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Update README.md

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@@ -4,14 +4,14 @@ tags:
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  - roberta
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  license: mit
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  datasets:
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- - indonlu
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  widget:
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  - text: "Amir Sjarifoeddin Harahap lahir di Kota Medan, Sumatera Utara, 27 April 1907. Ia meninggal di Surakarta, Jawa Tengah, pada 19 Desember 1948 dalam usia 41 tahun. </s></s> Amir Sjarifoeddin Harahap masih hidup."
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  ---
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- ## indo-roberta-indonli
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- indo-roberta-indonli is natural language inference classifier based on [Indo-roberta](https://huggingface.co/flax-community/indonesian-roberta-base) model. It was trained on the trained on [IndoNLI](https://github.com/ir-nlp-csui/indonli/tree/main/data/indonli) dataset. The model used was [Indo-roberta](https://huggingface.co/flax-community/indonesian-roberta-base) and was transfer-learned to a natural inference classifier model. The model is tested using the validation, test layer and test expert dataset given in the Github repository. The results are shown below.
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  ### Result
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  | Dataset | Accuracy | F1 | Precision | Recall |
@@ -37,7 +37,7 @@ The model was trained on with 5 epochs, batch size 16, learning rate 2e-5 and we
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  from transformers import pipeline
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  pretrained_name = "StevenLimcorn/indonesian-roberta-indonli"
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  nlp = pipeline(
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- "text-classification",
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  model=pretrained_name,
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  tokenizer=pretrained_name
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  )
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  - roberta
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  license: mit
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  datasets:
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+ - indonli
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  widget:
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  - text: "Amir Sjarifoeddin Harahap lahir di Kota Medan, Sumatera Utara, 27 April 1907. Ia meninggal di Surakarta, Jawa Tengah, pada 19 Desember 1948 dalam usia 41 tahun. </s></s> Amir Sjarifoeddin Harahap masih hidup."
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  ---
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+ ## Indo-roberta-indonli
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+ Indo-roberta-indonli is natural language inference classifier based on [Indo-roberta](https://huggingface.co/flax-community/indonesian-roberta-base) model. It was trained on the trained on [IndoNLI](https://github.com/ir-nlp-csui/indonli/tree/main/data/indonli) dataset. The model used was [Indo-roberta](https://huggingface.co/flax-community/indonesian-roberta-base) and was transfer-learned to a natural inference classifier model. The model are tested using the validation, test_layer and test_expert dataset given in the github repository. The results are shown below.
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  ### Result
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  | Dataset | Accuracy | F1 | Precision | Recall |
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  from transformers import pipeline
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  pretrained_name = "StevenLimcorn/indonesian-roberta-indonli"
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  nlp = pipeline(
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+ "zero-shot-classification",
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  model=pretrained_name,
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  tokenizer=pretrained_name
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  )