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
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Download README.md from gokulsrinivasagan/codebert_base_code_uml_c: direct link, hf CLI and curl.
- Browser
- Download file 1.9 kB
-
https://huggingface.co/gokulsrinivasagan/codebert_base_code_uml_c/resolve/main/README.md
- Command line
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hf download hf://gokulsrinivasagan/codebert_base_code_uml_c/README.md
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curl -L -o README.md https://huggingface.co/gokulsrinivasagan/codebert_base_code_uml_c/resolve/main/README.md
1.9 kB
metadata
library_name: transformers
base_model: microsoft/codebert-base-mlm
tags:
- generated_from_trainer
datasets:
- devgpt-aimotion/the-stack-v2_PlantUML_filtered
metrics:
- accuracy
model-index:
- name: codebert_base_code_uml_c
results:
- task:
name: Masked Language Modeling
type: fill-mask
dataset:
name: devgpt-aimotion/the-stack-v2_PlantUML_filtered
type: devgpt-aimotion/the-stack-v2_PlantUML_filtered
metrics:
- name: Accuracy
type: accuracy
value: 0.9053223443951083
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