# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("ViktorDo/DistilBERT-POWO_Scratch")
model = AutoModelForMaskedLM.from_pretrained("ViktorDo/DistilBERT-POWO_Scratch")Quick Links
DistilBERT-POWO_Scratch
This model is a fine-tuned version of on the None dataset. It achieves the following results on the evaluation set:
- Loss: 4.9068
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: 5
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 40
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 7.104 | 0.18 | 200 | 5.9641 |
| 5.6973 | 0.36 | 400 | 5.5992 |
| 5.5464 | 0.54 | 600 | 5.4564 |
| 5.377 | 0.72 | 800 | 5.3606 |
| 5.2162 | 0.9 | 1000 | 5.2674 |
| 5.1499 | 1.08 | 1200 | 5.2080 |
| 5.1313 | 1.26 | 1400 | 5.1447 |
| 5.0138 | 1.44 | 1600 | 5.1041 |
| 4.9509 | 1.62 | 1800 | 5.0572 |
| 4.9598 | 1.8 | 2000 | 5.0185 |
| 4.9581 | 1.98 | 2200 | 5.0109 |
| 4.8458 | 2.16 | 2400 | 4.9608 |
| 4.953 | 2.34 | 2600 | 4.9482 |
| 4.7448 | 2.52 | 2800 | 4.9211 |
| 4.8574 | 2.71 | 3000 | 4.9093 |
| 4.8402 | 2.89 | 3200 | 4.8980 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
- Downloads last month
- 10
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ViktorDo/DistilBERT-POWO_Scratch")