deberta-v3-base-quality-v3

Name: deberta-v3-base-quality-v3
Author: agentlans

3.0B

1 language

license:mit

agentlans

Other

OTHER

3B params

New

27 downloads

Early-stage

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Edge AI:

Mobile

Laptop

Server

7GB+ RAM

Mobile

Laptop

Server

Quick Summary

AI model with specialized capabilities.

Device Compatibility

Mobile

4-6GB RAM

Laptop

16GB RAM

Server

GPU

Minimum Recommended

3GB+ RAM

Code Examples

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Performancepythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/deberta-v3-base-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

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