# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("nomsgadded/mlm")
model = AutoModelForMaskedLM.from_pretrained("nomsgadded/mlm")Quick Links
mlm
This model is a fine-tuned version of roberta-base on the wikitext wikitext-2-raw-v1 dataset. It achieves the following results on the evaluation set:
- Loss: 1.2468
- Accuracy: 0.7303
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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 0
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.3758 | 1.0 | 150 | 1.2826 | 0.7277 |
| 1.3763 | 2.0 | 300 | 1.2747 | 0.7272 |
| 1.3558 | 3.0 | 450 | 1.2607 | 0.7278 |
Framework versions
- Transformers 4.33.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
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Model tree for nomsgadded/mlm
Base model
FacebookAI/roberta-baseDataset used to train nomsgadded/mlm
Evaluation results
- Accuracy on wikitext wikitext-2-raw-v1validation set self-reported0.730
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="nomsgadded/mlm")