Instructions to use hartular/rrtUngramaticalityAgreePerson with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hartular/rrtUngramaticalityAgreePerson with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hartular/rrtUngramaticalityAgreePerson")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hartular/rrtUngramaticalityAgreePerson") model = AutoModelForSequenceClassification.from_pretrained("hartular/rrtUngramaticalityAgreePerson", device_map="auto") - Notebooks
- Google Colab
- Kaggle
rrtUngramaticalityAgreePerson
This model is a fine-tuned version of dumitrescustefan/bert-base-romanian-cased-v1 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0504
- Accuracy: 0.9918
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
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.0736 | 1.0 | 1446 | 0.0408 | 0.9900 |
| 0.0171 | 2.0 | 2892 | 0.0494 | 0.9922 |
| 0.0032 | 3.0 | 4338 | 0.0504 | 0.9918 |
Framework versions
- Transformers 4.55.0
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.21.4
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Model tree for hartular/rrtUngramaticalityAgreePerson
Base model
dumitrescustefan/bert-base-romanian-cased-v1