# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("mwesner/bert-base-uncased")
model = AutoModelForMaskedLM.from_pretrained("mwesner/bert-base-uncased")Quick Links
bert-base-uncased
This model was trained on a dataset of issues from github. It achieves the following results on the evaluation set:
- Loss: 1.2437
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
Masked language model trained on github issue data with token length of 128.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 8
- 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 |
|---|---|---|---|
| 2.205 | 1.0 | 9303 | 1.7893 |
| 1.8417 | 2.0 | 18606 | 1.7270 |
| 1.7103 | 3.0 | 27909 | 1.6650 |
| 1.6014 | 4.0 | 37212 | 1.6052 |
| 1.523 | 5.0 | 46515 | 1.5782 |
| 1.4588 | 6.0 | 55818 | 1.4836 |
| 1.3922 | 7.0 | 65121 | 1.4289 |
| 1.317 | 8.0 | 74424 | 1.4414 |
| 1.2622 | 9.0 | 83727 | 1.4322 |
| 1.2123 | 10.0 | 93030 | 1.3651 |
| 1.1753 | 11.0 | 102333 | 1.3636 |
| 1.1164 | 12.0 | 111636 | 1.2872 |
| 1.0636 | 13.0 | 120939 | 1.3705 |
| 1.021 | 14.0 | 130242 | 1.3013 |
| 0.996 | 15.0 | 139545 | 1.2756 |
| 0.9625 | 16.0 | 148848 | 1.2437 |
Framework versions
- Transformers 4.14.1
- Pytorch 1.9.0
- Datasets 1.11.0
- Tokenizers 0.10.3
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="mwesner/bert-base-uncased")