Text Classification
Transformers
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use maticzav/model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use maticzav/model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="maticzav/model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("maticzav/model") model = AutoModelForSequenceClassification.from_pretrained("maticzav/model") - Notebooks
- Google Colab
- Kaggle
model
This model is a fine-tuned version of xlm-roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3278
- Accuracy: 0.9146
- F1 Macro: 0.8905
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: 32
- 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
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro |
|---|---|---|---|---|---|
| 0.3073 | 1.0 | 3653 | 0.3298 | 0.8945 | 0.8667 |
| 0.2909 | 2.0 | 7306 | 0.2869 | 0.9106 | 0.8863 |
| 0.1202 | 3.0 | 10959 | 0.3278 | 0.9146 | 0.8905 |
Framework versions
- Transformers 5.9.0
- Pytorch 2.10.0+cu128
- Datasets 4.8.5
- Tokenizers 0.22.2
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Model tree for maticzav/model
Base model
FacebookAI/xlm-roberta-base