## Cyclone Chinese NER

This model provides simplified Chinese NER model based on pretrained model BERT (specifically BERT + CRF)
Currently, we only support 8 general type of entities ("address", "company", "government", "name", "organization", "position", "scene", "time")

### Usage
    from transformers import BertConfig

    config = BertConfig.from_pretrained("bert-base-chinese", num_labels=num_labels)

    model_path = "cyclone/cyclone-ner"

    tokenizer = CNerTokenizer.from_pretrained(model_path, do_lower_case=True)
    model = BertCrfForNer.from_pretrained(model_path, config=config)

{}
transformers pytorch bert endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #endpoints_compatible #region-us 

## Cyclone Chinese NER

This model provides simplified Chinese NER model based on pretrained model BERT (specifically BERT + CRF)
Currently, we only support 8 general type of entities ("address", "company", "government", "name", "organization", "position", "scene", "time")

### Usage
    from transformers import BertConfig

    config = BertConfig.from_pretrained("bert-base-chinese", num_labels=num_labels)

    model_path = "cyclone/cyclone-ner"

    tokenizer = CNerTokenizer.from_pretrained(model_path, do_lower_case=True)
    model = BertCrfForNer.from_pretrained(model_path, config=config)

## Cyclone Chinese NER

This model provides simplified Chinese NER model based on pretrained model BERT (specifically BERT + CRF)
Currently, we only support 8 general type of entities ("address", "company", "government", "name", "organization", "position", "scene", "time") ### Usage
    from transformers import BertConfig

    config = BertConfig.from_pretrained("bert-base-chinese", num_labels=num_labels)

    model_path = "cyclone/cyclone-ner"

    tokenizer = CNerTokenizer.from_pretrained(model_path, do_lower_case=True)
    model = BertCrfForNer.from_pretrained(model_path, config=config)
TAGS
#transformers #pytorch #bert #endpoints_compatible #region-us 
 ## Cyclone Chinese NER

This model provides simplified Chinese NER model based on pretrained model BERT (specifically BERT + CRF)
Currently, we only support 8 general type of entities ("address", "company", "government", "name", "organization", "position", "scene", "time") ### Usage
    from transformers import BertConfig

    config = BertConfig.from_pretrained("bert-base-chinese", num_labels=num_labels)

    model_path = "cyclone/cyclone-ner"

    tokenizer = CNerTokenizer.from_pretrained(model_path, do_lower_case=True)
    model = BertCrfForNer.from_pretrained(model_path, config=config)
passage: TAGS
#transformers #pytorch #bert #endpoints_compatible #region-us 
## Cyclone Chinese NER

This model provides simplified Chinese NER model based on pretrained model BERT (specifically BERT + CRF)
Currently, we only support 8 general type of entities ("address", "company", "government", "name", "organization", "position", "scene", "time")### Usage
    from transformers import BertConfig

    config = BertConfig.from_pretrained("bert-base-chinese", num_labels=num_labels)

    model_path = "cyclone/cyclone-ner"

    tokenizer = CNerTokenizer.from_pretrained(model_path, do_lower_case=True)
    model = BertCrfForNer.from_pretrained(model_path, config=config)