NER-BERT-pytorch
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lemonhu
Project dependencies may have API risk issues
Hi, In NER-BERT-pytorch, inappropriate dependency versioning constraints can cause risks.
Below are the dependencies and version constraints that the project is using
tensorflow>=1.11.0
torch>=0.4.1
tqdm
pytorch-pretrained-bert==0.4.0
apex
The version constraint == will introduce the risk of dependency conflicts because the scope of dependencies is too strict. The version constraint No Upper Bound and * will introduce the risk of the missing API Error because the latest version of the dependencies may remove some APIs.
After further analysis, in this project, The version constraint of dependency tqdm can be changed to >=4.36.0,<=4.64.0. The version constraint of dependency pytorch-pretrained-bert can be changed to >=0.3.0,<=0.6.2.
The above modification suggestions can reduce the dependency conflicts as much as possible, and introduce the latest version as much as possible without calling Error in the projects.
The invocation of the current project includes all the following methods.
The calling methods from the tqdm
tqdm.trange
The calling methods from the pytorch-pretrained-bert
pytorch_pretrained_bert.BertForTokenClassification
The calling methods from the all methods
torch.nn.DataParallel.to sentences.append metrics.classification_report print numpy.argmax list str loss.mean.backward self.__dict__.update collections.defaultdict.items tqdm.trange file.write ps.append data_loader.DataLoader.data_iterator zip json.dump chunks.append torch.nn.DataParallel.train filter set torch.nn.DataParallel any model tqdm.trange.set_postfix torch.nn.DataParallel.parameters rs.append logging.info self.load_tags.append self.tag2idx.get logging.getLogger.addHandler torch.tensor.to file_sentences.write metrics.items batch_output.detach.cpu.numpy.detach logging.getLogger.setLevel self.load_tags s.append logging.StreamHandler isinstance collections.defaultdict utils.load_checkpoint numpy.average train_and_evaluate torch.optim.Adam file_tags.write torch.nn.DataParallel.half torch.save get_entities train hasattr float words.append format enumerate utils.RunningAverage.update sum utils.RunningAverage logging.StreamHandler.setFormatter set.append pytorch_pretrained_bert.BertTokenizer.from_pretrained torch.cuda.device_count open batch_output.detach.cpu.numpy range utils.Params pytorch_pretrained_bert.BertConfig.from_json_file line.strip.split self.tokenizer.tokenize utils.set_logger model.load_state_dict torch.cuda.manual_seed_all pytorch_pretrained_bert.BertForTokenClassification pred_tags.extend ImportError e.d2.add torch.optim.lr_scheduler.LambdaLR build_tags batch_tags.to.numpy.to row_fmt.format torch.optim.Adam.backward min logging.getLogger evaluate.evaluate logging.FileHandler apex.optimizers.FusedAdam join json.load max shutil.copyfile chunk.split dataset.append random.seed model.classifier.named_parameters loss_avg argparse.ArgumentParser.parse_args hasattr.state_dict data_loader.DataLoader torch.nn.DataParallel.named_parameters apex.optimizers.FP16_Optimizer e.d1.add ValueError numpy.ones data_loader.DataLoader.load_data true_tags.extend logging.FileHandler.setFormatter argparse.ArgumentParser.add_argument self.tokenizer.convert_tokens_to_ids torch.load torch.optim.lr_scheduler.LambdaLR.step next torch.nn.DataParallel.zero_grad load_dataset save_dataset end_of_chunk torch.nn.DataParallel.eval line.strip.strip start_of_chunk optimizer_to_save.state_dict torch.manual_seed optimizer.load_state_dict f1s.append os.path.join pytorch_pretrained_bert.BertForTokenClassification.from_pretrained batch_tags.to.numpy self.load_sentences_tags logging.Formatter numpy.sum len.format torch.device line.strip os.path.isfile batch_data.gt set.update idx2tag.get os.mkdir evaluate utils.save_checkpoint argparse.ArgumentParser tag.strip loss.mean.mean loss.mean.item torch.nn.utils.clip_grad_norm_ random.shuffle torch.optim.Adam.step os.path.exists metrics.f1_score len torch.cuda.is_available torch.tensor batch_output.detach.cpu os.makedirs
@developer Could please help me check this issue? May I pull a request to fix it? Thank you very much.
