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 train_results.json from gokulsrinivasagan/codebert_base_code_uml_c: direct link, hf CLI and curl.
- Browser
- Download file 239 Bytes
-
https://huggingface.co/gokulsrinivasagan/codebert_base_code_uml_c/resolve/main/train_results.json
- Command line
-
hf download hf://gokulsrinivasagan/codebert_base_code_uml_c/train_results.json
-
curl -L -o train_results.json https://huggingface.co/gokulsrinivasagan/codebert_base_code_uml_c/resolve/main/train_results.json
239 Bytes
| { | |
| "epoch": 5.0, | |
| "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 | |
| } |