Instructions to use Tobander/distilroberta-base-finetuned-wikitext2-mlm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tobander/distilroberta-base-finetuned-wikitext2-mlm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Tobander/distilroberta-base-finetuned-wikitext2-mlm")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Tobander/distilroberta-base-finetuned-wikitext2-mlm") model = AutoModelForMaskedLM.from_pretrained("Tobander/distilroberta-base-finetuned-wikitext2-mlm") - Notebooks
- Google Colab
- Kaggle
distilroberta-base-finetuned-wikitext2-mlm
This model is a fine-tuned version of distilroberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: nan
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: 16
- eval_batch_size: 16
- seed: 42
- 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
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.094 | 1.0 | 9180 | nan |
| 1.9593 | 2.0 | 18360 | nan |
| 1.8584 | 3.0 | 27540 | nan |
Framework versions
- Transformers 4.50.3
- Pytorch 2.6.0
- Datasets 3.5.0
- Tokenizers 0.21.1
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Model tree for Tobander/distilroberta-base-finetuned-wikitext2-mlm
Base model
distilbert/distilroberta-base