Add scripts to run NER inference on entire Singapore Hansard dataset

Author: kingsleykuan

README.md

@@ -1,6 +1,8 @@
 # singapore-hansard-nlp
 Singapore Hansard NLP
 
+[singapore-hansard-sentiment-ner-final.zip](https://drive.google.com/file/d/1xWDplG7ythfnv3EZjxhjEmtUHwS9st-E/view?usp=sharing)
+
 Note that 28 JSON files in the 12th session from 2011-10-10 to 2012-08-13 are excluded
 as they use an old format that is difficult to parse.
 
@@ -24,8 +26,6 @@ python test_sentiment.py input.json models/xlm-roberta-base-sst-2-sh-sentiment
 
 [xlm-roberta-base-sst-2-sh-sentiment.tar.xz](https://drive.google.com/file/d/1toqvkwWjXuHH0EIHHjJv9V9x5FZ-0Pba/view?usp=sharing)
 
-[singapore-hansard-sentiment-final.zip](https://drive.google.com/file/d/14yZRPLvQ7usliO1WOdFKmqoWgtJjax0C/view?usp=sharing)
-
 ### Raw Results
 
 xlm-roberta-base-sst-2

xlm-roberta-sh-ner/classify_ner.py

@@ -0,0 +1,126 @@
+import argparse
+import json
+import os
+
+import torch
+from seqeval import metrics
+from transformers import XLMRobertaForTokenClassification, XLMRobertaTokenizerFast
+
+from sh_ner_dataset import id_to_label
+
+DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+# NOTE: Assumes that sentiment has already been classified and JSON file is updated
+
+def create_arg_parser():
+    parser = argparse.ArgumentParser(
+        description='Perform NER on Singapore Hansard using XLM-RoBERTa model')
+
+    parser.add_argument('input_dir_path', type=str,
+        help='Path of directroy to read JSON files from.')
+
+    parser.add_argument('output_dir_path', type=str,
+        help='Path of directroy to write JSON files to.')
+
+    parser.add_argument('model_name_or_dir', type=str,
+        help='Name or directory of model.')
+
+    return parser
+
+def main(input_dir_path, output_dir_path, model_name_or_dir):
+    tokenizer = XLMRobertaTokenizerFast.from_pretrained(model_name_or_dir)
+    model = XLMRobertaForTokenClassification.from_pretrained(model_name_or_dir).to(DEVICE)
+    model.eval()
+
+    os.makedirs(output_dir_path, exist_ok=True)
+
+    for file_name in os.listdir(input_dir_path):
+        if file_name.endswith('.json'):
+            count = 0
+            input_file_path = os.path.join(input_dir_path, file_name)
+            with open(input_file_path) as json_file:
+                data = json.load(json_file)
+
+            for session in data['sessions']:
+                for speech in session['speeches']:
+                    for text_sentiment in speech['content']:
+                        text = text_sentiment['text']
+                        inputs = tokenizer(
+                            text,
+                            padding=False,
+                            truncation=True,
+                            return_special_tokens_mask=True,
+                            return_offsets_mapping=True)
+
+                        with torch.no_grad():
+                            input_ids = torch.tensor(inputs['input_ids']).unsqueeze(0).to(DEVICE)
+                            attention_mask = torch.tensor(inputs['attention_mask']).unsqueeze(0).to(DEVICE)
+                            outputs = model(input_ids, attention_mask).logits
+                            predictions = torch.argmax(outputs, dim=2)[0].detach().cpu().numpy()
+
+                        special_tokens_mask = inputs['special_tokens_mask']
+                        offset_mapping = inputs['offset_mapping']
+
+                        start_index = 0
+                        end_index = 0
+                        previous_iob_entity = None
+
+                        entities = []
+
+                        for i in range(len(predictions)):
+                            if special_tokens_mask[i] == 0 and predictions[i] != 0:
+                                iob_entity = id_to_label(predictions[i])
+
+                                if iob_entity[:2] == 'B-' or previous_iob_entity is None or iob_entity[2:] != previous_iob_entity[2:]:
+                                    if previous_iob_entity is not None:
+                                        label = previous_iob_entity[2:]
+                                        start = start_index
+                                        end = end_index
+
+                                        if text[start] == ' ':
+                                            start += 1
+
+                                        word = text[start:end]
+
+                                        entities.append({
+                                            'word': word,
+                                            'start': start,
+                                            'end': end,
+                                            'label': label,
+                                        })
+
+                                    start_index = offset_mapping[i][0]
+
+                                end_index = offset_mapping[i][1]
+                                previous_iob_entity = iob_entity
+
+                        if previous_iob_entity is not None:
+                            label = previous_iob_entity[2:]
+                            start = start_index
+                            end = end_index
+
+                            if text[start] == ' ':
+                                start += 1
+
+                            word = text[start:end]
+
+                            entities.append({
+                                'word': word,
+                                'start': start,
+                                'end': end,
+                                'label': label,
+                            })
+
+                        text_sentiment['entities'] = entities
+                        count += 1
+
+            output_file_path = os.path.join(output_dir_path, file_name)
+            with open(output_file_path, 'w') as json_file:
+                json.dump(data, json_file)
+
+            print("File: {}, Count: {}".format(file_name, count))
+
+if __name__ == '__main__':
+    parser = create_arg_parser()
+    args = parser.parse_args()
+    main(args.input_dir_path, args.output_dir_path, args.model_name_or_dir)

xlm-roberta-sh-ner/sh_ner_dataset.py

@@ -76,7 +76,7 @@ def create_sh_ner_dataset(train_json_path, val_json_path, tokenizer):
     return sh_ner_train_dataset, sh_ner_val_dataset
 
 class SingaporeHansardNerDataset(Dataset):
-    def __init__(self, sentences, sentences_entities, tokenizer):
+    def __init__(self, sentences, sentences_entities, tokenizer, trainer=True):
         self.sentences = sentences
         self.sentences_entities = sentences_entities
 
@@ -117,8 +117,10 @@ def __init__(self, sentences, sentences_entities, tokenizer):
                 else:
                     sentence_labels[i] = 'MASK'
 
-            del sentence_encodings['special_tokens_mask']
-            del sentence_encodings['offset_mapping']
+            if trainer:
+                del sentence_encodings['special_tokens_mask']
+                del sentence_encodings['offset_mapping']
+
             sentence_labels = [self.label2id[label] for label in sentence_labels]
 
             encodings.append(sentence_encodings)