Instructions to use gokulsrinivasagan/codebert_base_code_uml_c with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gokulsrinivasagan/codebert_base_code_uml_c with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="gokulsrinivasagan/codebert_base_code_uml_c")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("gokulsrinivasagan/codebert_base_code_uml_c") model = AutoModelForMaskedLM.from_pretrained("gokulsrinivasagan/codebert_base_code_uml_c", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download all_results.json from gokulsrinivasagan/codebert_base_code_uml_c: direct link, hf CLI and curl.
- Browser
- Download file 487 Bytes
-
https://huggingface.co/gokulsrinivasagan/codebert_base_code_uml_c/resolve/main/all_results.json
- Command line
-
hf download hf://gokulsrinivasagan/codebert_base_code_uml_c/all_results.json
-
curl -L -o all_results.json https://huggingface.co/gokulsrinivasagan/codebert_base_code_uml_c/resolve/main/all_results.json
487 Bytes
| { | |
| "epoch": 5.0, | |
| "eval_accuracy": 0.9053223443951083, | |
| "eval_loss": 0.45334097743034363, | |
| "eval_runtime": 83.1276, | |
| "eval_samples": 12451, | |
| "eval_samples_per_second": 149.782, | |
| "eval_steps_per_second": 1.564, | |
| "perplexity": 1.573560643713862, | |
| "total_flos": 2.898276322152499e+17, | |
| "train_loss": 0.5986486167209296, | |
| "train_runtime": 10447.1842, | |
| "train_samples": 220179, | |
| "train_samples_per_second": 105.377, | |
| "train_steps_per_second": 1.098 | |
| } |