diff --git a/data_utils.py b/data_utils.py index 22cb7c2..1edbe18 100644 --- a/data_utils.py +++ b/data_utils.py @@ -110,6 +110,8 @@ def convert_examples_to_features( hp_label_ids = [] for word, label, hp_label in zip(example.words, example.labels, example.hp_labels): word_tokens = tokenizer.tokenize(word) + if(len(word_tokens) == 0): + continue tokens.extend(word_tokens) # Use the real label id for the first token of the word, and padding ids for the remaining tokens label_ids.extend([label] + [pad_token_label_id] * (len(word_tokens) - 1)) @@ -216,7 +218,7 @@ def convert_examples_to_features( return features -def load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, mode): +def load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, mode, remove_labels=False): if args.local_rank not in [-1, 0] and not evaluate: torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache @@ -267,6 +269,9 @@ def load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, mode): all_label_ids = torch.tensor([f.label_ids for f in features], dtype=torch.long) all_full_label_ids = torch.tensor([f.full_label_ids for f in features], dtype=torch.long) all_hp_label_ids = torch.tensor([f.hp_label_ids for f in features], dtype=torch.long) + if remove_labels: + all_full_label_ids.fill_(pad_token_label_id) + all_hp_label_ids.fill_(pad_token_label_id) all_ids = torch.tensor([f for f in range(len(features))], dtype=torch.long) dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids, all_full_label_ids, all_hp_label_ids, all_ids) @@ -356,4 +361,4 @@ def get_chunks(seq, tags): if __name__ == '__main__': - save(args) \ No newline at end of file + save(args) diff --git a/dataset/.gitignore b/dataset/.gitignore new file mode 100644 index 0000000..0b72fc6 --- /dev/null +++ b/dataset/.gitignore @@ -0,0 +1 @@ +*/cached_* diff --git a/dataset/BC5CDR-chem/dev.json b/dataset/BC5CDR-chem/dev.json new file mode 100644 index 0000000..7afe854 --- /dev/null +++ b/dataset/BC5CDR-chem/dev.json @@ -0,0 +1 @@ +[{"str_words": ["22", "-", "oxacalcitriol", "suppresses", "secondary", "hyperparathyroidism", "without", "inducing", "low", "bone", "turnover", "in", "dogs", "with", "renal", "failure", "."], "tags": [1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}, {"str_words": ["BACKGROUND", ":", "Calcitriol", "therapy", "suppresses", "serum", "levels", "of", "parathyroid", "hormone", "(", "PTH", ")", "in", "patients", "with", "renal", "failure", "but", "has", "several", "drawbacks", ",", "including", "hypercalcemia", "and", "/", "or", "marked", "suppression", "of", "bone", "turnover", ",", "which", "may", "lead", "to", "adynamic", "bone", "disease", "."], "tags": [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}, {"str_words": ["A", "new", "vitamin", 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["Diazepam", "modification", "of", "evoked", "and", "spontaneous", "lateral", "geniculate", "activity", "."], "tags": [1, 0, 0, 0, 0, 0, 0, 0, 0, 0]}, {"str_words": ["In", "the", "majority", "of", "P", "cells", "tested", "both", "spontaneous", "and", "evoked", "activity", "were", "suppressed", "following", "diazepam", "treatment", "with", "these", "effects", "being", "altered", "little", "by", "a", "pentylenetetrazol", "(", "Metrazol", ")", "challenge", "."], "tags": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]}, {"str_words": ["I", "cell", "spontaneous", "activity", "was", "suppressed", "by", "diazepam", "and", "augmented", "by", "the", "Metrazol", "challenge", ";", "however", ",", "post", "-", "stimulus", "activity", "was", "relatively", "unaffected", "by", "either", "treatment", "."], "tags": [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}, {"str_words": ["The", "androgen", "concentration", "within", "both", "types", "of", "glands", "was", "nearly", "4", "-", "fold", "that", "in", "the", "blood", "but", "bore", "no", "direct", "relationship", "to", "the", "blood", "level", "."], "tags": [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}, {"str_words": ["The", "most", "striking", "finding", "was", "that", ",", "in", "spite", "of", "a", "25", ".", "5", "%", "less", "concentrated", "pool", "of", "apparent", "free", "testosterone", "in", "the", "blood", ",", "the", "level", "of", "T", "and", "its", "metabolites", "in", "the", "cancer", "tissue", "was", "29", "%", "above", "that", "in", "the", "samples", "of", "benign", "hypertrophic", "prostate", "."], "tags": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}, {"str_words": ["Paromomycin", "binds", "specifically", "to", "a", "single", "type", "of", "binding", "site", "on", "the", "70", "-", "S", "streptomycin", "-", "sensitive", "Escherichia", "coli", "ribosome", "."], "tags": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0]}, {"str_words": ["Paromomycin", "binding", ",", "unlike", "dihydrostreptomycin", "binding", ",", "is", "independent", "of", "changes", "in", "ribosome", "concentration", "but", "influenced", "by", "magnesium", "ion", "concentration", "."], "tags": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0]}, {"str_words": ["This", "induction", "does", "not", "occur", "with", "streptomycin", "-", "resistant", "ribosomes", "."], "tags": [0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0]}, {"str_words": ["Neither", "gentamicin", "nor", "kanamycin", "derivatives", ",", "which", "induce", "a", "high", "level", "of", "misreading", ",", "nor", "kasugamycin", "and", "spectinomycin", ",", "which", "do", "not", "induce", "misreading", ",", "compete", "for", "paromomycin", "or", "dihydrostreptomycin", "binding", "sites", "."], "tags": [0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}, {"str_words": ["Dipeptides", "L", "-", "phenylalanyl", "-", "proline", ",", "D", "-", "phenylalanyl", "-", "proline", ",", "prolyl", "-", "valine", ",", "valyl", "-", "lysine", ",", "lysyl", "-", "leucine", "and", "leucyl", "-", "phenylalanine", ",", "derived", "from", "the", "sequence", "of", "gramicidin", "S", ",", "are", "substrates", "of", "the", "gramicidin", "S", "synthetase", "."], "tags": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}, {"str_words": ["Time", "course", "of", "loss", "of", "dietary", "feedback", "control", "of", "cholesterol", "synthesis", "during", "carcinogen", "treatment", "."], "tags": [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]}, {"str_words": ["A", "suspension", "of", "granulocytes", "from", "calves", "sensitised", "to", "horse", "plasma", "released", "histamine", "when", "exposed", "to", "the", "antigen", "."], "tags": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0]}, {"str_words": ["5", "-", "HT", "is", "known", "to", "be", "released", "from", "bovine", "lungs", "in", "vitro", ",", "and", "may", "regulate", "the", "release", "of", "histamine", "in", "vivo", "during", "anaphylaxis", "."], "tags": [1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]}, {"str_words": ["Influence", "of", "steroid", "-", "receptor", "complexes", "on", "transcription", "by", "human", "hypertrophied", "prostatic", "RNA", "polymerases", "."], "tags": [0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}, {"str_words": ["5alpha", "-", "Dihydrotestoterone", "-", "receptor", "complexes", "were", "transferred", "into", "prostatic", "chromatin", "both", "steroid", "-", "and", "tissue", "-", "specifically", "."], "tags": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0]}, {"str_words": ["With", "a", "number", "of", "specimens", ",", "however", ",", "there", "was", "considerable", "variation", "in", "accessible", "cytoplasmic", "receptor", "sites", ",", "uptake", "of", "steroid", "-", "receptor", "complexes", "by", "chromatin", "preparations", ",", "the", "template", "activity", "of", "the", "chromatin", "and", "its", "response", "to", "steroid", "-", "receptor", "stimulation", "."], "tags": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0]}, {"str_words": ["Cellular", "localization", "of", "intestinal", "calcium", "-", "binding", "protein", "in", "pig", "duodenum", "."], "tags": [0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0]}, {"str_words": ["Our", "studies", "lend", "support", "to", "the", "hypothesis", "that", "CaBP", "may", "function", "in", "intracellular", "calcium", "transport", "."], "tags": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0]}, {"str_words": ["The", "titers", "of", "hepatitis", "B", "surface", "antigen", "(", "HBsAg", ")", "were", "found", "to", "decrease", "by", "greater", "than", "50", "%", "before", "death", "."], "tags": [0, 0, 0, 1, 2, 2, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}, {"str_words": ["Influence", "of", "bovine", "TSH", "on", "plasma", "thyroxine", "levels", "and", "thyroid", "function", "in", "brook", "trout", ",", "Salvelinus", "fontinalis", "(", "Mitchill", ")", "."], "tags": [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}, {"str_words": ["In", "this", "paper", "the", "reaction", "of", "tryptophan", "2", ",", "3", "-", "dioxygenase", "is", "further", "analyzed", "."], "tags": [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]}, {"str_words": ["Under", "these", "conditions", "an", "induction", "of", "the", "enzyme", "can", "already", "be", "registered", "in", "the", "presence", "of", "12", ".", "5", "mg", "L", "-", "tryptophan", "/", "kg", "."], "tags": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0]}, {"str_words": ["3", ")", "Experiments", "with", "antibodies", "in", "animals", "treated", "with", "a", "low", "dosis", "of", "cortisone", "show", "that", "L", "-", "tryptophan", "acts", "mainly", "via", "enzyme", "degradation", "or", "the", "saturation", "with", "the", "coenzyme", "hematin", ",", "respectively", "."], "tags": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}, {"str_words": ["Immunochemical", "study", "of", "DNA", "modified", "by", "aminomethylol", "amino", "acid", "derivatives", "."], "tags": [0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0]}, {"str_words": ["Sensitized", "bovine", "granulocytes", "release", "histamine", "when", "exposed", "to", "specific", "antigen", "."], "tags": [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0]}, {"str_words": ["Adrenaline", ",", "which", "is", "generally", "considered", "to", "be", "a", "physiological", "antagonist", "of", "the", "anaphylactic", "reaction", ",", "potentiated", "histamine", "release", "in", "this", "study", "."], "tags": [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]}, {"str_words": ["[", "Blood", "regeneration", "after", "blood", "substitution", "with", "dextran", "60", "or", "hydroxyethyl", "starch", "of", "an", "identical", "volume", "]", "."], "tags": [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}, {"str_words": ["Two", "clones", ",", "NB", "-", "2A", "and", "N", "-", "4", ",", "could", "be", "induced", "by", "treatment", "with", "5", "-", "iododeoxyuridine", "and", "dexamethasone", "phosphate", "to", "produce", "B", "-", "tropic", "type", "-", "C", "virus", ",", "but", "clone", "N", "-", "18", "failed", "to", "release", "either", "ecotropic", "or", "xenotropic", "virus", "under", "several", "different", "induction", "conditions", "."], "tags": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}, {"str_words": ["Dissociation", "and", "denaturation", "behaviour", "of", "sesame", "alpha", "-", "globulin", "in", "sodium", "dodecyl", "sulphate", "solution", "."], "tags": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0]}, {"str_words": ["The", "difference", "spectrum", "of", "the", "protein", "indicates", "that", "both", "tryptophan", "and", "tyrosine", "groups", "have", "been", "affected", "by", "the", "detergent", "."], "tags": [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0]}, {"str_words": ["Uptake", "and", "scintiscanning", "of", "the", "prostate", "with", "131I", "-", "labeled", "estradiol", "phosphate", "."], "tags": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0]}] \ No newline at end of file diff --git a/eval.py b/eval.py index 46f56b2..3a6874b 100644 --- a/eval.py +++ b/eval.py @@ -34,8 +34,9 @@ def evaluate(args, model, tokenizer, labels, pad_token_label_id, best, mode, pre eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size) # multi-gpu evaluate - if args.n_gpu > 1: - model = torch.nn.DataParallel(model) + #if args.n_gpu > 1: + # model = torch.nn.DataParallel(model) + #model.to(args.device) logger.info("***** Running evaluation %s *****", prefix) if verbose: diff --git a/model_utils.py b/model_utils.py index d3cdfd1..3ce6368 100644 --- a/model_utils.py +++ b/model_utils.py @@ -88,7 +88,7 @@ def multi_source_label_refine(args, hp_labels, combined_labels, pred_labels, pad label_mask = (pred_labels.max(dim=-1)[0]>_threshold) if args.self_training_hp_label < 5: label_mask = label_mask & (combined_labels!