meta-rater-cleanliness-rating

320
627.0B
1 language
license:mit
by
opendatalab
Other
OTHER
627B params
New
320 downloads
Early-stage
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Edge AI:
Mobile
Laptop
Server
1402GB+ RAM
Mobile
Laptop
Server
Quick Summary

AI model with specialized capabilities.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
584GB+ RAM

Code Examples

Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load the model and tokenizer
model_name = "opendatalab/meta-rater-cleanliness-rating"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Example text
text = "This is a well-formatted article about renewable energy. It contains complete sentences and proper structure."

# Tokenize and predict
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=4096)
with torch.no_grad():
    outputs = model(**inputs)
    score = outputs.logits.squeeze().argmax(dim=0)

print(f"Cleanliness Score: {score:.2f}")
Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load the model and tokenizer
model_name = "opendatalab/meta-rater-cleanliness-rating"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Example text
text = "This is a well-formatted article about renewable energy. It contains complete sentences and proper structure."

# Tokenize and predict
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=4096)
with torch.no_grad():
    outputs = model(**inputs)
    score = outputs.logits.squeeze().argmax(dim=0)

print(f"Cleanliness Score: {score:.2f}")
Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load the model and tokenizer
model_name = "opendatalab/meta-rater-cleanliness-rating"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Example text
text = "This is a well-formatted article about renewable energy. It contains complete sentences and proper structure."

# Tokenize and predict
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=4096)
with torch.no_grad():
    outputs = model(**inputs)
    score = outputs.logits.squeeze().argmax(dim=0)

print(f"Cleanliness Score: {score:.2f}")

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