Instructions to use jbdaniel/distilroberta-base-finetuned-wikitext2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jbdaniel/distilroberta-base-finetuned-wikitext2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="jbdaniel/distilroberta-base-finetuned-wikitext2")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("jbdaniel/distilroberta-base-finetuned-wikitext2") model = AutoModelForMaskedLM.from_pretrained("jbdaniel/distilroberta-base-finetuned-wikitext2", device_map="auto") - 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: 1.8489
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 |
|---|---|---|---|
| 2.0843 | 1.0 | 2406 | 1.9226 |
| 1.9913 | 2.0 | 4812 | 1.8820 |
| 1.9597 | 3.0 | 7218 | 1.8214 |
Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
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