=pad_token_label_id) - elif 6 <= args.self_training_hp_label <= 7: + elif 6 <= args.self_training_hp_label < 7: _threshold = args.self_training_hp_label%1 _confidence = pred_labels.max(dim=-1)[0] for i in range(1,pred_labels.shape[0]): @@ -97,6 +97,17 @@ def multi_source_label_refine(args, hp_labels, combined_labels, pred_labels, pad _distantlabel = combined_labels[i,j] pred_labels[i,j] *= 0 pred_labels[i,j,_distantlabel] = 1 + elif 7 <= args.self_training_hp_label < 9: + _threshold = args.self_training_hp_label%1 + label_mask = (pred_labels.max(dim=-1)[0]>_threshold) + if args.self_training_hp_label < 8: + label_mask = label_mask & (combined_labels!=pad_token_label_id) + # overwrite by hp_labels + for i in range(0,pred_labels.shape[2]): + _labeli = [0]*pred_labels.shape[2] + _labeli[i] = 1 + _labeli = torch.tensor(_labeli).to(pred_labels) + pred_labels[hp_labels==i] = _labeli else: raise NotImplementedError('error') diff --git a/modeling_bert.py b/modeling_bert.py new file mode 100644 index 0000000..f87078c --- /dev/null +++ b/modeling_bert.py @@ -0,0 +1,53 @@ +from transformers import BertPreTrainedModel,BertForTokenClassification +import torch +import torch.nn as nn +from torch.nn import CrossEntropyLoss, KLDivLoss + +class BERTForTokenClassification_v2(BertForTokenClassification): + + def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, + position_ids=None, head_mask=None, inputs_embeds=None, labels=None, label_mask=None): + + outputs = self.bert(input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds) + + sequence_output = outputs[0] + + sequence_output = self.dropout(sequence_output) + logits = self.classifier(sequence_output) + + outputs = (logits,sequence_output) + outputs[2:] # add hidden states and attention if they are here + if labels is not None: + + # Only keep active parts of the loss + if attention_mask is not None or label_mask is not None: + active_loss = True + if attention_mask is not None: + active_loss = attention_mask.view(-1) == 1 + if label_mask is not None: + active_loss = active_loss & label_mask.view(-1) + active_logits = logits.view(-1, self.num_labels)[active_loss] + + + if labels.shape == logits.shape: + loss_fct = KLDivLoss() + if attention_mask is not None or label_mask is not None: + active_labels = labels.view(-1, self.num_labels)[active_loss] + loss = loss_fct(active_logits, active_labels) + else: + loss = loss_fct(logits, labels) + else: + loss_fct = CrossEntropyLoss() + if attention_mask is not None or label_mask is not None: + active_labels = labels.view(-1)[active_loss] + loss = loss_fct(active_logits, active_labels) + else: + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + + + outputs = (loss,) + outputs + return outputs # (loss), scores, (hidden_states), (attentions) diff --git a/run_ner.py b/run_ner.py index 9b7cb4f..f9e6072 100644 --- a/run_ner.py +++ b/run_ner.py @@ -52,6 +52,7 @@ ) from modeling_roberta import RobertaForTokenClassification_v2 +from modeling_bert import BERTForTokenClassification_v2 from data_utils import load_and_cache_examples, get_labels from model_utils import mt_update, get_mt_loss, opt_grad from eval import evaluate @@ -73,7 +74,8 @@ ) MODEL_CLASSES = { - "bert": (BertConfig, BertForTokenClassification, BertTokenizer), + "bert": (BertConfig, BERTForTokenClassification_v2, BertTokenizer), + "biobert": (BertConfig, BERTForTokenClassification_v2, BertTokenizer), "roberta": (RobertaConfig, RobertaForTokenClassification_v2, RobertaTokenizer), "distilbert": (DistilBertConfig, DistilBertForTokenClassification, DistilBertTokenizer), "camembert": (CamembertConfig, CamembertForTokenClassification, CamembertTokenizer), @@ -162,15 +164,18 @@ def train(args, train_dataset, model, tokenizer, labels, pad_token_label_id): steps_trained_in_current_epoch = 0 # Check if continuing training from a checkpoint if os.path.exists(args.model_name_or_path): - # set global_step to gobal_step of last saved checkpoint from model path - global_step = int(args.model_name_or_path.split("-")[-1].split("/")[0]) - epochs_trained = global_step // (len(train_dataloader) // args.gradient_accumulation_steps) - steps_trained_in_current_epoch = global_step % (len(train_dataloader) // args.gradient_accumulation_steps) - - logger.info(" Continuing training from checkpoint, will skip to saved global_step") - logger.info(" Continuing training from epoch %d", epochs_trained) - logger.info(" Continuing training from global step %d", global_step) - logger.info(" Will