Text Classification
Transformers
PyTorch
Safetensors
English
bert
nlp
code
classification
text-embeddings-inference
Instructions to use TigerByteCyber/codenlbert-sm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TigerByteCyber/codenlbert-sm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TigerByteCyber/codenlbert-sm")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TigerByteCyber/codenlbert-sm") model = AutoModelForSequenceClassification.from_pretrained("TigerByteCyber/codenlbert-sm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 879 Bytes
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datasets:
- vishnun/CodevsNL
language:
- en
metrics:
- accuracy
library_name: transformers
tags:
- nlp
- code
- classification
---
## PreFace
Code vs Natural language classification using bert-small from prajwall, below are the metrics achieved
## Training Metrics
| Epoch | Training Loss | Validation Loss | Accuracy | |
|------:|--------------:|----------------:|---------:|---|
| 1 | 0.022500 | 0.012705 | 0.997203 | |
| 2 | 0.008700 | 0.013107 | 0.996880 | |
| 3 | 0.002700 | 0.014081 | 0.997633 | |
| 4 | 0.001800 | 0.010666 | 0.997526 | |
| 5 | 0.000900 | 0.010800 | 0.998063 | |
## More
- Github repo for installable python package: https://github.com/Vishnunkumar
- Space on the extraction of code blocks from screenshots: https://huggingface.co/spaces/vishnun/SnapCode |