model update
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README.md
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@@ -79,6 +79,24 @@ 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-conll2003/raw/main/eval/metric.json)
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and [metric file of entity span](https://huggingface.co/tner/roberta-large-conll2003/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-conll2003/raw/main/eval/metric.json)
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and [metric file of entity span](https://huggingface.co/tner/roberta-large-conll2003/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-conll2003")
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model = AutoModelForTokenClassification.from_pretrained("tner/roberta-large-conll2003")
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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-conll2003")
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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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