# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("devleoespinosa/bert-base-uncased-issues-128")
model = AutoModelForMaskedLM.from_pretrained("devleoespinosa/bert-base-uncased-issues-128", device_map="auto")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: 1.2341
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: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 16
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.1014 | 1.0 | 291 | 1.7049 |
| 1.6352 | 2.0 | 582 | 1.5080 |
| 1.4965 | 3.0 | 873 | 1.3509 |
| 1.3996 | 4.0 | 1164 | 1.3444 |
| 1.333 | 5.0 | 1455 | 1.2414 |
| 1.2871 | 6.0 | 1746 | 1.3665 |
| 1.2358 | 7.0 | 2037 | 1.2885 |
| 1.2016 | 8.0 | 2328 | 1.3422 |
| 1.1692 | 9.0 | 2619 | 1.2215 |
| 1.145 | 10.0 | 2910 | 1.1708 |
| 1.1269 | 11.0 | 3201 | 1.1325 |
| 1.1127 | 12.0 | 3492 | 1.1719 |
| 1.0898 | 13.0 | 3783 | 1.2175 |
| 1.0759 | 14.0 | 4074 | 1.2070 |
| 1.0764 | 15.0 | 4365 | 1.2166 |
| 1.0608 | 16.0 | 4656 | 1.2341 |
Framework versions
- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
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Model tree for devleoespinosa/bert-base-uncased-issues-128
Base model
google-bert/bert-base-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="devleoespinosa/bert-base-uncased-issues-128")