Instructions to use Mou11209203/distilroberta-base-finetuned-wikitext2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mou11209203/distilroberta-base-finetuned-wikitext2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Mou11209203/distilroberta-base-finetuned-wikitext2")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Mou11209203/distilroberta-base-finetuned-wikitext2") model = AutoModelForMaskedLM.from_pretrained("Mou11209203/distilroberta-base-finetuned-wikitext2") - 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:
- eval_loss: 2.9527
- eval_runtime: 2.2803
- eval_samples_per_second: 871.801
- eval_steps_per_second: 109.194
- step: 0
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
Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2
- Downloads last month
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Model tree for Mou11209203/distilroberta-base-finetuned-wikitext2
Base model
distilbert/distilroberta-base