| language: | |
| - hi | |
| - en | |
| - multilingual | |
| license: mit | |
| tags: | |
| - codeswitching | |
| - hindi-english | |
| - ner | |
| datasets: | |
| - lince | |
| # codeswitch-hineng-ner-lince | |
| This is a pretrained model for **Name Entity Recognition** of `Hindi-english` code-mixed data used from [LinCE](https://ritual.uh.edu/lince/home) | |
| This model is trained for this below repository. | |
| [https://github.com/sagorbrur/codeswitch](https://github.com/sagorbrur/codeswitch) | |
| To install codeswitch: | |
| ``` | |
| pip install codeswitch | |
| ``` | |
| ## Name Entity Recognition of Code-Mixed Data | |
| * **Method-1** | |
| ```py | |
| from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline | |
| tokenizer = AutoTokenizer.from_pretrained("sagorsarker/codeswitch-hineng-ner-lince") | |
| model = AutoModelForTokenClassification.from_pretrained("sagorsarker/codeswitch-hineng-ner-lince") | |
| ner_model = pipeline('ner', model=model, tokenizer=tokenizer) | |
| ner_model("put any hindi english code-mixed sentence") | |
| ``` | |
| * **Method-2** | |
| ```py | |
| from codeswitch.codeswitch import NER | |
| ner = NER('hin-eng') | |
| text = "" # your mixed sentence | |
| result = ner.tag(text) | |
| print(result) | |
| ``` | |