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
|
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
-
hf download hf://Mohamedd123321/Tokenization/README.md
-
curl -L -H "Authorization: Bearer $HF_TOKEN" -o README.md https://huggingface.co/Mohamedd123321/Tokenization/resolve/main/README.md
1.48 kB
metadata
library_name: transformers
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
model-index:
- name: Tokenization
results: []
Tokenization
This model is a fine-tuned version of 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