Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
custom_code
text-embeddings-inference
Instructions to use potemin/toxic_generation_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use potemin/toxic_generation_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="potemin/toxic_generation_classifier", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("potemin/toxic_generation_classifier", trust_remote_code=True) model = AutoModelForSequenceClassification.from_pretrained("potemin/toxic_generation_classifier", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| base_model: Tochka-AI/ruRoPEBert-classic-base-2k | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| - f1 | |
| model-index: | |
| - name: toxic_generation_classifier | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # toxic_generation_classifier | |
| This model is a fine-tuned version of [Tochka-AI/ruRoPEBert-classic-base-2k](https://huggingface.co/Tochka-AI/ruRoPEBert-classic-base-2k) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.1611 | |
| - Accuracy: 0.9536 | |
| - F1: 0.9537 | |
| ## 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: 0.0002 | |
| - train_batch_size: 32 | |
| - eval_batch_size: 32 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 2 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | |
| | No log | 1.0 | 149 | 0.1758 | 0.9460 | 0.9457 | | |
| | No log | 2.0 | 298 | 0.1611 | 0.9536 | 0.9537 | | |
| ### Framework versions | |
| - Transformers 4.44.2 | |
| - Pytorch 2.4.1+cu121 | |
| - Datasets 3.0.0 | |
| - Tokenizers 0.19.1 | |