": [2]}]}, {"sentence": ["http://en.wikipedia.org/wiki/Java_bytecode_instruction_listings"], "golden-entity-mentions": []}, {"sentence": ["So", "let", "'s", "take", "a", "look", "at", "the", "bytecode", "from", "some", "simple", "Java", "code", ":"], "golden-entity-mentions": [{"text": ["Java"], "entity-type": 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{"sentence": ["If", "I", "make", "some", "minor", "changes", "in", "the", "program", ",", "the", "address", "changes", ",", "but", "the", "difference", "between", "these", "two", "addresses", "remains", "344", "."], "golden-entity-mentions": [{"text": ["344"], "entity-type": "Value", "index": [22]}]}, {"sentence": ["This", "is", "all", "the", "more", "puzzling", ",", "since", "the", "size", "of", "all", "my", "three", "classes", "is", "less", "than", "100", "."], "golden-entity-mentions": [{"text": ["100"], "entity-type": "Value", "index": [18]}]}, {"sentence": ["The", "program", "crashes", "inside", "the", "void", "Problem", "::", "evaluate(PopulationMember&)const", ";", "method", "as", "soon", "as", "some", "logic", "is", "involved", "."], "golden-entity-mentions": [{"text": ["void", "Problem", "::", "evaluate(PopulationMember&)const", ";"], "entity-type": "Code_Block", "index": [5, 6, 7, 8, 9]}]}, {"sentence": ["EDIT", ":"], "golden-entity-mentions": []}, {"sentence": ["You", "are", "copying", "around", "Population-instances", "quite", "a", "bit", ":", "1", ".", "you", "are", "returning", "a", "local", "copy", "by", "value", ",", "2", ".", "copying", "again", "by", "assigning", "into", "another", "local", "with"], "golden-entity-mentions": [{"text": ["Population-instances"], "entity-type": "Class", "index": [4]}]}, {"sentence": ["All", "these", "instances", "get", "pointers", "to", "PopulationMember", "and", "ownsMembers_", "is", "always", "set", "true", "-", "this", "looks", "quite", "a", "bit", "fishy", "and", "you", "might", "want", "to", "debug", "with", "breakpoints", "in", "your", "destructors/constructors", "to", "find", "out", "the", "lifecycle", "of", "each", "population", "and", "its", "members", "."], "golden-entity-mentions": [{"text": ["pointers"], "entity-type": "Data_Type", "index": [4]}, {"text": ["PopulationMember"], "entity-type": "Class", "index": [6]}, {"text": ["ownsMembers_"], "entity-type": "Variable", "index": [8]}, {"text": ["true"], "entity-type": "Value", "index": [12]}, {"text": ["population"], "entity-type": "Class", "index": [38]}]}, {"sentence": ["EDIT", ":", "append", "method"], "golden-entity-mentions": []}, {"sentence": ["This", "means", "that", "the", "members", "to", "not", "point", "to", "the", "correct", "Population", "anymore", "!"], "golden-entity-mentions": [{"text": ["Population"], "entity-type": "Class", "index": [11]}]}, {"sentence": ["The", "value", "of", "Population&", "is", "stored", "on", "stack", "and", "gets", "deleted", "after", "the", "for", "loop", "ends", ",", "but", "the", "PopulationMembers", "still", "point", "to", "these", "Populations", "."], "golden-entity-mentions": [{"text": ["Population&"], "entity-type": "Class", "index": [3]}, {"text": ["stack"], "entity-type": "Data_Structure", "index": [7]}, {"text": ["for"], "entity-type": "Code_Block", "index": [13]}, {"text": ["PopulationMembers"], "entity-type": "Class", "index": [19]}, {"text": ["Populations"], "entity-type": "Class", "index": [24]}]}, {"sentence": ["Edit", ":", "Fix"], "golden-entity-mentions": []}, {"sentence": ["please", "try", "this", ":"], "golden-entity-mentions": []}, {"sentence": ["I", "'ve", "scoured", "SOF", "and", "google", "for", "an", "answer", "no", "joy"], "golden-entity-mentions": [{"text": ["SOF"], "entity-type": "Website", "index": [3]}, {"text": ["google"], "entity-type": "Website", "index": [5]}]}, {"sentence": ["I", "am", "able", "to", "connect", "to", "a", "database", "I", "presume", "in", "JavaScript", ",", "I", "wish", "to", "populate", "the", "results", "into", "a", "HTML", "forms", "option", "field", "."], "golden-entity-mentions": [{"text": ["JavaScript"], "entity-type": "Language", "index": [11]}, {"text": ["HTML"], "entity-type": "Language", "index": [21]}]}] \ No newline at end of file diff --git a/task3/NER/input/README.md b/task3/NER/input/README.md new file mode 100644 index 0000000..fc9f160 --- /dev/null +++ b/task3/NER/input/README.md @@ -0,0 +1,5 @@ +# 数据集 + +两个数据集，一个是我们自己打标签的 + +一个是[Code and Named Entity Recognition in StackOverflow - ACL Anthology](https://aclanthology.org/2020.acl-main.443/)这篇论文的 \ No newline at end of file diff --git a/task3/NER/input/dev.json b/task3/NER/input/dev.json new file mode 100644 index 0000000..956520f --- /dev/null +++ b/task3/NER/input/dev.json @@ -0,0 +1 @@ +[{"sentence": ["If", "you", "'re", "on", "Windows", ",", "rename", "the", "file", "by", "adding", "'.exe", "'", "on", "the", "end", "."], "golden-entity-mentions": [{"text": ["Windows"], "entity-type": "platform", "start": 13, "end": 19, "index": [4]}]}, {"sentence": ["Linux", ".", "Might", "work", "for", "Windows", "starting", "v2.3.2", "."], "golden-entity-mentions": [{"text": ["Linux"], "entity-type": "platform", "start": 0, "end": 4, "index": [0]}, {"text": ["Windows"], "entity-type": "platform", "start": 22, "end": 28, "index": [5]}]}, {"sentence": ["See", "the", "[", "LICENSE", "]", "(", "LICENSE", ")", "file", ",", "as", "well", "as", "our", "accompanying", "[", "Acceptable", "Use", "Policy", "]", "(", "USE_POLICY.md", ")"], "golden-entity-mentions": [{"text": ["LICENSE"], "entity-type": "license", "start": 9, "end": 15, "index": [3]}]}, {"sentence": ["TypeScript", "is", "JavaScript", "with", "static", "type", "checking", "and", "helps", "catch", "many", "silly", "bugs", "at", "code", "time", "."], "golden-entity-mentions": [{"text": ["TypeScript"], "entity-type": "language", "start": 0, "end": 9, "index": [0]}, {"text": ["JavaScript"], "entity-type": "language", "start": 14, "end": 23, "index": [2]}]}, {"sentence": ["OpenAI", "'s", "GPT-3", "model", ",", "demonstrating", "poetry", ",", "dialogue", ",", "puns", ",", "literary", "parodies", ",", "and", "storytelling", "."], "golden-entity-mentions": [{"text": ["OpenAI"], "entity-type": "company", "start": 0, "end": 5, "index": [0]}, {"text": ["GPT-3"], "entity-type": "company", "start": 9, "end": 13, "index": [2]}]}, {"sentence": ["Microsoft", "and", "any", "contributors", "grant", "you", "a", "license", "to", "the", "Microsoft", "documentation", "and", "other", "content", "in", "this", "repository", "under", "the", "[", "Creative", "Commons", "Attribution", "4.0", "International", "Public", "License", "]", "(", "https", ":", "//creativecommons.org/licenses/by/4.0/legalcode", ")", ",", "see", "the", "[", "LICENSE", "]", "(", "https", ":", "//chatgpt.com/c/LICENSE", ")", "file", ",", "and", "grant", "you", "a", "license", "to", "any", "code", "in", "the", "repository", "under", "the", "[", "MIT", "License", "]", "(", "https", ":", "//opensource.org/licenses/MIT", ")", ",", "see", "the", "[", "LICENSE-CODE", "]", "(", "https", ":", "//chatgpt.com/c/LICENSE-CODE", ")", "file", "."], "golden-entity-mentions": [{"text": ["Creative", "Commons", "Attribution", "4.0", "International", "Public", "License"], "entity-type": "license", "start": 