model.py

import os
import json
import torch
from nltk import word_tokenize
from pytorch_transformers import BertForTokenClassification, BertTokenizer


class BertNer(BertForTokenClassification):

    def forward(self, input_ids, token_type_ids=None, attention_mask=None, valid_ids=None, **kwargs):
        sequence_output = self.bert(input_ids, token_type_ids, attention_mask, head_mask=None)[0]
        batch_size, max_len, feat_dim = sequence_output.shape
        valid_output = torch.zeros(batch_size, max_len, feat_dim, dtype=torch.float32,
                                   device='cuda' if torch.cuda.is_available() else 'cpu')
        for i in range(batch_size):
            jj = -1
            for j in range(max_len):
                if valid_ids[i][j].item() == 1:
                    jj += 1
                    valid_output[i][jj] = sequence_output[i][j]
        sequence_output = self.dropout(valid_output)
        logits = self.classifier(sequence_output)
        return logits


class Ner:

    def __init__(self, model_dir: str):
        self.model, self.tokenizer, self.model_config = self.load_model(model_dir)
        self.label_map = self.model_config["label_map"]
        self.max_seq_length = self.model_config["max_seq_length"]
        self.label_map = {int(k): v for k, v in self.label_map.items()}
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        self.model = self.model.to(self.device)
        self.model.eval()

    def load_model(self, model_dir: str, model_config: str = "model_config.json"):
        model_config = os.path.join(model_dir, model_config)
        model_config = json.load(open(model_config))
        model = BertNer.from_pretrained(model_dir)
        tokenizer = BertTokenizer.from_pretrained(model_dir, do_lower_case=model_config["do_lower"])
        return model, tokenizer, model_config

    def tokenize(self, text: str):
        """ tokenize input"""
        words = word_tokenize(text)
        tokens = []
        valid_positions = []
        tokens_len = 0
        parsed_word_num = 0
        tokenize_flag = True
        for i, word in enumerate(words):
            if tokenize_flag:
                token = self.tokenizer.tokenize(word)
            else:
                token = self.tokenizer.tokenize(word)
                if len(token) > 1:
                    token = ['UNK']
            tokens_len += len(token)
            parsed_word_num += 1
            rest_word_num = len(words) - parsed_word_num
            token_size = tokens_len + rest_word_num
            if token_size >= 510:
                tokenize_flag = False
                token_size -= len(token)

                token = self.tokenizer.tokenize(word)
                if len(token) > 1:
                    token = ['UNK']

                token_size += len(token)

            tokens.extend(token)

            for j in range(len(token)):
                if j == 0:
                    valid_positions.append(1)
                else:
                    valid_positions.append(0)
        return tokens, valid_positions

    def preprocess(self, text: str):
        """ preprocess """
        tokens, valid_positions = self.tokenize(text)
        # insert "[CLS]"
        tokens.insert(0, "[CLS]")
        valid_positions.insert(0, 1)
        # insert "[SEP]"
        tokens.append("[SEP]")
        valid_positions.append(1)
        segment_ids = []
        for i in range(len(tokens)):
            segment_ids.append(0)
        input_ids = self.tokenizer.convert_tokens_to_ids(tokens)
        input_mask = [1] * len(input_ids)
        while len(input_ids) < self.max_seq_length:
            input_ids.append(0)
            input_mask.append(0)
            segment_ids.append(0)
            valid_positions.append(0)
        return input_ids, input_mask, segment_ids, valid_positions

    def predict(self, text: str):
        input_ids, input_mask, segment_ids, valid_ids = self.preprocess(text)
        input_ids = torch.tensor([input_ids], dtype=torch.long, device=self.device)
        input_mask = torch.tensor([input_mask], dtype=torch.long, device=self.device)
        segment_ids = torch.tensor([segment_ids], dtype=torch.long, device=self.device)
        valid_ids = torch.tensor([valid_ids], dtype=torch.long, device=self.device)
        with torch.no_grad():
            logits = self.model(input_ids, segment_ids, input_mask, valid_ids)
        logits = torch.nn.functional.softmax(logits, dim=2)
        logits_label = torch.argmax(logits, dim=2)
        logits_label = logits_label.detach().cpu().numpy().tolist()[0]

        logits_confidence = [values[label].item() for values, label in zip(logits[0], logits_label)]

        logits = []
        pos = 0
        for index, mask in enumerate(valid_ids[0]):
            if index == 0:
                continue
            if mask == 1:
                logits.append((logits_label[index - pos], logits_confidence[index - pos]))
            else:
                pos += 1
        logits.pop()

        labels = [(self.label_map[label], confidence) for label, confidence in logits]
        words = word_tokenize(text)
        assert len(labels) == len(words)
        output = [{"word": word, "tag": label, "confidence": confidence} for word, (label, confidence) in
                  zip(words, labels)]
        return output