Instructions to use Mohamedd123321/Tokenization-large-lr1e-5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mohamedd123321/Tokenization-large-lr1e-5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Mohamedd123321/Tokenization-large-lr1e-5")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Mohamedd123321/Tokenization-large-lr1e-5") model = AutoModelForMaskedLM.from_pretrained("Mohamedd123321/Tokenization-large-lr1e-5") - Notebooks
- Google Colab
- Kaggle
Tokenization-large-lr1e-5
This model is a fine-tuned version of xlm-roberta-large on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.0254
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: 1e-05
- train_batch_size: 32
- 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
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.4327 | 1.0 | 14289 | 1.2043 |
| 1.2143 | 2.0 | 28578 | 1.0653 |
| 1.1104 | 3.0 | 42867 | 1.0254 |
Framework versions
- Transformers 4.56.0
- Pytorch 2.8.0+cu129
- Datasets 5.0.0
- Tokenizers 0.22.0
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Model tree for Mohamedd123321/Tokenization-large-lr1e-5
Base model
FacebookAI/xlm-roberta-large