ByT5-Singlish-Sinhala-Merged2

24
license:mit
by
savinugunarathna
Other
OTHER
New
24 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-Singlish-Sinhala-Merged2 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

Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_id = "savinugunarathna/ByT5-Singlish-Sinhala-Merged2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(model_id)

inputs = tokenizer("mama giya", return_tensors="pt")
outputs = model.generate(
    **inputs,
    num_beams=4,
    max_length=512,   # important — ByT5 produces longer byte sequences
    length_penalty=1.0,
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
# → මම ගිය

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