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model update

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  1. README.md +18 -0
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@@ -77,6 +77,24 @@ For F1 scores, the confidence interval is obtained by bootstrap as below:
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  Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/roberta-large-bc5cdr/raw/main/eval/metric.json)
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  and [metric file of entity span](https://huggingface.co/tner/roberta-large-bc5cdr/raw/main/eval/metric_span.json).
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  ### Training hyperparameters
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  Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/roberta-large-bc5cdr/raw/main/eval/metric.json)
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  and [metric file of entity span](https://huggingface.co/tner/roberta-large-bc5cdr/raw/main/eval/metric_span.json).
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+ ### Usage
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+ This model can be used through the transformers library by
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+ ```python
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+ from transformers import AutoTokenizer, AutoModelForTokenClassification
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+ tokenizer = AutoTokenizer.from_pretrained("tner/roberta-large-bc5cdr")
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+ model = AutoModelForTokenClassification.from_pretrained("tner/roberta-large-bc5cdr")
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+ ```
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+ but, since transformers do not support CRF layer, it is recommended to use the model via `T-NER` library.
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+ Install the library via pip
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+ ```shell
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+ pip install tner
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+ ```
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+ and activate model as below.
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+ ```
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+ from tner import TransformersNER
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+ model = TransformersNER("tner/roberta-large-bc5cdr")
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+ model.predict("Jacob Collier is a Grammy awarded English artist from London".split(" "))
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+ ```
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  ### Training hyperparameters
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