130, "end": 190, "index": [21, 22, 23, 24, 25, 26, 27]}, {"text": ["MIT", "License"], "entity-type": "license", "start": 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"entity-type": "platform", "start": 14, "end": 18, "index": [4]}]}, {"sentence": ["Apache", "Redis", "Pub-Sub"], "golden-entity-mentions": [{"text": ["Apache", "Redis", "Pub-Sub"], "entity-type": "Framework", "start": 0, "end": 19, "index": [0, 1, 2]}]}, {"sentence": ["You", "can", "install", "ComfyUI", "in", "Apple", "Mac", "silicon", "(", "M1", "or", "M2", ")", "with", "any", "recent", "macOS", "version", "."], "golden-entity-mentions": [{"text": ["macOS"], "entity-type": "Platform", "start": 72, "end": 76, "index": [16]}, {"text": ["Apple"], "entity-type": "Company", "start": 27, "end": 31, "index": [5]}]}, {"sentence": ["This", "project", "is", "licensed", "under", "the", "MIT", "License", "."], "golden-entity-mentions": [{"text": ["MIT", "License"], "entity-type": "license", "start": 35, "end": 45, "index": [6, 7]}]}, {"sentence": ["If", "you", "love", "it", ",", "consider", "supporting", "its", "development", "via", "GitHub", "Sponsors", "or", "Patreon", "."], "golden-entity-mentions": [{"text": ["GitHub"], "entity-type": "company", "start": 56, "end": 61, "index": [10]}]}, {"sentence": ["This", "repository", "is", "licensed", "under", "the", "Apache", "License", "v2.0", "with", "LLVM", "Exceptions", "(", "see", "the", "LLVM", "[", "License", "]", "(", "https", ":", "//llvm.org/LICENSE.txt", ")", ")", "."], "golden-entity-mentions": [{"text": ["Apache", "License", "v2.0"], "entity-type": "license", "start": 38, "end": 56, "index": [6, 7, 8]}]}, {"sentence": ["Compared", "to", "hosted", "services", "such", "as", "Github", "Codespaces", ",", "JetBrains", "Spaces", ",", "or", "Google", "Cloud", "Workstations", ",", "DevPod", "has", "the", "following", "advantages", ":"], "golden-entity-mentions": [{"text": ["Google"], "entity-type": "company", "start": 76, "end": 81, "index": [13]}]}, {"sentence": ["[", "Follow", "me", "on", "Twitter", "for", "updates", "]", "(", "https", ":", "//twitter.com/_abi_", ")", "."], "golden-entity-mentions": 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"end": 38, "index": [5]}, {"text": ["Linux"], "entity-type": "platform", "start": 41, "end": 45, "index": [7]}, {"text": ["macOS"], "entity-type": "platform", "start": 52, "end": 56, "index": [10]}, {"text": ["PowerShell"], "entity-type": "framework", "start": 0, "end": 9, "index": [0]}]}, {"sentence": ["Ruff", "'s", "import", "resolver", "is", "based", "on", "the", "import", "resolution", "algorithm", "from", "Pyright", "."], "golden-entity-mentions": [{"text": ["Pyright"], "entity-type": "company", "start": 72, "end": 78, "index": [12]}]}, {"sentence": ["Then", "\ud83e\udd17", "Accelerate", "can", "be", "installed", "using", "pip", "as", "follows", ":", "pip", "install", "accelerate"], "golden-entity-mentions": [{"text": ["pip"], "entity-type": "language", "start": 41, "end": 43, "index": [7]}, {"text": ["pip"], "entity-type": "language", "start": 41, "end": 43, "index": [7]}]}, {"sentence": ["Maintainers", ":", "The", "project", "is", "currently", "being", "maintained", "by", "[", "Christian", "Brabandt", "]", "[", "42", "]", "and", "[", "Bailey", "Ling", "]", "[", "41", "]", "."], "golden-entity-mentions": [{"text": ["Christian", "Brabandt"], "entity-type": "company", "start": 59, "end": 76, "index": [10, 11]}, {"text": ["Bailey", "Ling"], "entity-type": "company", "start": 88, "end": 98, "index": [18, 