Instructions to use kalhansb/esperberto with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kalhansb/esperberto with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="kalhansb/esperberto")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("kalhansb/esperberto") model = AutoModelForMaskedLM.from_pretrained("kalhansb/esperberto", device_map="auto") - Notebooks
- Google Colab
- Kaggle
esperberto
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 7.2773
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: 0.0001
- train_batch_size: 8
- 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 8.1043 | 0.4444 | 500 | 8.0023 |
| 7.8787 | 0.8889 | 1000 | 7.8835 |
| 7.778 | 1.3333 | 1500 | 7.6755 |
| 7.595 | 1.7778 | 2000 | 7.5751 |
| 7.4362 | 2.2222 | 2500 | 7.5073 |
| 7.4063 | 2.6667 | 3000 | 7.4029 |
| 7.1675 | 3.1111 | 3500 | 7.3419 |
| 7.1212 | 3.5556 | 4000 | 7.2704 |
| 6.99 | 4.0 | 4500 | 7.0921 |
| 6.9225 | 4.4444 | 5000 | 7.0655 |
| 6.8496 | 4.8889 | 5500 | 7.0027 |
Framework versions
- Transformers 4.57.3
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
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