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| from collections import defaultdict | |
| from typing import List, Tuple, Dict | |
| import torch | |
| from torch import nn | |
| from torch.nn.utils.rnn import pad_sequence | |
| from torch.utils.data import DataLoader | |
| import random | |
| class InstructBase(nn.Module): | |
| def __init__(self, config): | |
| super().__init__() | |
| self.max_width = config.max_width | |
| self.base_config = config | |
| def get_dict(self, spans, classes_to_id): | |
| dict_tag = defaultdict(int) | |
| for span in spans: | |
| if span[2] in classes_to_id: | |
| dict_tag[(span[0], span[1])] = classes_to_id[span[2]] | |
| return dict_tag | |
| def preprocess_spans(self, tokens, ner, classes_to_id): | |
| max_len = self.base_config.max_len | |
| if len(tokens) > max_len: | |
| length = max_len | |
| tokens = tokens[:max_len] | |
| else: | |
| length = len(tokens) | |
| spans_idx = [] | |
| for i in range(length): | |
| spans_idx.extend([(i, i + j) for j in range(self.max_width)]) | |
| dict_lab = self.get_dict(ner, classes_to_id) if ner else defaultdict(int) | |
| # 0 for null labels | |
| span_label = torch.LongTensor([dict_lab[i] for i in spans_idx]) | |
| spans_idx = torch.LongTensor(spans_idx) | |
| # mask for valid spans | |
| valid_span_mask = spans_idx[:, 1] > length - 1 | |
| # mask invalid positions | |
| span_label = span_label.masked_fill(valid_span_mask, -1) | |
| return { | |
| 'tokens': tokens, | |
| 'span_idx': spans_idx, | |
| 'span_label': span_label, | |
| 'seq_length': length, | |
| 'entities': ner, | |
| } | |
| def collate_fn(self, batch_list, entity_types=None): | |
| # batch_list: list of dict containing tokens, ner | |
| if entity_types is None: | |
| negs = self.get_negatives(batch_list, 100) | |
| class_to_ids = [] | |
| id_to_classes = [] | |
| for b in batch_list: | |
| # negs = b["negative"] | |
| random.shuffle(negs) | |
| # negs = negs[:sampled_neg] | |
| max_neg_type_ratio = int(self.base_config.max_neg_type_ratio) | |
| if max_neg_type_ratio == 0: | |
| # no negatives | |
| neg_type_ratio = 0 | |
| else: | |
| neg_type_ratio = random.randint(0, max_neg_type_ratio) | |
| if neg_type_ratio == 0: | |
| # no negatives | |
| negs_i = [] | |
| else: | |
| negs_i = negs[:len(b['ner']) * neg_type_ratio] | |
| # this is the list of all possible entity types (positive and negative) | |
| types = list(set([el[-1] for el in b['ner']] + negs_i)) | |
| # shuffle (every epoch) | |
| random.shuffle(types) | |
| if len(types) != 0: | |
| # prob of higher number shoul | |
| # random drop | |
| if self.base_config.random_drop: | |
| num_ents = random.randint(1, len(types)) | |
| types = types[:num_ents] | |
| # maximum number of entities types | |
| types = types[:int(self.base_config.max_types)] | |
| # supervised training | |
| if "label" in b: | |
| types = sorted(b["label"]) | |
| class_to_id = {k: v for v, k in enumerate(types, start=1)} | |
| id_to_class = {k: v for v, k in class_to_id.items()} | |
| class_to_ids.append(class_to_id) | |
| id_to_classes.append(id_to_class) | |
| batch = [ | |
| self.preprocess_spans(b["tokenized_text"], b["ner"], class_to_ids[i]) for i, b in enumerate(batch_list) | |
| ] | |
| else: | |
| class_to_ids = {k: v for v, k in enumerate(entity_types, start=1)} | |
| id_to_classes = {k: v for v, k in class_to_ids.items()} | |
| batch = [ | |
| self.preprocess_spans(b["tokenized_text"], b["ner"], class_to_ids) for b in batch_list | |
| ] | |
| span_idx = pad_sequence( | |
| [b['span_idx'] for b in batch], batch_first=True, padding_value=0 | |
| ) | |
| span_label = pad_sequence( | |
| [el['span_label'] for el in batch], batch_first=True, padding_value=-1 | |
| ) | |
| return { | |
| 'seq_length': torch.LongTensor([el['seq_length'] for el in batch]), | |
| 'span_idx': span_idx, | |
| 'tokens': [el['tokens'] for el in batch], | |
| 'span_mask': span_label != -1, | |
| 'span_label': span_label, | |
| 'entities': [el['entities'] for el in batch], | |
| 'classes_to_id': class_to_ids, | |
| 'id_to_classes': id_to_classes, | |
| } | |
| def get_negatives(batch_list, sampled_neg=5): | |
| ent_types = [] | |
| for b in batch_list: | |
| types = set([el[-1] for el in b['ner']]) | |
| ent_types.extend(list(types)) | |
| ent_types = list(set(ent_types)) | |
| # sample negatives | |
| random.shuffle(ent_types) | |
| return ent_types[:sampled_neg] | |
| def create_dataloader(self, data, entity_types=None, **kwargs): | |
| return DataLoader(data, collate_fn=lambda x: self.collate_fn(x, entity_types), **kwargs) | |