t5_translate_en_ru_zh_large_1024_v2

106
36
3 languages
license:apache-2.0
by
utrobinmv
Language Model
OTHER
1024B params
New
106 downloads
Early-stage
Try on Hugging FaceAdd to Compare
Edge AI:
Mobile
Laptop
Server
2289GB+ RAM
Mobile
Laptop
Server
Quick Summary

T5 English, Russian and Chinese multilingual machine translation This model represents a conventional T5 transformer in multitasking mode for translation into...

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
954GB+ RAM

Training Data Analysis

🔵 Good (6.0/10)

Researched training datasets used by t5_translate_en_ru_zh_large_1024_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

translate Russian to Chinesepythontransformers
from transformers import T5ForConditionalGeneration, T5Tokenizer

device = 'cuda' #or 'cpu' for translate on cpu

model_name = 'utrobinmv/t5_translate_en_ru_zh_large_1024_v2'
model = T5ForConditionalGeneration.from_pretrained(model_name)
model.eval()
model.to(device)
tokenizer = T5Tokenizer.from_pretrained(model_name)

prefix = 'translate to zh: '
src_text = prefix + "Съешь ещё этих мягких французских булок."

# translate Russian to Chinese
input_ids = tokenizer(src_text, return_tensors="pt")

generated_tokens = model.generate(**input_ids.to(device))

result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
print(result)
# 再吃这些法国的甜蜜的面包。
translate Russian to Chinesepythontransformers
from transformers import T5ForConditionalGeneration, T5Tokenizer

device = 'cuda' #or 'cpu' for translate on cpu

model_name = 'utrobinmv/t5_translate_en_ru_zh_large_1024_v2'
model = T5ForConditionalGeneration.from_pretrained(model_name)
model.eval()
model.to(device)
tokenizer = T5Tokenizer.from_pretrained(model_name)

prefix = 'translate to zh: '
src_text = prefix + "Съешь ещё этих мягких французских булок."

# translate Russian to Chinese
input_ids = tokenizer(src_text, return_tensors="pt")

generated_tokens = model.generate(**input_ids.to(device))

result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
print(result)
# 再吃这些法国的甜蜜的面包。

Deploy This Model

Production-ready deployment in minutes

Together.ai

Instant API access to this model

Fastest API

Production-ready inference API. Start free, scale to millions.

Try Free API

Replicate

One-click model deployment

Easiest Setup

Run models in the cloud with simple API. No DevOps required.

Deploy Now

Disclosure: We may earn a commission from these partners. This helps keep LLMYourWay free.