Instructions to use yochen/distilroberta-base-wiki-mark with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yochen/distilroberta-base-wiki-mark with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="yochen/distilroberta-base-wiki-mark")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("yochen/distilroberta-base-wiki-mark") model = AutoModelForMaskedLM.from_pretrained("yochen/distilroberta-base-wiki-mark") - Notebooks
- Google Colab
- Kaggle
distilroberta-base-wiki-mark
This model is a fine-tuned version of yochen/distilroberta-base-wiki-mark on the None dataset. It achieves the following results on the evaluation set:
- eval_loss: 2.2695
- eval_runtime: 4.3489
- eval_samples_per_second: 431.836
- eval_steps_per_second: 54.037
- epoch: 10.1
- step: 20489
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: 5000
Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
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