region:us
The MSRA NER dataset is a Chinese Named Entity Recognition dataset
@inproceedings{levow-2006-third,
    title = "The Third International {C}hinese Language Processing Bakeoff: Word Segmentation and Named Entity Recognition",
    author = "Levow, Gina-Anne",
    booktitle = "Proceedings of the Fifth {SIGHAN} Workshop on {C}hinese Language Processing",
    month = jul,
    year = "2006",
    address = "Sydney, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W06-0115",
    pages = "108--117",
}
---
dataset_info:
  features:
  - name: text
    sequence: string
  - name: labels
    sequence:
      class_label:
        names:
          '0': O
          '1': B-NS
          '2': M-NS
          '3': E-NS
          '4': S-NS
          '5': B-NT
          '6': M-NT
          '7': E-NT
          '8': S-NT
          '9': B-NR
          '10': M-NR
          '11': E-NR
          '12': S-NR
  splits:
  - name: train
    num_bytes: 32917977
    num_examples: 46364
  - name: test
    num_bytes: 2623860
    num_examples: 4365
  download_size: 14890129
  dataset_size: 35541837
---

### How to loading dataset?
```python
from datasets import load_dataset
datasets = load_dataset("minskiter/msra",save_infos=True)
train,test = datasets['train'],datasets['test']
# convert label to str
print(train.features['labels'].feature.int2str(0))
```

### Force update
```python
from datasets import load_dataset
datasets = load_dataset("minskiter/msra", download_mode="force_redownload")
```

### Fit your train

```python
def transform(example):
  # edit example here
  return example
for key in datasets:
  datasets[key] = datasets.map(transform)
```
