# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("aytugkaya/python-gpt2-large-issues-128")
model = AutoModelForMaskedLM.from_pretrained("aytugkaya/python-gpt2-large-issues-128")Quick Links
python-gpt2-large-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: 1.2286
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: 8
- 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 |
|---|---|---|---|
| 1.9843 | 1.0 | 1163 | 1.6715 |
| 1.5713 | 2.0 | 2326 | 1.4301 |
| 1.4226 | 3.0 | 3489 | 1.3808 |
| 1.332 | 4.0 | 4652 | 1.3806 |
| 1.2708 | 5.0 | 5815 | 1.2737 |
| 1.2089 | 6.0 | 6978 | 1.2354 |
| 1.167 | 7.0 | 8141 | 1.2250 |
| 1.126 | 8.0 | 9304 | 1.2262 |
| 1.0846 | 9.0 | 10467 | 1.1891 |
| 1.0647 | 10.0 | 11630 | 1.2263 |
| 1.0301 | 11.0 | 12793 | 1.1383 |
| 1.0054 | 12.0 | 13956 | 1.0922 |
| 0.9714 | 13.0 | 15119 | 1.1141 |
| 0.9713 | 14.0 | 16282 | 1.1614 |
| 0.9362 | 15.0 | 17445 | 1.0753 |
| 0.9382 | 16.0 | 18608 | 1.2286 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.6
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="aytugkaya/python-gpt2-large-issues-128")