# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("makinx/results")
model = AutoModelForSequenceClassification.from_pretrained("makinx/results")Quick Links
results
This model is a fine-tuned version of distilgpt2 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6798
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_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.6869 | 1.0 | 5702 | 0.6381 |
| 0.5972 | 2.0 | 11404 | 0.6700 |
| 0.5924 | 3.0 | 17106 | 0.6798 |
Framework versions
- Transformers 4.57.3
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
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Model tree for makinx/results
Base model
distilbert/distilgpt2
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="makinx/results")