Instructions to use gokulsrinivasagan/bert_base_code_uml with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gokulsrinivasagan/bert_base_code_uml with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="gokulsrinivasagan/bert_base_code_uml")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("gokulsrinivasagan/bert_base_code_uml") model = AutoModelForMaskedLM.from_pretrained("gokulsrinivasagan/bert_base_code_uml", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download train_results.json from gokulsrinivasagan/bert_base_code_uml: direct link, hf CLI and curl.
- Browser
- Download file 237 Bytes
-
https://huggingface.co/gokulsrinivasagan/bert_base_code_uml/resolve/main/train_results.json
- Command line
-
hf download hf://gokulsrinivasagan/bert_base_code_uml/train_results.json
-
curl -L -o train_results.json https://huggingface.co/gokulsrinivasagan/bert_base_code_uml/resolve/main/train_results.json
237 Bytes
| { | |
| "epoch": 25.0, | |
| "total_flos": 8.0444602960128e+17, | |
| "train_loss": 1.908989332549426, | |
| "train_runtime": 23433.9018, | |
| "train_samples": 122254, | |
| "train_samples_per_second": 130.424, | |
| "train_steps_per_second": 1.359 | |
| } |