Instructions to use AT/distilroberta-base-finetuned-wikitext2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AT/distilroberta-base-finetuned-wikitext2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="AT/distilroberta-base-finetuned-wikitext2")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("AT/distilroberta-base-finetuned-wikitext2") model = AutoModelForMaskedLM.from_pretrained("AT/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.
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: 80.0
Training results
Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
- Downloads last month
- 10