Instructions to use angusleung100/CodeT5-Small-Solidity-Vulnerability with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use angusleung100/CodeT5-Small-Solidity-Vulnerability with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="angusleung100/CodeT5-Small-Solidity-Vulnerability")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("angusleung100/CodeT5-Small-Solidity-Vulnerability") model = AutoModelForSequenceClassification.from_pretrained("angusleung100/CodeT5-Small-Solidity-Vulnerability", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from angusleung100/CodeT5-Small-Solidity-Vulnerability: direct link, hf CLI and curl.
- Browser
- Download file 1.78 kB
-
https://huggingface.co/angusleung100/CodeT5-Small-Solidity-Vulnerability/resolve/main/README.md
- Command line
-
hf download hf://angusleung100/CodeT5-Small-Solidity-Vulnerability/README.md
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curl -L -o README.md https://huggingface.co/angusleung100/CodeT5-Small-Solidity-Vulnerability/resolve/main/README.md
1.78 kB
metadata
library_name: transformers
license: apache-2.0
base_model: Salesforce/codet5-small
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: CodeT5-Small-Solidity-Vulnerability
results: []
CodeT5-Small-Solidity-Vulnerability
This model is a fine-tuned version of Salesforce/codet5-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0000
- Accuracy: 1.0
- Precision: 1.0
- Recall: 1.0
- F1: 1.0
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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.0748 | 1.0 | 4713 | 0.0835 | 0.9830 | 0.9834 | 0.9830 | 0.9828 |
| 0.025 | 2.0 | 9426 | 0.0333 | 0.9805 | 0.9822 | 0.9805 | 0.9808 |
| 0.012 | 3.0 | 14139 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1