---
license: wtfpl
datasets:
- cakiki/rosetta-code
language:
- en
metrics:
- accuracy
library_name: transformers
pipeline_tag: text-classification
tags:
- code
- programming-language
- code-classification
base_model: huggingface/CodeBERTa-small-v1
---
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<br> extracted from Dataset having 1006 of total Programming Language.
### Programming Languages this model is able to detect vs Examples used for training
<ol>
  <li>'ARM Assembly':</li>
 <li>'AppleScript'</li>
 <li>'C'</li>
 <li>'C#'</li>
 <li>'C++'</li>
 <li>'COBOL'</li>
 <li>'Erlang'</li>
 <li>'Fortran'</li>
 <li>'Go'</li>
 <li>'Java'</li>
 <li>'JavaScript'</li>
 <li>'Kotlin'</li>
 <li>'Lua</li>
 <li>'Mathematica/Wolfram Language'</li>
 <li>'PHP'</li>
 <li>'Pascal'</li>
 <li>'Perl'</li>
 <li>'PowerShell'</li>
 <li>'Python'</li>
 <li>'R</li>
 <li>'Ruby'</li>
 <li>'Rust'</li>
 <li>'Scala'</li>
 <li>'Swift'</li>
 <li>'Visual Basic .NET'</li>
 <li>'jq'</li>
</ol>
<br>

## Below is the Training Result for 25 epochs.
<ul>
    <li>Training Computer Configuration: <ul>
       <li>GPU:1xNvidia Tesla T4, </li>
       <li>VRam: 16GB,</li>
       <li>Ram:112GB,</li>
       <li>Cores:6 Cores </li>
    </ul></li>
         
<li>Training Time taken: exactly 7 hours for 25 epochs</li>
<li>Training Hyper-parameters: </li>
  </ul>

 

![image/png](https://cdn-uploads.huggingface.co/production/uploads/645c859ad90782b1a6a3e957/YIYl1XZk0zpi3DCvn3D80.png)



![training detail.png](https://cdn-uploads.huggingface.co/production/uploads/645c859ad90782b1a6a3e957/Oi9TuJ8nEjtt6Z_W56myn.png)

## Inference Code

  ```python
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.
```shell
pip install transformers optimum[onnxruntime] optimum
```

```python
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]

```