19]}]}, {"sentence": ["The", "model", "is", "licensed", "under", "the", "Apache", "2.0", "license", "."], "golden-entity-mentions": [{"text": ["Apache", "2.0"], "entity-type": "license", "start": 32, "end": 41, "index": [6, 7]}]}, {"sentence": ["npm", "install", "react", "react-dom", "@", "excalidraw/excalidraw"], "golden-entity-mentions": [{"text": ["react"], "entity-type": "framework", "start": 12, "end": 16, "index": [2]}]}, {"sentence": ["Our", "frontend", "is", "a", "Next.JS", "app", "and", "is", "hosted", "on", "Vercel", "."], "golden-entity-mentions": [{"text": ["Vercel"], "entity-type": "company", "start": 47, "end": 52, "index": [10]}]}, {"sentence": ["The", "most", "powerful", "and", "modular", "stable", "diffusion", "GUI", "and", "backend", "."], "golden-entity-mentions": [{"text": ["GUI"], "entity-type": "Language", "start": 47, "end": 49, "index": [7]}]}, {"sentence": ["They", "include", "an", ".rmd", "extension", "that", "represents", "an", "*", "*", "R", "Markdown", "*", "*", "file", "which", "can", "be", "simply", "defined", "as", "an", "embedding", "of", "`", "code", "chunks", "`", "(", "of", "R", "or", "other", "languages", ")", "and", "a", "`", "YAML", "header", "`", "(", "that", "guides", "how", "to", "format", "outputs", "such", "as", "PDF", ")", "in", "a", "`", "Markdown", "document", "`", "."], "golden-entity-mentions": [{"text": ["R", "Markdown"], "entity-type": "language", "start": 52, "end": 61, "index": [10, 11]}]}, {"sentence": ["Integrated", "bundler", "for", "deploying", "to", "the", "web", ",", "macOS", ",", "Linux", ",", "and", "Windows", "."], "golden-entity-mentions": [{"text": ["Linux"], "entity-type": "platform", "start": 52, "end": 56, "index": [10]}]}, {"sentence": ["What", "is", "Spark"], "golden-entity-mentions": [{"text": ["Spark"], "entity-type": "Language", "start": 8, "end": 12, "index": [2]}]}, {"sentence": ["Read", "our", "[", "*", "*", "Contributing", "Guide", "*", "*", "]", "[", "contribute", "]", "to", "learn", "about", "our", "development", "process", ",", "how", "to", "propose", "bugfixes", "and", "improvements", ",", "and", "how", "to", "build", "and", "test", "your", "changes", "to", "React", "Native", "."], "golden-entity-mentions": [{"text": ["Contributing", "Guide"], "entity-type": "documentation", "start": 12, "end": 29, "index": [5, 6]}, {"text": ["React", "Native"], "entity-type": "framework", "start": 170, "end": 181, "index": [36, 37]}]}, {"sentence": ["use", "beforehand", "acknowledge", "Community", "Proxy"], "golden-entity-mentions": [{"text": ["Community", "Proxy"], "entity-type": "platform", "start": 27, "end": 41, "index": [3, 4]}]}] \ No newline at end of file diff --git a/task3/NER/structure.pptx b/task3/NER/structure.pptx new file mode 100644 index 0000000..c596535 Binary files /dev/null and b/task3/NER/structure.pptx differ diff --git a/task3/NER/utils/APIs/APIDataset.py b/task3/NER/utils/APIs/APIDataset.py new file mode 100644 index 0000000..bff67fc --- /dev/null +++ b/task3/NER/utils/APIs/APIDataset.py @@ -0,0 +1,59 @@ +''' +Dataset api: 与api_encode配合, 将api_encode的返回结果构造成Dataset方便Pytorch调用 + tips: + 注意如果数据长度不一需要编写collate_fn函数, 若无则将collate_fn设为None +''' + +import torch +import numpy as np +from torch.utils.data import Dataset +from torch.nn.utils.rnn import pad_sequence + +class APIDataset(Dataset): + def __init__(self, bert_inputs, grid_labels, grid_mask2d, pieces2word, dist_inputs, sent_length, entity_text): + self.bert_inputs = bert_inputs + self.grid_labels = grid_labels + self.grid_mask2d = grid_mask2d + self.pieces2word = pieces2word + self.dist_inputs = dist_inputs + self.sent_length = sent_length + self.entity_text = entity_text + + def __getitem__(self, item): + return torch.LongTensor(self.bert_inputs[item]), \ + torch.LongTensor(self.grid_labels[item]), \ + torch.LongTensor(self.grid_mask2d[item]), \ + torch.LongTensor(self.pieces2word[item]), \ + torch.LongTensor(self.dist_inputs[item]), \ + self.sent_length[item], \ + self.entity_text[item] + + def __len__(self): + return len(self.bert_inputs) + + # collate_fn = None + + def collate_fn(self, data): + bert_inputs, grid_labels, grid_mask2d, pieces2word, dist_inputs, sent_length, entity_text = map(list, zip(*data)) + + max_tok = np.max(sent_length) + sent_length = torch.LongTensor(sent_length) + max_pie = np.max([x.shape[0] for x in bert_inputs]) + bert_inputs = pad_sequence(bert_inputs, True) + batch_size = bert_inputs.size(0) + + def fill(data, new_data): + for j, x in enumerate(data): + new_data[j, :x.shape[0], :x.shape[1]] = x + return new_data + + dis_mat = torch.zeros((batch_size, max_tok, max_tok), dtype=torch.long) + dist_inputs = fill(dist_inputs, dis_mat) + labels_mat = torch.zeros((batch_size, max_tok, max_tok), dtype=torch.long) + grid_labels = fill(grid_labels, labels_mat) + mask2d_mat = torch.zeros((batch_size, max_tok, max_tok), dtype=torch.bool) + grid_mask2d = fill(grid_mask2d, mask2d_mat) + sub_mat = torch.zeros((batch_size, max_tok, max_pie), dtype=torch.bool) + pieces2word = fill(pieces2word, sub_mat) + + return bert_inputs, grid_labels, grid_mask2d, pieces2word, dist_inputs, sent_length, entity_text \ No newline at end of file diff --git a/task3/NER/utils/APIs/APIDecode.py b/task3/NER/utils/APIs/APIDecode.py new file mode 100644 index 0000000..189b558 --- /dev/null +++ b/task3/NER/utils/APIs/APIDecode.py @@ -0,0 +1,68 @@ +''' +decode api: 将model预测出的数据转变成理想的格式 + tips: + 需要与Model配合, Model forward的输出即此api输入 +''' + +from collections import defaultdict, deque + +def convert_index_to_text(index, type): + text = "-".join([str(i) for i in index]) + text = text + "-#-{}".format(type) + return text + +def convert_text_to_index(text): + index, type = text.split("-#-") + index = [int(x) for x in index.split("-")] + return index, int(type) + +def api_decode(outputs, entities, length): + + class Node: + def __init__(self): + self.THW = [] # [(tail, type)] + self.NNW = defaultdict(set) # {(head,tail): {next_index}} + + ent_r, ent_p, ent_c = 0, 0, 0 + decode_entities = [] + q = deque() + for instance, ent_set, l in zip(outputs, entities, length): + predicts = [] + nodes = [Node() for _ in range(l)] + for cur in reversed(range(l)): + heads = [] + for pre in range(cur+1): + # THW + if instance[cur, pre] > 1: + nodes[pre].THW.append((cur, instance[cur, pre])) + heads.append(pre) + # NNW + if pre < cur and instance[pre, cur] == 1: + # cur node + for head in heads: + nodes[pre].NNW[(head,cur)].add(cur) + # post nodes + for head,tail in nodes[cur].NNW.keys(): + if tail >= cur and head <= pre: + nodes[pre].NNW[(head,tail)].add(cur) + # entity + for tail,type_id in nodes[cur].THW: + if cur == tail: + predicts.append(([cur], type_id)) + continue + q.clear() + q.append([cur]) + while len(q) > 0: + chains = q.pop() + for idx in nodes[chains[-1]].NNW[(cur,tail)]: + if idx == tail: + predicts.append((chains + [idx], type_id)) + else: + q.append(chains + [idx]) + + predicts = set([convert_index_to_text(x[0], x[1]) for x in predicts]) + decode_entities.append([convert_text_to_index(x) for x in predicts]) + ent_r += len(ent_set) + ent_p += len(predicts) + ent_c += len(predicts.intersection(ent_set)) + return ent_c, ent_p, ent_r, decode_entities \ No newline at end of file diff --git a/task3/NER/utils/APIs/APIEncode.py b/task3/NER/utils/APIs/APIEncode.py new file mode 100644 index 0000000..42f2d82 --- /dev/null +++ b/task3/NER/utils/APIs/APIEncode.py @@ -0,0 +1,115 @@ +''' +encode api: 将原始data数据转化成APIDataset所需要的数据 + tips: + 可以不使用接口的tokenizer, 只需在api_decode中对应修改为自己的tokenizer即可 + 需要调用vocab的add_label方法扩容vocab +''' + +import numpy as np + +class Keys: + sentence = 'sentence' + entity_mentions = 'golden-entity-mentions' + text = 'text' + type = 'entity-type' + start = 'start' + end = 'end' + index = 'index' + +dis2idx = np.zeros((1000), dtype='int64') +dis2idx[1] = 1 +dis2idx[2:] = 2 +dis2idx[4:] = 3 +dis2idx[8:] = 4 +dis2idx[16:] = 5 +dis2idx[32:] = 6 +dis2idx[64:] = 7 +dis2idx[128:] = 8 +dis2idx[256:] = 9 + +def convert_index_to_text(index, type): + text = "-".join([str(i) for i in index]) + text = text + "-#-{}".format(type) + return text + +def fill_vocab(vocab, dataset): + entity_num = 0 + for instance in dataset: + if instance.get(Keys.entity_mentions, None) is not None: + for entity in instance[Keys.entity_mentions]: + vocab.add_label(entity[Keys.type]) + entity_num += len(instance[Keys.entity_mentions]) + return entity_num + +def api_encode(data, tokenizer, vocab): + bert_inputs = [] + grid_labels = [] + grid_mask2d = [] + dist_inputs = [] + entity_text = [] + pieces2word = [] + sent_length = [] + + fill_vocab(vocab, data) + + for idx, instance in enumerate(data): + sentence = instance[Keys.sentence] + if len(sentence) == 0 or len(sentence) >= 512: + continue + + tokens = [tokenizer.tokenize(word) for word in sentence] + pieces = [piece for pieces in tokens for piece in pieces] + _bert_inputs = tokenizer.convert_tokens_to_ids(pieces) + _bert_inputs = np.array([tokenizer.cls_token_id] + _bert_inputs + [tokenizer.sep_token_id]) + + length = len(sentence) + _grid_labels = np.zeros((length, length), dtype=np.int64) + _pieces2word = np.zeros((length, len(_bert_inputs)), dtype=bool) + _dist_inputs = np.zeros((length, length), dtype=np.int64) + _grid_mask2d = np.ones((length, length), dtype=bool) + + if tokenizer is not None: + start = 0 + for i, pieces in enumerate(tokens): + if len(pieces) == 0: + continue + pieces = list(range(start, start + len(pieces))) + _pieces2word[i, pieces[0] + 1:pieces[-1] + 2] = 1 + start += len(pieces) + + for k in range(length): + _dist_inputs[k, :] += k + _dist_inputs[:, k] -= k + + for i in range(length): + for j in range(length): + if _dist_inputs[i, j] < 0: + _dist_inputs[i, j] = dis2idx[-_dist_inputs[i, j]] + 9 + else: + _dist_inputs[i, j] = dis2idx[_dist_inputs[i, j]] + _dist_inputs[_dist_inputs == 0] = 19 + + _entity_text = [] + if instance.get(Keys.entity_mentions, None) is not None: + for entity in instance[Keys.entity_mentions]: + # b, e, type = entity[Keys.start], entity[Keys.end], entity[Keys.type] + # if b < 0: continue + # if e > len(sentence): e = len(sentence) + index, type = entity['index'], entity[Keys.type] + for i in range(len(index)): + if i + 1 >= len(index): + break + _grid_labels[index[i], index[i + 1]] = vocab.label_to_id(vocab.SUC) + _grid_labels[index[-1], index[0]] = vocab.label_to_id(type) + _entity_text.append(convert_index_to_text(index, vocab.label_to_id(type))) + _entity_text = set(_entity_text) + + sent_length.append(length) + bert_inputs.append(_bert_inputs) + grid_labels.append(_grid_labels) + grid_mask2d.append(_grid_mask2d) + dist_inputs.append(_dist_inputs) + pieces2word.append(_pieces2word) + entity_text.append(_entity_text) + + return bert_inputs, grid_labels, grid_mask2d, pieces2word, dist_inputs, sent_length, entity_text \ No newline at end of file diff --git a/task3/NER/utils/APIs/__init__.py b/task3/NER/utils/APIs/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/task3/NER/utils/DataProcess.py b/task3/NER/utils/DataProcess.py new file mode 100644 index 0000000..9917dd6 --- /dev/null +++ b/task3/NER/utils/DataProcess.py @@ -0,0 +1,68 @@ +''' +data process: 数据处理, 包括 标签Vocab 和 数据处理类 + tips: + 其中标签Vocab实例化对象必须在api_encode中被调用(add_label) +''' + +from torch.utils.data import DataLoader +from transformers import AutoTokenizer + +from APIs.APIDataset import APIDataset +from APIs.APIEncode import api_encode +from APIs.APIDecode import api_decode + +import os +os.environ["TOKENIZERS_PARALLELISM"] = "false" + + +class LabelVocabulary(object): + PAD = '' + UNK = '' + SUC = '' # 表连续随后的 + + def __init__(self): + self.label2id = {self.PAD: 0, self.SUC: 1} + self.id2label = {0: self.PAD, 1: self.SUC} + + def add_label(self, label): + if label not in self.label2id: + self.label2id[label] = len(self.label2id) + self.id2label[self.label2id[label]] = label + + assert label == self.id2label[self.label2id[label]] + + def __len__(self): + return len(self.label2id) + + def label_to_id(self, label): + return self.label2id[label] + + def id_to_label(self, i): + return self.id2label[i] + + +class Processor: + def __init__(self, config) -> None: + self.config = config + self.tokenizer = AutoTokenizer.from_pretrained(config.bert_name, cache_dir=config.cache_path) + self.vocab = LabelVocabulary() + + def __call__(self, data, params): + return self.to_loader(data, params) + + def encode(self, data): + return api_encode(data, self.tokenizer, self.vocab) + + def decode(self, outputs, entities, length): + return api_decode(outputs, entities, length) + + def to_dataset(self, data): + dataset_inputs = self.encode(data) + self.config.label_num = len(self.vocab.label2id) + self.config.vocab = self.vocab + return APIDataset(*dataset_inputs) + + def to_loader(self, data, params): + dataset = self.to_dataset(data) + return DataLoader(dataset=dataset, **params, collate_fn=dataset.collate_fn, num_workers=2) + diff --git a/task3/NER/utils/__init__.py b/task3/NER/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/task3/NER/utils/common.py b/task3/NER/utils/common.py new file mode 100644 index 0000000..13b4baa --- /dev/null +++ b/task3/NER/utils/common.py @@ -0,0 +1,38 @@ +''' +常用一般的utils +''' +import logging +import time +import json + +def get_logger(dataset): + pathname = "./log/{}_{}.txt".format(dataset, time.strftime("%m-%d_%H-%M-%S")) + logger = logging.getLogger() + logger.setLevel(logging.INFO) + formatter = logging.Formatter("%(asctime)s - %(levelname)s: %(message)s", + datefmt='%Y-%m-%d %H:%M:%S') + + file_handler = logging.FileHandler(pathname) + file_handler.setLevel(logging.DEBUG) + file_handler.setFormatter(formatter) + + stream_handler = logging.StreamHandler() + stream_handler.setLevel(logging.DEBUG) + stream_handler.setFormatter(formatter) + + logger.addHandler(file_handler) + logger.addHandler(stream_handler) + + return logger + +def read_from_file(path): + with open(path, encoding='utf-8') as f: + data = json.load(f) + f.close() + return data + +def write_to_file(path, output): + with open(path, 'w', encoding='utf-8') as f: + json.dump(output, f, indent=4, ensure_ascii=False) + f.close() + diff --git a/task3/README.md b/task3/README.md new file mode 100644 index 0000000..54ea4ad --- /dev/null +++ b/task3/README.md @@ -0,0 +1,5 @@ +# 运行 + +运行最好在colab上 + +然后两个数据集的model.pt可以私下发送，400MB，GitHub不好传 \ No newline at end of file