gokulsrinivasagan / codebert_base_code_uml_c

huggingface.co
Total runs: 25
24-hour runs: 0
7-day runs: 0
30-day runs: 11
Model's Last Updated: June 26 2025
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Introduction of codebert_base_code_uml_c

Model Details of codebert_base_code_uml_c

codebert_base_code_uml_c

This model is a fine-tuned version of microsoft/codebert-base-mlm on the devgpt-aimotion/the-stack-v2_PlantUML_filtered dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4533
  • Accuracy: 0.9053
Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure
Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.0001
  • train_batch_size: 96
  • eval_batch_size: 96
  • seed: 10
  • optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 10000
  • num_epochs: 5
Training results
Training Loss Epoch Step Validation Loss Accuracy
0.5254 4.3592 10000 0.4521 0.9053
Framework versions
  • Transformers 4.51.2
  • Pytorch 2.6.0+cu126
  • Datasets 3.5.0
  • Tokenizers 0.21.1

Runs of gokulsrinivasagan codebert_base_code_uml_c on huggingface.co

25
Total runs
0
24-hour runs
0
3-day runs
0
7-day runs
11
30-day runs

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codebert_base_code_uml_c install

codebert_base_code_uml_c is an open source model from GitHub that offers a free installation service, and any user can find codebert_base_code_uml_c on GitHub to install. At the same time, huggingface.co provides the effect of codebert_base_code_uml_c install, users can directly use codebert_base_code_uml_c installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

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