# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("JungHun/bert-base-uncased-issues-128")
model = AutoModelForMaskedLM.from_pretrained("JungHun/bert-base-uncased-issues-128")Quick Links
bert-base-uncased-issues-128
This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.2312
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: 128
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.3399 | 1.0 | 73 | 1.7462 |
| 1.799 | 2.0 | 146 | 1.4703 |
| 1.6353 | 3.0 | 219 | 1.4796 |
| 1.5464 | 4.0 | 292 | 1.3851 |
| 1.4697 | 5.0 | 365 | 1.3032 |
| 1.4146 | 6.0 | 438 | 1.3339 |
| 1.3677 | 7.0 | 511 | 1.3349 |
| 1.3345 | 8.0 | 584 | 1.2818 |
| 1.3053 | 9.0 | 657 | 1.2646 |
| 1.2886 | 10.0 | 730 | 1.2355 |
| 1.278 | 11.0 | 803 | 1.3037 |
| 1.2568 | 12.0 | 876 | 1.1511 |
| 1.2399 | 13.0 | 949 | 1.2578 |
| 1.2369 | 14.0 | 1022 | 1.2487 |
| 1.2165 | 15.0 | 1095 | 1.2581 |
| 1.2289 | 16.0 | 1168 | 1.2312 |
Framework versions
- Transformers 4.21.3
- Pytorch 1.11.0+cu113
- 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="JungHun/bert-base-uncased-issues-128")