# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("arman1o1/bert-base-cased-wikitext2")
model = AutoModelForMaskedLM.from_pretrained("arman1o1/bert-base-cased-wikitext2", device_map="auto")Quick Links
bert-base-cased-wikitext2
This model is a fine-tuned version of bert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 6.5036
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: 2e-05
- train_batch_size: 8
- 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: 15
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 7.0847 | 1.0 | 2346 | 7.0386 |
| 6.8734 | 2.0 | 4692 | 6.8551 |
| 6.7814 | 3.0 | 7038 | 6.7920 |
| 6.7354 | 4.0 | 9384 | 6.7064 |
| 6.6452 | 5.0 | 11730 | 6.6737 |
| 6.5976 | 6.0 | 14076 | 6.6223 |
| 6.561 | 7.0 | 16422 | 6.6287 |
| 6.5116 | 8.0 | 18768 | 6.5647 |
| 6.5225 | 9.0 | 21114 | 6.5995 |
| 6.4733 | 10.0 | 23460 | 6.5538 |
| 6.4449 | 11.0 | 25806 | 6.5882 |
| 6.4292 | 12.0 | 28152 | 6.5310 |
| 6.4342 | 13.0 | 30498 | 6.5242 |
| 6.3938 | 14.0 | 32844 | 6.4913 |
| 6.4199 | 15.0 | 35190 | 6.5006 |
Framework versions
- Transformers 4.55.4
- Pytorch 2.7.0+gitf717b2a
- Datasets 3.6.0
- Tokenizers 0.21.4
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Model tree for arman1o1/bert-base-cased-wikitext2
Base model
google-bert/bert-base-cased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="arman1o1/bert-base-cased-wikitext2")