File size: 1,944 Bytes
184224c b1c45aa 184224c b1c45aa |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 |
---
language: ti
widget:
- text: "ድምጻዊ ኣብርሃም ኣፈወርቂ ንዘልኣለም ህያው ኮይኑ ኣብ ልብና ይነብር"
- text: "ወአመ ሳብዕት ዕለት ቦዘወፅአ እምውስተ ሕዝብ ከመ ያስተጋብእ ወኢረከበ።"
- text: "እሊ እግል ኖሱ አሳስ ተጠውር ወዐቦት ክምሰልቱ ሸክ ኢወትውዴ።"
- text: "ኣኩኽር ፡ ልሽክክ ናው ጀረቢነዅስክ ክሙኑኽር ክራውል ሕበርሲድኖ ገረሰነኵ።"
- text: "ነገ ለግማሽ ፍፃሜ ያለፉትን አሳውቀንና አስመርጠናችሁ እንሸልማለን።"
tags:
- geezlab
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: geezswitch-tiroberta
results: []
license: cc-by-4.0
---
# TiRoBERTa-GeezSwitch
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.
It achieves the following results on the test set:
- F1: 0.9948
- Recall: 0.9948
- Precision: 0.9948
- Accuracy: 0.9948
- Loss: 0.0222
## Training
### Hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- seed: 42
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
### Citation
If you use this model or the GeezSwitch model in your research, please cite as follows:
```markdown
@inproceedings{fgaim2022geezswitch,
title={GeezSwitch: Language Identification in Typologically Related Low-resourced East African Languages},
author={Fitsum Gaim and Wonsuk Yang and Jong C. Park},
booktitle={Proceedings of the 13th Language Resources and Evaluation Conference},
year={2022}
}
```
|