Identificación de lenguajes de programación

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]

import torch
from transformers import pipeline, AutoTokenizer
from optimum.onnxruntime import ORTModelForSequenceClassification

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]