# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("aleksahet/comic-lake-91")
model = AutoModelForMaskedLM.from_pretrained("aleksahet/comic-lake-91")Quick Links
comic-lake-91
This model is a fine-tuned version of on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.0439
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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 5.1203 | 1.15 | 15000 | 5.1215 |
| 2.6573 | 2.29 | 30000 | 2.4824 |
| 2.1498 | 3.44 | 45000 | 2.0455 |
Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.14.5
- Tokenizers 0.13.3
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="aleksahet/comic-lake-91")