Instructions to use dzungpham/graphcodebert-code-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dzungpham/graphcodebert-code-classification with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("dzungpham/graphcodebert-code-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
graphcodebert-code-classification / inference /graphcodebert-mixcode-fft /checkpoint-200 /metrics.json
Download inference/graphcodebert-mixcode-fft/checkpoint-200/metrics.json from dzungpham/graphcodebert-code-classification: direct link, hf CLI and curl.
- Browser
- Download file 291 Bytes
-
https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/34959f8150aa72974f031232b8d5f8b74b50e9fe/inference/graphcodebert-mixcode-fft/checkpoint-200/metrics.json
- Command line
-
hf download hf://dzungpham/graphcodebert-code-classification@34959f8150aa72974f031232b8d5f8b74b50e9fe/inference/graphcodebert-mixcode-fft/checkpoint-200/metrics.json
-
curl -L -o metrics.json https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/34959f8150aa72974f031232b8d5f8b74b50e9fe/inference/graphcodebert-mixcode-fft/checkpoint-200/metrics.json
291 Bytes
| { | |
| "checkpoint": "checkpoint-200", | |
| "split": "test", | |
| "accuracy": 0.588, | |
| "precision": 0.6758, | |
| "recall": 0.588, | |
| "f1_score": 0.6189, | |
| "confusion_matrix": [ | |
| [ | |
| 490, | |
| 287 | |
| ], | |
| [ | |
| 125, | |
| 98 | |
| ] | |
| ] | |
| } |