ByT5-Small-fine-tuned

10
license:apache-2.0
by
savinugunarathna
Language Model
OTHER
New
10 downloads
Early-stage
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Quick Summary

AI model with specialized capabilities.

Training Data Analysis

🔵 Good (6.0/10)

Researched training datasets used by ByT5-Small-fine-tuned with quality assessment

Specialized For

general
multilingual

Training Datasets (1)

c4
🔵 6/10
general
multilingual
Key Strengths
  • •Scale and Accessibility: 750GB of publicly available, filtered text
  • •Systematic Filtering: Documented heuristics enable reproducibility
  • •Language Diversity: Despite English-only, captures diverse writing styles
Considerations
  • •English-Only: Limits multilingual applications
  • •Filtering Limitations: Offensive content and low-quality text remain despite filtering

Explore our comprehensive training dataset analysis

View All Datasets

Code Examples

Output: මට බඩගිනිpythonpytorch
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"
model = model.to(device)
model.eval()

while True:
    text = input("Enter Singlish (or 'quit'): ")
    if text.lower() == 'quit':
        break
    
    inputs = tokenizer(text, return_tensors="pt").to(device)
    with torch.no_grad():
        outputs = model.generate(**inputs, max_new_tokens=80, num_beams=5)
    print(f"Sinhala: {tokenizer.decode(outputs[0], skip_special_tokens=True)}")

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