kazakh-gec-mt5-small-run3-finetune-v2

34
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by
saken-tukenov
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OTHER
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Quick Summary

AI model with specialized capabilities.

Training Data Analysis

🔵 Good (6.0/10)

Researched training datasets used by kazakh-gec-mt5-small-run3-finetune-v2 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

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Code Examples

Usagepythontransformers
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("saken-tukenov/kazakh-gec-mt5-small-run3-finetune-v2")
model = AutoModelForSeq2SeqLM.from_pretrained("saken-tukenov/kazakh-gec-mt5-small-run3-finetune-v2")

input_text = "gec: " + "Мен кеше мектепке бардым"
inputs = tokenizer(input_text, return_tensors="pt", max_length=128, truncation=True)
outputs = model.generate(**inputs, max_new_tokens=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Projectbibtex
@misc{tukenov2026gec,
  title={Kazakh Grammatical Error Correction with mT5},
  author={Tukenov, Saken},
  year={2026},
  url={https://huggingface.co/saken-tukenov/kazakh-gec-mt5-small-run3-finetune-v2}
}

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