# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Namronaldo2004/results_unixcoder")
model = AutoModelForSequenceClassification.from_pretrained("Namronaldo2004/results_unixcoder")Quick Links
results_unixcoder
This model is a fine-tuned version of Namronaldo2004/unixcoder-full-code on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1734
- F1: 0.9691
- Accuracy: 0.9691
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: 4e-05
- train_batch_size: 64
- eval_batch_size: 64
- 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: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy |
|---|---|---|---|---|---|
| 0.1425 | 1.0 | 8159 | 0.1734 | 0.9691 | 0.9691 |
Framework versions
- Transformers 5.2.0
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for Namronaldo2004/results_unixcoder
Base model
Namronaldo2004/unixcoder-full-code
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Namronaldo2004/results_unixcoder")