---
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