readability-arabertv2-d3tok-reg

2
1
license:mit
by
CAMeL-Lab
Other
OTHER
2502.13520B params
New
2 downloads
Early-stage
Try on Hugging FaceAdd to Compare
Edge AI:
Mobile
Laptop
Server
5593GB+ RAM
Mobile
Laptop
Server
Quick Summary

Model description AraBERTv2+D3Tok+Reg is a readability assessment model that was built by fine-tuning the AraBERTv2 model with Mean Squared Error loss (Reg).

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
2331GB+ RAM

Code Examples

How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-reg")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
results = readability(sentences, function_to_apply="none")
readability_levels = [max(round(result['score']+0.5),1) for result in results]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-reg")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
results = readability(sentences, function_to_apply="none")
readability_levels = [max(round(result['score']+0.5),1) for result in results]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-reg")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
results = readability(sentences, function_to_apply="none")
readability_levels = [max(round(result['score']+0.5),1) for result in results]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-reg")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
results = readability(sentences, function_to_apply="none")
readability_levels = [max(round(result['score']+0.5),1) for result in results]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-reg")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
results = readability(sentences, function_to_apply="none")
readability_levels = [max(round(result['score']+0.5),1) for result in results]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-reg")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
results = readability(sentences, function_to_apply="none")
readability_levels = [max(round(result['score']+0.5),1) for result in results]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-reg")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
results = readability(sentences, function_to_apply="none")
readability_levels = [max(round(result['score']+0.5),1) for result in results]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-reg")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
results = readability(sentences, function_to_apply="none")
readability_levels = [max(round(result['score']+0.5),1) for result in results]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-reg")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
results = readability(sentences, function_to_apply="none")
readability_levels = [max(round(result['score']+0.5),1) for result in results]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-reg")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
results = readability(sentences, function_to_apply="none")
readability_levels = [max(round(result['score']+0.5),1) for result in results]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-reg")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
results = readability(sentences, function_to_apply="none")
readability_levels = [max(round(result['score']+0.5),1) for result in results]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-reg")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
results = readability(sentences, function_to_apply="none")
readability_levels = [max(round(result['score']+0.5),1) for result in results]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-reg")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
results = readability(sentences, function_to_apply="none")
readability_levels = [max(round(result['score']+0.5),1) for result in results]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-reg")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
results = readability(sentences, function_to_apply="none")
readability_levels = [max(round(result['score']+0.5),1) for result in results]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-reg")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
results = readability(sentences, function_to_apply="none")
readability_levels = [max(round(result['score']+0.5),1) for result in results]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-reg")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
results = readability(sentences, function_to_apply="none")
readability_levels = [max(round(result['score']+0.5),1) for result in results]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-reg")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
results = readability(sentences, function_to_apply="none")
readability_levels = [max(round(result['score']+0.5),1) for result in results]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-reg")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
results = readability(sentences, function_to_apply="none")
readability_levels = [max(round(result['score']+0.5),1) for result in results]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-reg")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
results = readability(sentences, function_to_apply="none")
readability_levels = [max(round(result['score']+0.5),1) for result in results]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-reg")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
results = readability(sentences, function_to_apply="none")
readability_levels = [max(round(result['score']+0.5),1) for result in results]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-reg")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
results = readability(sentences, function_to_apply="none")
readability_levels = [max(round(result['score']+0.5),1) for result in results]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-reg")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
results = readability(sentences, function_to_apply="none")
readability_levels = [max(round(result['score']+0.5),1) for result in results]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-reg")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
results = readability(sentences, function_to_apply="none")
readability_levels = [max(round(result['score']+0.5),1) for result in results]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-reg")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
results = readability(sentences, function_to_apply="none")
readability_levels = [max(round(result['score']+0.5),1) for result in results]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-reg")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
results = readability(sentences, function_to_apply="none")
readability_levels = [max(round(result['score']+0.5),1) for result in results]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-reg")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
results = readability(sentences, function_to_apply="none")
readability_levels = [max(round(result['score']+0.5),1) for result in results]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-reg")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
results = readability(sentences, function_to_apply="none")
readability_levels = [max(round(result['score']+0.5),1) for result in results]

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