metadata
base_model: s-nlp/russian_toxicity_classifier
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: tg_comments_model
results: []
tg_comments_model
This model is a fine-tuned version of s-nlp/russian_toxicity_classifier on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0519
- Precision: 0.9762
- Recall: 0.9856
- F1: 0.9809
- Accuracy: 0.9817
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 64
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.075 | 0.2239 | 300 | 0.0591 | 0.9833 | 0.9731 | 0.9781 | 0.9793 |
| 0.0627 | 0.4478 | 600 | 0.0567 | 0.9749 | 0.9843 | 0.9796 | 0.9805 |
| 0.0612 | 0.6716 | 900 | 0.0537 | 0.9795 | 0.9821 | 0.9808 | 0.9817 |
| 0.0633 | 0.8955 | 1200 | 0.0519 | 0.9762 | 0.9856 | 0.9809 | 0.9817 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1