# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("Payoto/roberta-base-finetuned-wikitext2")
model = AutoModelForMaskedLM.from_pretrained("Payoto/roberta-base-finetuned-wikitext2")Quick Links
roberta-base-finetuned-wikitext2
This model is a fine-tuned version of roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.5068
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: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: IPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- total_eval_batch_size: 20
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- training precision: Mixed Precision
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.571 | 1.0 | 300 | 1.5332 |
| 1.5725 | 2.0 | 600 | 1.5117 |
| 1.4742 | 3.0 | 900 | 1.5078 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.10.0+cpu
- Datasets 2.7.1
- 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="Payoto/roberta-base-finetuned-wikitext2")