PointGuardAI/programming-language-identification

PointGuardAI mirror. This repository redistributes the unmodified model artifacts from philomath-1209/programming-language-identification at source revision 9090d38e7333a2c6ff00f154ab981a549842c20f for reproducible AISEC runtime deployments. The model was created by its upstream authors, not retrained by PointGuardAI, and remains available under the WTFPL license.

This Model is a fine-tuned version of huggingface/CodeBERTa-small-v1 on cakiki/rosetta-code Dataset for 26 Programming Languages as mentioned below.

Training Details:

Model is trained for 25 epochs on Azure for nearly 26000 Datapoints for above Mentioned 26 Programming Languages
extracted from Dataset having 1006 of total Programming Language.

Programming Languages this model is able to detect vs Examples used for training

1. 'ARM Assembly':
2. 'AppleScript'
3. 'C'
4. 'C#'
5. 'C++'
6. 'COBOL'
7. 'Erlang'
8. 'Fortran'
9. 'Go'
10. 'Java'
11. 'JavaScript'
12. 'Kotlin'
13. 'Lua
14. 'Mathematica/Wolfram Language'
15. 'PHP'
16. 'Pascal'
17. 'Perl'
18. 'PowerShell'
19. 'Python'
20. 'R
21. 'Ruby'
22. 'Rust'
23. 'Scala'
24. 'Swift'
25. 'Visual Basic .NET'
26. 'jq'

Below is the Training Result for 25 epochs.

• Training Computer Configuration:
  • GPU:1xNvidia Tesla T4,
  • VRam: 16GB,
  • Ram:112GB,
  • Cores:6 Cores
• Training Time taken: exactly 7 hours for 25 epochs
• Training Hyper-parameters:

Inference Code

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline
model_name = 'philomath-1209/programming-language-identification'
loaded_tokenizer = AutoTokenizer.from_pretrained(model_name)
loaded_model = AutoModelForSequenceClassification.from_pretrained(model_name)


device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
text = """
PROGRAM Triangle
   IMPLICIT NONE
   REAL :: a, b, c, Area
   PRINT *, 'Welcome, please enter the&
            &lengths of the 3 sides.'
   READ *, a, b, c
   PRINT *, 'Triangle''s area:  ', Area(a,b,c)
  END PROGRAM Triangle
  FUNCTION Area(x,y,z)
   IMPLICIT NONE
   REAL :: Area            ! function type
   REAL, INTENT( IN ) :: x, y, z
   REAL :: theta, height
   theta = ACOS((x**2+y**2-z**2)/(2.0*x*y))
   height = x*SIN(theta); Area = 0.5*y*height
  END FUNCTION Area

"""
inputs = loaded_tokenizer(text, return_tensors="pt",truncation=True)
with torch.no_grad():
  logits = loaded_model(**inputs).logits
predicted_class_id = logits.argmax().item()
loaded_model.config.id2label[predicted_class_id]

Optimum with ONNX inference

Loading the model requires the 🤗 Optimum library installed.

pip install transformers optimum[onnxruntime] optimum

model_path = "philomath-1209/programming-language-identification"
import torch
from transformers import pipeline, AutoTokenizer
from optimum.onnxruntime import ORTModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained(model_path, subfolder="onnx")
model = ORTModelForSequenceClassification.from_pretrained(model_path, export=False, subfolder="onnx")

text = """
  PROGRAM Triangle
     IMPLICIT NONE
     REAL :: a, b, c, Area
     PRINT *, 'Welcome, please enter the&
              &lengths of the 3 sides.'
     READ *, a, b, c
     PRINT *, 'Triangle''s area:  ', Area(a,b,c)
    END PROGRAM Triangle
    FUNCTION Area(x,y,z)
     IMPLICIT NONE
     REAL :: Area            ! function type
     REAL, INTENT( IN ) :: x, y, z
     REAL :: theta, height
     theta = ACOS((x**2+y**2-z**2)/(2.0*x*y))
     height = x*SIN(theta); Area = 0.5*y*height
    END FUNCTION Area

"""
inputs = tokenizer(text, return_tensors="pt",truncation=True)
with torch.no_grad():
    logits = model(**inputs).logits
predicted_class_id = logits.argmax().item()
model.config.id2label[predicted_class_id]