# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("vishnun/codenlbert-tiny")
model = AutoModelForSequenceClassification.from_pretrained("vishnun/codenlbert-tiny")Quick Links
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
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vishnun/codenlbert-tiny")