skip the first %d steps in the first epoch", steps_trained_in_current_epoch) + try: + # set global_step to gobal_step of last saved checkpoint from model path + global_step = int(args.model_name_or_path.split("-")[-1].split("/")[0]) + epochs_trained = global_step // (len(train_dataloader) // args.gradient_accumulation_steps) + steps_trained_in_current_epoch = global_step % (len(train_dataloader) // args.gradient_accumulation_steps) + + logger.info(" Continuing training from checkpoint, will skip to saved global_step") + logger.info(" Continuing training from epoch %d", epochs_trained) + logger.info(" Continuing training from global step %d", global_step) + logger.info(" Will skip the first %d steps in the first epoch", steps_trained_in_current_epoch) + except: + logger.warning(f"Unable to recover training step from {args.model_name_or_path}") tr_loss, logging_loss = 0.0, 0.0 model.zero_grad() @@ -198,9 +203,10 @@ def train(args, train_dataset, model, tokenizer, labels, pad_token_label_id): #inputs = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[4]} if args.model_type != "distilbert": inputs["token_type_ids"] = ( - batch[2] if args.model_type in ["bert", "xlnet"] else None + batch[2] if args.model_type in ["bert", "biobert", "xlnet"] else None ) # XLM and RoBERTa don"t use segment_ids + # import ipdb; ipdb.set_trace() outputs = model(**inputs) loss, logits, final_embeds = outputs[0], outputs[1], outputs[2] # model outputs are always tuple in pytorch-transformers (see doc) mt_loss, vat_loss = 0, 0 @@ -236,7 +242,7 @@ def train(args, train_dataset, model, tokenizer, labels, pad_token_label_id): if args.model_type in ["roberta", "camembert", "xlmroberta"]: word_embed = model.roberta.get_input_embeddings() - elif args.model_type == "bert": + elif args.model_type in ["bert", "biobert"]: word_embed = model.bert.get_input_embeddings() elif args.model_type == "distilbert": word_embed = model.distilbert.get_input_embeddings() @@ -251,7 +257,7 @@ def train(args, train_dataset, model, tokenizer, labels, pad_token_label_id): vat_inputs = {"inputs_embeds": vat_embeds, "attention_mask": batch[1], "labels": batch[3]} if args.model_type != "distilbert": inputs["token_type_ids"] = ( - batch[2] if args.model_type in ["bert", "xlnet"] else None + batch[2] if args.model_type in ["bert", "biobert", "xlnet"] else None ) # XLM and RoBERTa don"t use segment_ids vat_outputs = model(**vat_inputs) @@ -275,7 +281,7 @@ def train(args, train_dataset, model, tokenizer, labels, pad_token_label_id): vat_inputs = {"inputs_embeds": vat_embeds, "attention_mask": batch[1], "labels": batch[3]} if args.model_type != "distilbert": inputs["token_type_ids"] = ( - batch[2] if args.model_type in ["bert", "xlnet"] else None + batch[2] if args.model_type in ["bert", "biobert", "xlnet"] else None ) # XLM and RoBERTa don"t use segment_ids vat_outputs = model(**vat_inputs) @@ -514,6 +520,12 @@ def main(): parser.add_argument('--vat_loss_type', default="logits", type=str, help="subject to measure model difference, choices = [embeds, logits(default)].") + # Use data from weak.json + parser.add_argument('--load_weak', action="store_true", help = 'Load data from weak.json.') + parser.add_argument('--remove_labels_from_weak', action="store_true", help = 'Use data from weak.json, and remove their labels for semi-supervised learning') + parser.add_argument('--rep_train_against_weak', type = int, default = 1, help = 'Upsampling training data again weak data. Default: 1') + + args = parser.parse_args() if ( @@ -609,6 +621,11 @@ def main(): # Training if args.do_train: train_dataset = load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, mode="train") + # import ipdb; ipdb.set_trace() + if args.load_weak: + weak_dataset = load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, mode="weak", remove_labels=args.remove_labels_from_weak) + train_dataset = torch.utils.data.ConcatDataset([train_dataset]*args.rep_train_against_weak + [weak_dataset,]) + global_step, tr_loss, best_dev, best_test = train(args, train_dataset, model, tokenizer, labels, pad_token_label_id) logger.info(" global_step = %s, average loss = %s", global_step, tr_loss) diff --git a/run_self_training_ner.py b/run_self_training_ner.py index ed50b68..18bb85e 100644 --- a/run_self_training_ner.py +++ b/run_self_training_ner.py @@ -51,6 +51,7 @@ get_linear_schedule_with_warmup, ) +from modeling_bert import BERTForTokenClassification_v2 from modeling_roberta import RobertaForTokenClassification_v2 from data_utils import load_and_cache_examples, get_labels from model_utils import multi_source_label_refine, soft_frequency, mt_update, get_mt_loss, opt_grad @@ -73,7 +74,7 @@ ) MODEL_CLASSES = { - "bert": (BertConfig, BertForTokenClassification, BertTokenizer), + "bert": (BertConfig, BERTForTokenClassification_v2, BertTokenizer), "roberta": (RobertaConfig, RobertaForTokenClassification_v2, RobertaTokenizer), "distilbert": (DistilBertConfig, DistilBertForTokenClassification, DistilBertTokenizer), "camembert": (CamembertConfig, CamembertForTokenClassification, CamembertTokenizer), @@ -570,6 +571,11 @@ def main(): parser.add_argument('--self_training_hp_label', type = float, default = 0, help = 'use high precision label.') parser.add_argument('--self_training_ensemble_label', type = int, default = 0, help = 'use ensemble label.') + # Use data from weak.json + parser.add_argument('--load_weak', action="store_true", help = 'Load data from weak.json.') + parser.add_argument('--remove_labels_from_weak', action="store_true", help = 'Use data from weak.json, and remove their labels for semi-supervised learning') + parser.add_argument('--rep_train_against_weak', type = int, default = 1, help = 'Upsampling training data again weak data. Default: 1') + args = parser.parse_args() if ( @@ -657,6 +663,11 @@ def main(): # Training if args.do_train: train_dataset = load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, mode="train") + # import ipdb; ipdb.set_trace() + if args.load_weak: + weak_dataset = load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, mode="weak", remove_labels=args.remove_labels_from_weak) + train_dataset = torch.utils.data.ConcatDataset([train_dataset]*args.rep_train_against_weak + [weak_dataset,]) + model, global_step, tr_loss, best_dev, best_test = train(args, train_dataset, model_class, config, tokenizer, labels, pad_token_label_id) logger.info(" global_step = %s, average loss = %s", global_step, tr_loss) diff --git a/semi_script/README.md b/semi_script/README.md new file mode 100644 index 0000000..db4da59 --- /dev/null +++ b/semi_script/README.md @@ -0,0 +1,65 @@ +## 1. Data Format + +Strongly labeled: +`dev.txt` +`test.txt` +`train.txt` + +Weakly labeled: +`weak.txt` + +Transform into json, e.g., see `dataset/BC5CDR-chem/turn.py` + +## 2. Train Baseline + +```bash +sh ./semi_script/bc5cdr_chem_basline.sh GPUTIDS +``` +where GPUTIDS are the ids of gpus, e.g., `sh ./semi_script/bc5cdr_chem_basline.sh 0,1,2,3` + +## 3. Semi Supervised Learning + +**Mean Teacher** +```bash +sh ./semi_script/bc5cdr_chem_mt.sh GPUTIDS +``` +additional parameters: +1. change `MODEL_NAME` to the baseline model +2. `--mt 1` for enabling mean teacher +3. `--load_weak` and `--remove_labels_from_weak ` for loading data from weak.json and remove their labels. +4. `--rep_train_against_weak N` for upsampling strongly labeled data by `N` times. + +Other parameters +``` +parser.add_argument('--mt', type = int, default = 0, help = 'mean teacher.') +parser.add_argument('--mt_updatefreq', type=int, default=1, help = 'mean teacher update frequency') +parser.add_argument('--mt_class', type=str, default="kl", help = 'mean teacher class, choices:[smart, prob, logit, kl(default), distill].') +parser.add_argument('--mt_lambda', type=float, default=1, help= "trade off parameter of the consistent loss.") +parser.add_argument('--mt_rampup', type=int, default=300, help="rampup iteration.") +parser.add_argument('--mt_alpha1', default=0.99, type=float, help="moving average parameter of mean teacher (for the exponential moving average).") +parser.add_argument('--mt_alpha2', default=0.995, type=float, help="moving average parameter of mean teacher (for the exponential moving average).") +parser.add_argument('--mt_beta', default=10, type=float, help="coefficient of mt_loss term.") +parser.add_argument('--mt_avg', default="exponential", type=str, help="moving average method, choices:[exponentail(default), simple, double_ema].") +parser.add_argument('--mt_loss_type', default="logits", type=str, help="subject to measure model difference, choices:[embeds, logits(default)].") +``` + + +**VAT** +```bash +sh ./semi_script/bc5cdr_chem_vat.sh GPUTIDS +``` +additional parameters: +1. change `MODEL_NAME` to the baseline model +2. `--vat 1` for enabling mean teacher +3. `--load_weak` and `--remove_labels_from_weak ` for loading data from weak.json and remove their labels. +4. `--rep_train_against_weak N` for upsampling strongly labeled data by `N` times. + +Other parameters +``` +# virtual adversarial training +parser.add_argument('--vat', type = int, default = 0, help = 'virtual adversarial training.') +parser.add_argument('--vat_eps', type = float, default = 1e-3, help = 'perturbation size for virtual adversarial training.') +parser.add_argument('--vat_lambda', type = float, default = 1, help = 'trade off parameter for virtual adversarial training.') +parser.add_argument('--vat_beta', type = float, default = 1, help = 'coefficient of the virtual adversarial training loss term.') +parser.add_argument('--vat_loss_type', default="logits", type=str, help="subject to measure model difference, choices = [embeds, logits(default)].") +``` \ No newline at end of file diff --git a/semi_script/bc5cdr_chem_baseline.sh b/semi_script/bc5cdr_chem_baseline.sh new file mode 100755 index 0000000..813b894 --- /dev/null +++ b/semi_script/bc5cdr_chem_baseline.sh @@ -0,0 +1,61 @@ +#!/bin/bash + +if [[ $# -ne 1 ]]; then + GPUID=0 +else + GPUID=$1 +fi + +echo "Run on GPU $GPUID" + +# data +PROJECT_ROOT=$(dirname "$(readlink -f "$0")")/.. +DATA_ROOT=$PROJECT_ROOT/dataset/BC5CDR-chem/ + +# model +MODEL_TYPE=bert +MODEL_NAME=dmis-lab/biobert-base-cased-v1.1 + +# params +LR=1e-5 +WEIGHT_DECAY=1e-4 +EPOCH=25 +SEED=0 + +ADAM_EPS=1e-8 +ADAM_BETA1=0.9 +ADAM_BETA2=0.98 +WARMUP=200 + +TRAIN_BATCH=64 +EVAL_BATCH=64 + +# output +OUTPUT=$PROJECT_ROOT/outputs/BC5CDR-chem/baseline/${MODEL_TYPE}_${EPOCH}_${LR}/ + +[ -e $OUTPUT/script ] || mkdir -p $OUTPUT/script +cp -f $(readlink -f "$0") $OUTPUT/script +rsync -ruzC --exclude-from=$PROJECT_ROOT/.gitignore --exclude 'dataset' --exclude 'pretrained_model' --exclude 'outputs' $PROJECT_ROOT/ $OUTPUT/src + +CUDA_DEVICE_ORDER=PCI_BUS_ID CUDA_VISIBLE_DEVICES=$GPUID python3 run_ner.py --data_dir $DATA_ROOT \ + --model_type $MODEL_TYPE --model_name_or_path $MODEL_NAME \ + --learning_rate $LR \ + --weight_decay $WEIGHT_DECAY \ + --adam_epsilon $ADAM_EPS \ + --adam_beta1 $ADAM_BETA1 \ + --adam_beta2 $ADAM_BETA2 \ + --num_train_epochs $EPOCH \ + --warmup_steps $WARMUP \ + --per_gpu_train_batch_size $TRAIN_BATCH \ + --per_gpu_eval_batch_size $EVAL_BATCH \ + --logging_steps 200 \ + --save_steps 100000 \ + --do_train \ + --do_eval \ + --do_predict \ + --evaluate_during_training \ + --output_dir $OUTPUT \ + --cache_dir $PROJECT_ROOT/pretrained_model \ + --seed $SEED \ + --max_seq_length 128 \ + --overwrite_output_dir \ No newline at end of file diff --git a/semi_script/bc5cdr_chem_bond.sh b/semi_script/bc5cdr_chem_bond.sh new file mode 100755 index 0000000..24da6b2 --- /dev/null +++ b/semi_script/bc5cdr_chem_bond.sh @@ -0,0 +1,73 @@ +#!/bin/bash + +if [[ $# -ne 1 ]]; then + GPUID=0 +else + GPUID=$1 +fi + +echo "Run on GPU $GPUID" + +# data +PROJECT_ROOT=$(dirname "$(readlink -f "$0")")/.. +DATA_ROOT=$PROJECT_ROOT/dataset/BC5CDR-chem/ + +# model +MODEL_TYPE=bert +MODEL_NAME=dmis-lab/biobert-base-cased-v1.1 + +# params +LR=1e-5 +WEIGHT_DECAY=1e-4 +EPOCH=20 +SEED=0 + +ADAM_EPS=1e-8 +ADAM_BETA1=0.9 +ADAM_BETA2=0.98 +WARMUP=200 + +TRAIN_BATCH=64 +EVAL_BATCH=64 + +# self-training parameters +REINIT=0 +BEGIN_STEP=2000 +LABEL_MODE=soft +PERIOD=1000 +# For HP_LABEL in [7,9], BOND will overwrite self-training labels by strong labels. (Only work for soft pseudo labels) +HP_LABEL=7.9 + +# output +OUTPUT=$PROJECT_ROOT/outputs/BC5CDR-chem/BOND/${MODEL_TYPE}_reinit${REINIT}_begin${BEGIN_STEP}_period${PERIOD}_${LABEL_MODE}_hp${HP_LABEL}_${EPOCH}_${LR}/ + +[ -e $OUTPUT/script ] || mkdir -p $OUTPUT/script +cp -f $(readlink -f "$0") $OUTPUT/script +rsync -ruzC --exclude-from=$PROJECT_ROOT/.gitignore --exclude 'dataset' --exclude 'pretrained_model' --exclude 'outputs' $PROJECT_ROOT/ $OUTPUT/src + +CUDA_DEVICE_ORDER=PCI_BUS_ID CUDA_VISIBLE_DEVICES=$GPUID python3 run_self_training_ner.py --data_dir $DATA_ROOT \ + --model_type $MODEL_TYPE --model_name_or_path $MODEL_NAME \ + --learning_rate $LR \ + --weight_decay $WEIGHT_DECAY \ + --adam_epsilon $ADAM_EPS \ + --adam_beta1 $ADAM_BETA1 \ + --adam_beta2 $ADAM_BETA2 \ + --num_train_epochs $EPOCH \ + --warmup_steps $WARMUP \ + --per_gpu_train_batch_size $TRAIN_BATCH \ + --per_gpu_eval_batch_size $EVAL_BATCH \ + --logging_steps 100 \ + --save_steps 100000 \ + --do_train \ + --do_eval \ + --do_predict \ + --evaluate_during_training \ + --output_dir $OUTPUT \ + --cache_dir $PROJECT_ROOT/pretrained_model \ + --seed $SEED \ + --max_seq_length 128 \ + --overwrite_output_dir \ + --self_training_reinit $REINIT --self_training_begin_step $BEGIN_STEP \ + --self_training_label_mode $LABEL_MODE --self_training_period $PERIOD \ + --self_training_hp_label $HP_LABEL \ + --load_weak diff --git a/semi_script/bc5cdr_chem_mt.sh b/semi_script/bc5cdr_chem_mt.sh new file mode 100755 index 0000000..65c1c0d --- /dev/null +++ b/semi_script/bc5cdr_chem_mt.sh @@ -0,0 +1,65 @@ +#!/bin/bash + +if [[ $# -ne 1 ]]; then + GPUID=0 +else + GPUID=$1 +fi + +echo "Run on GPU $GPUID" + +# data +PROJECT_ROOT=$(dirname "$(readlink -f "$0")")/.. +DATA_ROOT=$PROJECT_ROOT/dataset/BC5CDR-chem/ + +# model +MODEL_TYPE=bert +MODEL_NAME=$PROJECT_ROOT/outputs/BC5CDR-chem/baseline/biobert_25_1e-5/checkpoint-best + +# params +LR=1e-5 +WEIGHT_DECAY=1e-4 +EPOCH=25 +SEED=0 + +ADAM_EPS=1e-8 +ADAM_BETA1=0.9 +ADAM_BETA2=0.98 +WARMUP=200 + +TRAIN_BATCH=64 +EVAL_BATCH=64 + +# output +OUTPUT=${MODEL_NAME}/MT/${MODEL_TYPE}_${EPOCH}_${LR}/ + +[ -e $OUTPUT/script ] || mkdir -p $OUTPUT/script +cp -f $(readlink -f "$0") $OUTPUT/script +rsync -ruzC --exclude-from=$PROJECT_ROOT/.gitignore --exclude 'dataset' --exclude 'pretrained_model' --exclude 'outputs' $PROJECT_ROOT/ $OUTPUT/src + +CUDA_DEVICE_ORDER=PCI_BUS_ID CUDA_VISIBLE_DEVICES=$GPUID python3 run_ner.py --data_dir $DATA_ROOT \ + --model_type $MODEL_TYPE --model_name_or_path $MODEL_NAME \ + --learning_rate $LR \ + --weight_decay $WEIGHT_DECAY \ + --adam_epsilon $ADAM_EPS \ + --adam_beta1 $ADAM_BETA1 \ + --adam_beta2 $ADAM_BETA2 \ + --num_train_epochs $EPOCH \ + --warmup_steps $WARMUP \ + --per_gpu_train_batch_size $TRAIN_BATCH \ + --per_gpu_eval_batch_size $EVAL_BATCH \ + --logging_steps 200 \ + --save_steps 100000 \ + --do_train \ + --do_eval \ + --do_predict \ + --evaluate_during_training \ + --output_dir $OUTPUT \ + --cache_dir $PROJECT_ROOT/pretrained_model \ + --seed $SEED \ + --max_seq_length 128 \ + --overwrite_output_dir \ + --mt 1 \ + --load_weak \ + --remove_labels_from_weak \ + --rep_train_against_weak 10 \ No newline at end of file diff --git a/semi_script/bc5cdr_chem_vat.sh b/semi_script/bc5cdr_chem_vat.sh new file mode 100755 index 0000000..fc0a0ee --- /dev/null +++ b/semi_script/bc5cdr_chem_vat.sh @@ -0,0 +1,65 @@ +#!/bin/bash + +if [[ $# -ne 1 ]]; then + GPUID=0 +else + GPUID=$1 +fi + +echo "Run on GPU $GPUID" + +# data +PROJECT_ROOT=$(dirname "$(readlink -f "$0")")/.. +DATA_ROOT=$PROJECT_ROOT/dataset/BC5CDR-chem/ + +# model +MODEL_TYPE=bert +MODEL_NAME=$PROJECT_ROOT/outputs/BC5CDR-chem/baseline/biobert_25_1e-5/checkpoint-best + +# params +LR=1e-5 +WEIGHT_DECAY=1e-4 +EPOCH=25 +SEED=0 + +ADAM_EPS=1e-8 +ADAM_BETA1=0.9 +ADAM_BETA2=0.98 +WARMUP=200 + +TRAIN_BATCH=64 +EVAL_BATCH=64 + +# output +OUTPUT=${MODEL_NAME}/VAT/${MODEL_TYPE}_${EPOCH}_${LR}/ + +[ -e $OUTPUT/script ] || mkdir -p $OUTPUT/script +cp -f $(readlink -f "$0") $OUTPUT/script +rsync -ruzC --exclude-from=$PROJECT_ROOT/.gitignore --exclude 'dataset' --exclude 'pretrained_model' --exclude 'outputs' $PROJECT_ROOT/ $OUTPUT/src + +CUDA_DEVICE_ORDER=PCI_BUS_ID CUDA_VISIBLE_DEVICES=$GPUID python3 run_ner.py --data_dir $DATA_ROOT \ + --model_type $MODEL_TYPE --model_name_or_path $MODEL_NAME \ + --learning_rate $LR \ + --weight_decay $WEIGHT_DECAY \ + --adam_epsilon $ADAM_EPS \ + --adam_beta1 $ADAM_BETA1 \ + --adam_beta2 $ADAM_BETA2 \ + --num_train_epochs $EPOCH \ + --warmup_steps $WARMUP \ + --per_gpu_train_batch_size $TRAIN_BATCH \ + --per_gpu_eval_batch_size $EVAL_BATCH \ + --logging_steps 200 \ + --save_steps 100000 \ + --do_train \ + --do_eval \ + --do_predict \ + --evaluate_during_training \ + --output_dir $OUTPUT \ + --cache_dir $PROJECT_ROOT/pretrained_model \ + --seed $SEED \ + --max_seq_length 128 \ + --overwrite_output_dir \ + --vat 1 \ + --load_weak \ + --remove_labels_from_weak \ + --rep_train_against_weak 10 \ No newline at end of file