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--- |
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language: |
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- uz |
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tags: |
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- transformers |
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- robert |
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- uzrobert |
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- uzbek |
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- latin |
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license: apache-2.0 |
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widget: |
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- text: Menga yoqdi, juda yaxshi ekan. |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: uzroberta-sentiment-analysis |
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results: [] |
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--- |
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# uzroberta-sentiment-analysis |
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This is a roBERTa-base model trained on ~23K reviews(~323K words) and finetuned for sentiment analysis of customer reviews. This model is built as part of author's project at the Uz-NLP 2022 Hackathon and it is suitable for Uzbek language. |
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<b>Labels</b>: |
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0 -> Negative; |
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1 -> Positive |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5718 |
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- Precision: 0.9113 |
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- Recall: 0.8869 |
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- F1 Score: 0.8989 |
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- Accuracy: 0.896 |
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## Model description |
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This model is a fine-tuned version of [rifkat/uztext-3Gb-BPE-Roberta](https://huggingface.co/rifkat/uztext-3Gb-BPE-Roberta) on the [Uzbek App reviews for Sentiment Classification](https://github.com/SanatbekMatlatipov/uzbek-sentiment-analysis) dataset. It achieves the following results on the evaluation set: |
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- Loss: 0.5718 |
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- Precision: 0.9113 |
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- Recall: 0.8869 |
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- F1 Score: 0.8989 |
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- Accuracy: 0.896 |
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## Intended uses & limitations |
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More information needed |
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## Training procedure |
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### Training 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: 16 |
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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: cosine |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 4 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.1595 | 1.0 | 1125 | 0.4438 | 0.8971 | 0.8523 | 0.8741 | 0.872 | |
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| 0.1891 | 2.0 | 2250 | 0.4157 | 0.8961 | 0.9012 | 0.8987 | 0.894 | |
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| 0.1201 | 3.0 | 3375 | 0.5024 | 0.9074 | 0.8830 | 0.8950 | 0.892 | |
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| 0.0772 | 4.0 | 4500 | 0.5718 | 0.9113 | 0.8869 | 0.8989 | 0.896 | |
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### Framework versions |
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- Transformers 4.20.1 |
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- Pytorch 1.12.0+cu116 |
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- Datasets 2.3.2 |
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- Tokenizers 0.12.1 |
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