Instructions to use fromdeath2morning/xlm-r-argumentClassification-german with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fromdeath2morning/xlm-r-argumentClassification-german with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="fromdeath2morning/xlm-r-argumentClassification-german")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("fromdeath2morning/xlm-r-argumentClassification-german") model = AutoModelForTokenClassification.from_pretrained("fromdeath2morning/xlm-r-argumentClassification-german", device_map="auto") - Notebooks
- Google Colab
- Kaggle
xlm-r-argumentClassification-german
This model is a fine-tuned version of fromdeath2morning/xlm-r-argumentClassification-german on the None dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.5888
- eval_model_preparation_time: 0.0082
- eval_accuracy: 0.9382
- eval_w_accuracy: 0.3797
- eval_classification_report: {'None': {'precision': 0.10743801652892562, 'recall': 0.26, 'f1-score': 0.15204678362573099, 'support': 50.0}, 'S': {'precision': 0.9771763532052881, 'recall': 0.9656149864431847, 'f1-score': 0.9713612695264071, 'support': 16228.0}, 'A': {'precision': 0.08780487804878048, 'recall': 0.20454545454545456, 'f1-score': 0.12286689419795221, 'support': 176.0}, 'P': {'precision': 0.5533769063180828, 'recall': 0.44405594405594406, 'f1-score': 0.492725509214355, 'support': 572.0}, 'accuracy': 0.9381534124280512, 'macro avg': {'precision': 0.4314490385252693, 'recall': 0.46855409626114586, 'f1-score': 0.43475011414111137, 'support': 17026.0}, 'weighted avg': {'precision': 0.9511908263592384, 'recall': 0.9381534124280512, 'f1-score': 0.9441041105195157, 'support': 17026.0}}
- eval_runtime: 8.8895
- eval_samples_per_second: 107.206
- eval_steps_per_second: 13.499
- step: 0
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- 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: 3.0
- mixed_precision_training: Native AMP
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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