Instructions to use Mohamedd123321/Tokenization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mohamedd123321/Tokenization with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Mohamedd123321/Tokenization")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Mohamedd123321/Tokenization") model = AutoModelForMaskedLM.from_pretrained("Mohamedd123321/Tokenization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Mohamedd123321/Tokenization: direct link, hf CLI and curl.
- Browser
- Download file 1.48 kB
-
https://huggingface.co/Mohamedd123321/Tokenization/resolve/main/README.md
- Command line
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hf download hf://Mohamedd123321/Tokenization/README.md
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curl -L -H "Authorization: Bearer $HF_TOKEN" -o README.md https://huggingface.co/Mohamedd123321/Tokenization/resolve/main/README.md
1.48 kB
| library_name: transformers | |
| license: mit | |
| base_model: xlm-roberta-base | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: Tokenization | |
| 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. --> | |
| # Tokenization | |
| This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 1.0500 | |
| ## 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: 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 | | |
| |:-------------:|:-----:|:-----:|:---------------:| | |
| | 1.4652 | 1.0 | 14289 | 1.2451 | | |
| | 1.2572 | 2.0 | 28578 | 1.1048 | | |
| | 1.1374 | 3.0 | 42867 | 1.0500 | | |
| ### Framework versions | |
| - Transformers 4.56.0 | |
| - Pytorch 2.8.0+cu129 | |
| - Datasets 4.8.5 | |
| - Tokenizers 0.22.0 | |