readability-arabertv2-d3tok-CE

19
1
license:mit
by
CAMeL-Lab
Other
OTHER
2502.13520B params
New
19 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+CE is a readability assessment model that was built by fine-tuning the AraBERTv2 model with cross-entropy loss (CE).

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-CE")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
readability_levels = [int(readability(sentences)[i]['label'][6:])+1 for i in range(len(sentences))]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-CE")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
readability_levels = [int(readability(sentences)[i]['label'][6:])+1 for i in range(len(sentences))]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-CE")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
readability_levels = [int(readability(sentences)[i]['label'][6:])+1 for i in range(len(sentences))]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-CE")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
readability_levels = [int(readability(sentences)[i]['label'][6:])+1 for i in range(len(sentences))]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-CE")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
readability_levels = [int(readability(sentences)[i]['label'][6:])+1 for i in range(len(sentences))]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-CE")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
readability_levels = [int(readability(sentences)[i]['label'][6:])+1 for i in range(len(sentences))]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-CE")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
readability_levels = [int(readability(sentences)[i]['label'][6:])+1 for i in range(len(sentences))]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-CE")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
readability_levels = [int(readability(sentences)[i]['label'][6:])+1 for i in range(len(sentences))]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-CE")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
readability_levels = [int(readability(sentences)[i]['label'][6:])+1 for i in range(len(sentences))]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-CE")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
readability_levels = [int(readability(sentences)[i]['label'][6:])+1 for i in range(len(sentences))]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-CE")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
readability_levels = [int(readability(sentences)[i]['label'][6:])+1 for i in range(len(sentences))]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-CE")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
readability_levels = [int(readability(sentences)[i]['label'][6:])+1 for i in range(len(sentences))]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-CE")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
readability_levels = [int(readability(sentences)[i]['label'][6:])+1 for i in range(len(sentences))]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-CE")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
readability_levels = [int(readability(sentences)[i]['label'][6:])+1 for i in range(len(sentences))]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-CE")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
readability_levels = [int(readability(sentences)[i]['label'][6:])+1 for i in range(len(sentences))]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-CE")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
readability_levels = [int(readability(sentences)[i]['label'][6:])+1 for i in range(len(sentences))]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-CE")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
readability_levels = [int(readability(sentences)[i]['label'][6:])+1 for i in range(len(sentences))]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-CE")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
readability_levels = [int(readability(sentences)[i]['label'][6:])+1 for i in range(len(sentences))]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-CE")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
readability_levels = [int(readability(sentences)[i]['label'][6:])+1 for i in range(len(sentences))]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-CE")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
readability_levels = [int(readability(sentences)[i]['label'][6:])+1 for i in range(len(sentences))]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-CE")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
readability_levels = [int(readability(sentences)[i]['label'][6:])+1 for i in range(len(sentences))]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-CE")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
readability_levels = [int(readability(sentences)[i]['label'][6:])+1 for i in range(len(sentences))]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-CE")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
readability_levels = [int(readability(sentences)[i]['label'][6:])+1 for i in range(len(sentences))]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-CE")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
readability_levels = [int(readability(sentences)[i]['label'][6:])+1 for i in range(len(sentences))]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-CE")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
readability_levels = [int(readability(sentences)[i]['label'][6:])+1 for i in range(len(sentences))]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-CE")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
readability_levels = [int(readability(sentences)[i]['label'][6:])+1 for i in range(len(sentences))]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-CE")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
readability_levels = [int(readability(sentences)[i]['label'][6:])+1 for i in range(len(sentences))]
How to usepythontransformers
from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-CE")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
readability_levels = [int(readability(sentences)[i]['label'][6:])+1 for i in range(len(sentences))]

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