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README.md
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---
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license: cc-by-4.0
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---
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---
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language: ti
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widget:
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- text: "ድምጻዊ ኣብርሃም ኣፈወርቂ ንዘልኣለም ህያው ኮይኑ ኣብ ልብና ይነብር"
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- text: "ወአመ ሳብዕት ዕለት ቦዘወፅአ እምውስተ ሕዝብ ከመ ያስተጋብእ ወኢረከበ።"
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- text: "እሊ እግል ኖሱ አሳስ ተጠውር ወዐቦት ክምሰልቱ ሸክ ኢወትውዴ።"
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- text: "ኣኩኽር ፡ ልሽክክ ናው ጀረቢነዅስክ ክሙኑኽር ክራውል ሕበርሲድኖ ገረሰነኵ።"
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- text: "ነገ ለግማሽ ፍፃሜ ያለፉትን አሳውቀንና አስመርጠናችሁ እንሸልማለን።"
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tags:
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- geezlab
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metrics:
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- accuracy
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- f1
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- precision
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- recall
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model-index:
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- name: geezswitch-tiroberta
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results: []
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license: cc-by-4.0
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---
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# TiRoBERTa-GeezSwitch
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This model is a fine-tuned version of [fgaim/tiroberta-base](https://huggingface.co/fgaim/tiroberta-base) on the [GeezSwitch](https://github.com/fgaim/geezswitch-data) dataset.
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It achieves the following results on the test set:
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- F1: 0.9948
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- Recall: 0.9948
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- Precision: 0.9948
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- Accuracy: 0.9948
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- Loss: 0.0222
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## Training
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### Hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size: 32
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- eval_batch_size: 32
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 3.0
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- seed: 42
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### Framework versions
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- Transformers 4.19.0.dev0
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- Pytorch 1.11.0+cu113
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- Datasets 2.1.0
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- Tokenizers 0.12.1
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### Citation
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If you use this model or the GeezSwitch model in your research, please cite as follows:
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```markdown
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@inproceedings{fgaim2022geezswitch,
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title={GeezSwitch: Language Identification in Typologically Related Low-resourced East African Languages},
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author={Fitsum Gaim and Wonsuk Yang and Jong C. Park},
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booktitle={Proceedings of the 13th Language Resources and Evaluation Conference},
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year={2022}
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}
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```
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