Text Classification
Transformers
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use PetrKelin/idnes-iab-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PetrKelin/idnes-iab-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="PetrKelin/idnes-iab-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("PetrKelin/idnes-iab-classifier") model = AutoModelForSequenceClassification.from_pretrained("PetrKelin/idnes-iab-classifier") - Notebooks
- Google Colab
- Kaggle
idnes-iab-classifier
This model is a fine-tuned version of FacebookAI/xlm-roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.7711
- Accuracy: 0.7827
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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.8827 | 1.0 | 2839 | 0.8920 | 0.7457 |
| 0.7287 | 2.0 | 5678 | 0.7622 | 0.7769 |
| 0.5941 | 3.0 | 8517 | 0.7531 | 0.7792 |
| 0.5028 | 4.0 | 11356 | 0.7711 | 0.7827 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for PetrKelin/idnes-iab-classifier
Base model
FacebookAI/xlm-roberta-base