Instructions to use ania3000/demo-ossbert-e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ania3000/demo-ossbert-e with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ania3000/demo-ossbert-e")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ania3000/demo-ossbert-e") model = AutoModelForMaskedLM.from_pretrained("ania3000/demo-ossbert-e") - Notebooks
- Google Colab
- Kaggle
demo-ossbert-e-sh
This model is a fine-tuned version of ania3000/untrained-ossbert-e on the None dataset. It achieves the following results on the evaluation set:
- Loss: 6.0671
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: 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
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 0.9662 | 200 | 6.4811 |
| No log | 1.9324 | 400 | 6.2655 |
| No log | 2.8986 | 600 | 6.1398 |
| No log | 3.8647 | 800 | 6.0718 |
| No log | 4.8309 | 1000 | 6.0671 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
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Model tree for ania3000/demo-ossbert-e
Base model
ania3000/untrained-ossbert-e