README.md

---
license: mit
language:
- en
tags:
- Chinese Spell Correction
- csc
- Chinese Spell Checking
---

# NamBert-for-csc

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/iioSnail/NamBert/blob/master/example.ipynb)

Official model for the paper "Unveiling the Impact of Multimodal Features on Chinese Spelling Correction: From Analysis to Design".

Github: https://github.com/iioSnail/NamBert

The sentence-level performance of the model in SIGHAN datasets is as follows:

|  | Detect-Acc | Detect-Precision | Detect-Recall | Detect-F1 | Correct-Acc | Correct-Precision | Correct-Recall | Correct-F1 | 
|--|--|--|--|--|--|--|--|--|
| Sighan2013 | 82.70 | 87.72 | 82.39 | 84.97 | 81.60 | 86.51 | 81.26 | 83.80 |
| Sighan2014 | 79.76 | 69.03 | 75.00 | 71.89 | 79.10 | 67.79 | 73.65 | 70.60 |
| Sighan2015 | 86.18 | 77.52 | 85.40 | 81.27 | 85.73 | 76.68 | 84.47 | 80.39 |

# Usage

```python
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("iioSnail/NamBert-for-csc", trust_remote_code=True)
model = AutoModel.from_pretrained("iioSnail/NamBert-for-csc", trust_remote_code=True)

inputs = tokenizer("我喜换吃平果，逆呢？", return_tensors='pt')
logits = model(**inputs).logits

target_ids = logits.argmax(-1)
target_ids = tokenizer.restore_ids(target_ids, inputs['input_ids'])

print(''.join(tokenizer.convert_ids_to_tokens(target_ids[0, 1:-1])))
```

Or

```python
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("iioSnail/NamBert-for-csc", trust_remote_code=True)
model = AutoModel.from_pretrained("iioSnail/NamBert-for-csc", trust_remote_code=True)

model = model.to(device)
model = model.eval()
model.set_tokenizer(tokenizer)

model.predict("我是炼习时长两念半的个人练习生菜徐坤")
model.predict(["我是炼习时长两念半的个人练习生菜徐坤", "喜欢场跳rap篮球！！"])
```