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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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datasets: |
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- TLMD |
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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: tiroberta-sentiment |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.828 |
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- name: F1 |
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type: f1 |
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value: 0.8476527900797165 |
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- name: Precision |
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type: precision |
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value: 0.760731319554849 |
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- name: Recall |
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type: recall |
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value: 0.957 |
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--- |
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# Sentiment Analysis for Tigrinya with TiRoBERTa |
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This model is a fine-tuned version of [TiRoBERTa](https://huggingface.co/fgaim/roberta-base-tigrinya) on a YouTube comments Sentiment Analysis dataset for Tigrinya (Tela et al. 2020). |
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## Basic usage |
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```python |
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from transformers import pipeline |
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ti_sent = pipeline("sentiment-analysis", model="fgaim/tiroberta-sentiment") |
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ti_sent("ድምጻዊ ኣብርሃም ኣፈወርቂ ንዘልኣለም ህያው ኮይኑ ኣብ ልብና ይነብር") |
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``` |
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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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- seed: 42 |
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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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### Results |
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It achieves the following results on the evaluation set: |
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- F1: 0.8477 |
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- Precision: 0.7607 |
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- Recall: 0.957 |
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- Accuracy: 0.828 |
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- Loss: 0.6796 |
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### Framework versions |
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- Transformers 4.10.3 |
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- Pytorch 1.9.0+cu111 |
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- Datasets 1.10.2 |
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- Tokenizers 0.10.1 |
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## Citation |
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If you use this model in your product or research, please cite as follows: |
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``` |
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@article{Fitsum2021TiPLMs, |
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author={Fitsum Gaim and Wonsuk Yang and Jong C. Park}, |
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title={Monolingual Pre-trained Language Models for Tigrinya}, |
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year=2021, |
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publisher={WiNLP 2021/EMNLP 2021} |
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} |
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``` |
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## References |
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``` |
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Tela, A., Woubie, A. and Hautamäki, V. 2020. |
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Transferring Monolingual Model to Low-Resource Language: The Case of Tigrinya. |
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ArXiv, abs/2006.07698. |
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``` |
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