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---
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>
## 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]
``` |