metadata
language:
- uz
tags:
- transformers
- robert
- uzrobert
- 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.
Labels:
LABEL_0 -> Negative;
LABEL_1 -> Positive
Model description
This model is a fine-tuned version of rifkat/uztext-3Gb-BPE-Roberta on the Uzbek App reviews for Sentiment Classification 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
Intended uses & limitations
More information needed
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