# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("DioLiu/SimpleDataset")
model = AutoModelForMaskedLM.from_pretrained("DioLiu/SimpleDataset")Quick Links
SimpleDataset
This model is a fine-tuned version of distilroberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 3.6762
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 |
|---|---|---|---|
| No log | 1.0 | 4 | 3.3454 |
| No log | 2.0 | 8 | 3.8818 |
| No log | 3.0 | 12 | 3.6762 |
Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="DioLiu/SimpleDataset")