readme: ---
  language:
    - uz
  tags:
    - transformers
    - uzroberta
    - uzbek
    - latin
  license: apache-2.0
  widget:
    - text: Menga yoqdi, juda yaxshi ekan.
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: uzroberta-sentiment-analysis
    results: []
  ---
  
  # uzroberta-sentiment-analysis
  
  This is a roBERTa-base model trained on ~23K reviews (more than 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.
  
  <b>Labels</b>: LABEL_0 -> Negative;   LABEL_1 -> Positive
  
  ## Model description
  
  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:
  - Loss: 0.5718
  - Precision: 0.9113
  - Recall: 0.8869
  - F1 Score: 0.8989
  - Accuracy: 0.896
  
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 2e-05
  - train_batch_size: 16
  - eval_batch_size: 32
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: cosine
  - lr_scheduler_warmup_ratio: 0.1
  - num_epochs: 4
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.1595        | 1.0   | 1125 | 0.4438          | 0.8971    | 0.8523 | 0.8741 | 0.872    |
  | 0.1891        | 2.0   | 2250 | 0.4157          | 0.8961    | 0.9012 | 0.8987 | 0.894    |
  | 0.1201        | 3.0   | 3375 | 0.5024          | 0.9074    | 0.8830 | 0.8950 | 0.892    |
  | 0.0772        | 4.0   | 4500 | 0.5718          | 0.9113    | 0.8869 | 0.8989 | 0.896    |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.12.0+cu116
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  