kazakh-gec-mt5-base-run9-grammar-balanced-v2

25
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by
saken-tukenov
Other
OTHER
New
25 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 kazakh-gec-mt5-base-run9-grammar-balanced-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

Explore our comprehensive training dataset analysis

View All Datasets

Code Examples

Usagepythontransformers
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("saken-tukenov/kazakh-gec-mt5-base-run9-grammar-balanced-v2")
model = AutoModelForSeq2SeqLM.from_pretrained("saken-tukenov/kazakh-gec-mt5-base-run9-grammar-balanced-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))

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