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 eval_results.json from gokulsrinivasagan/bert_base_code_uml: direct link, hf CLI and curl.
- Browser
- Download file 265 Bytes
-
https://huggingface.co/gokulsrinivasagan/bert_base_code_uml/resolve/main/eval_results.json
- Command line
-
hf download hf://gokulsrinivasagan/bert_base_code_uml/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/gokulsrinivasagan/bert_base_code_uml/resolve/main/eval_results.json
265 Bytes
| { | |
| "epoch": 25.0, | |
| "eval_accuracy": 0.829663160408593, | |
| "eval_loss": 0.822982132434845, | |
| "eval_runtime": 31.502, | |
| "eval_samples": 6151, | |
| "eval_samples_per_second": 195.258, | |
| "eval_steps_per_second": 2.063, | |
| "perplexity": 2.2772808746912707 | |
| } |