Instructions to use iliyaML/distilroberta-base-finetuned-wikitext2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use iliyaML/distilroberta-base-finetuned-wikitext2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="iliyaML/distilroberta-base-finetuned-wikitext2")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("iliyaML/distilroberta-base-finetuned-wikitext2") model = AutoModelForMaskedLM.from_pretrained("iliyaML/distilroberta-base-finetuned-wikitext2") - Notebooks
- Google Colab
- Kaggle
distilroberta-base-finetuned-wikitext2
This model is a fine-tuned version of distilroberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.8268
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.0838 | 1.0 | 2406 | 1.9292 |
| 1.9985 | 2.0 | 4812 | 1.8664 |
| 1.9406 | 3.0 | 7218 | 1.8571 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.1
- Tokenizers 0.13.3
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Model tree for iliyaML/distilroberta-base-finetuned-wikitext2
Base model
distilbert/distilroberta-base