T5-REF-CORRUPT-EN

52
—
by
elizaveta-dev
Language Model
OTHER
New
52 downloads
Early-stage
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Edge AI:
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Quick Summary

AI model with specialized capabilities.

Training Data Analysis

🔵 Good (6.0/10)

Researched training datasets used by T5-REF-CORRUPT-EN 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

Examplepythontransformers
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_id = "elizaveta-dev/T5-REF-CORRUPT-EN"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(model_id)

text = "According to Smith & Peterson 2016 56, the translation reveals patterns that suggest underlying semantic shifts."
inputs = tokenizer(text, return_tensors="pt")

outputs = model.generate(**inputs, max_new_tokens=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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