Instructions to use redsat/model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use redsat/model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="redsat/model")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("redsat/model") model = AutoModelForMaskedLM.from_pretrained("redsat/model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: mit | |
| base_model: atlasia/XLM-RoBERTa-Morocco | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: model | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # model | |
| This model is a fine-tuned version of [atlasia/XLM-RoBERTa-Morocco](https://huggingface.co/atlasia/XLM-RoBERTa-Morocco) on an unknown dataset. | |
| ## 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: 1 | |
| - eval_batch_size: 1 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 16 | |
| - total_train_batch_size: 16 | |
| - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_ratio: 0.03 | |
| - num_epochs: 1 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| ### Framework versions | |
| - Transformers 4.51.3 | |
| - Pytorch 2.6.0+cu124 | |
| - Datasets 3.6.0 | |
| - Tokenizers 0.21.1 | |