Instructions to use Marcell322/distilroberta-base-finetuned-wikitext2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Marcell322/distilroberta-base-finetuned-wikitext2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Marcell322/distilroberta-base-finetuned-wikitext2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Marcell322/distilroberta-base-finetuned-wikitext2") model = AutoModelForMaskedLM.from_pretrained("Marcell322/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 an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 5.5214
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 |
|---|---|---|---|
| 5.8841 | 1.0 | 2334 | 5.7795 |
| 5.6526 | 2.0 | 4668 | 5.5853 |
| 5.541 | 3.0 | 7002 | 5.5264 |
Framework versions
- Transformers 4.40.0.dev0
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.1
- Downloads last month
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Model tree for Marcell322/distilroberta-base-finetuned-wikitext2
Base model
distilbert/distilroberta-base