Mlong T5 Tglobal Large

40
7
102 languages
license:apache-2.0
by
agemagician
Language Model
OTHER
New
40 downloads
Early-stage
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Quick Summary

MLongT5 (transient-global attention, large-sized model) MLongT5 model pre-trained on Multi-language corpus.

Training Data Analysis

🔵 Good (6.0/10)

Researched training datasets used by Mlong T5 Tglobal Large 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

S-Denoisingpythontransformers
from transformers import LongT5ForConditionalGeneration, T5Tokenizer
import torch

model = LongT5ForConditionalGeneration.from_pretrained("agemagician/mlong-t5-tglobal-large", low_cpu_mem_usage=True, torch_dtype=torch.bfloat16).to("cuda")                                                                                                   
tokenizer = T5Tokenizer.from_pretrained("agemagician/mlong-t5-tglobal-large")

input_string = "[S2S] Mr. Dursley was the director of a firm called Grunnings, which made drills. He was a big, solid man with a bald head. Mrs. Dursley was thin and blonde and more than the usual amount of neck, which came in very useful as she spent so much of her time craning over garden fences, spying on the neighbours. The Dursleys had a small son called Dudley and in their opinion there was no finer boy anywhere <extra_id_0>"                                               

inputs = tokenizer(input_string, return_tensors="pt").input_ids.to("cuda")

outputs = model.generate(inputs, max_length=200)

print(tokenizer.decode(outputs[0]))
S-Denoisingpythontransformers
from transformers import LongT5ForConditionalGeneration, T5Tokenizer
import torch

model = LongT5ForConditionalGeneration.from_pretrained("agemagician/mlong-t5-tglobal-large", low_cpu_mem_usage=True, torch_dtype=torch.bfloat16).to("cuda")                                                                                                   
tokenizer = T5Tokenizer.from_pretrained("agemagician/mlong-t5-tglobal-large")

input_string = "[S2S] Mr. Dursley was the director of a firm called Grunnings, which made drills. He was a big, solid man with a bald head. Mrs. Dursley was thin and blonde and more than the usual amount of neck, which came in very useful as she spent so much of her time craning over garden fences, spying on the neighbours. The Dursleys had a small son called Dudley and in their opinion there was no finer boy anywhere <extra_id_0>"                                               

inputs = tokenizer(input_string, return_tensors="pt").input_ids.to("cuda")

outputs = model.generate(inputs, max_length=200)

print(tokenizer.decode(outputs[0]))
S-Denoisingpythontransformers
from transformers import LongT5ForConditionalGeneration, T5Tokenizer
import torch

model = LongT5ForConditionalGeneration.from_pretrained("agemagician/mlong-t5-tglobal-large", low_cpu_mem_usage=True, torch_dtype=torch.bfloat16).to("cuda")                                                                                                   
tokenizer = T5Tokenizer.from_pretrained("agemagician/mlong-t5-tglobal-large")

input_string = "[S2S] Mr. Dursley was the director of a firm called Grunnings, which made drills. He was a big, solid man with a bald head. Mrs. Dursley was thin and blonde and more than the usual amount of neck, which came in very useful as she spent so much of her time craning over garden fences, spying on the neighbours. The Dursleys had a small son called Dudley and in their opinion there was no finer boy anywhere <extra_id_0>"                                               

inputs = tokenizer(input_string, return_tensors="pt").input_ids.to("cuda")

outputs = model.generate(inputs, max_length=200)

print(tokenizer.decode(outputs[0]))
S-Denoisingpythontransformers
from transformers import LongT5ForConditionalGeneration, T5Tokenizer
import torch

model = LongT5ForConditionalGeneration.from_pretrained("agemagician/mlong-t5-tglobal-large", low_cpu_mem_usage=True, torch_dtype=torch.bfloat16).to("cuda")                                                                                                   
tokenizer = T5Tokenizer.from_pretrained("agemagician/mlong-t5-tglobal-large")

input_string = "[S2S] Mr. Dursley was the director of a firm called Grunnings, which made drills. He was a big, solid man with a bald head. Mrs. Dursley was thin and blonde and more than the usual amount of neck, which came in very useful as she spent so much of her time craning over garden fences, spying on the neighbours. The Dursleys had a small son called Dudley and in their opinion there was no finer boy anywhere <extra_id_0>"                                               

inputs = tokenizer(input_string, return_tensors="pt").input_ids.to("cuda")

outputs = model.generate(inputs, max_length=200)

print(tokenizer.decode(outputs[0]))
S-Denoisingpythontransformers
from transformers import LongT5ForConditionalGeneration, T5Tokenizer
import torch

model = LongT5ForConditionalGeneration.from_pretrained("agemagician/mlong-t5-tglobal-large", low_cpu_mem_usage=True, torch_dtype=torch.bfloat16).to("cuda")                                                                                                   
tokenizer = T5Tokenizer.from_pretrained("agemagician/mlong-t5-tglobal-large")

input_string = "[S2S] Mr. Dursley was the director of a firm called Grunnings, which made drills. He was a big, solid man with a bald head. Mrs. Dursley was thin and blonde and more than the usual amount of neck, which came in very useful as she spent so much of her time craning over garden fences, spying on the neighbours. The Dursleys had a small son called Dudley and in their opinion there was no finer boy anywhere <extra_id_0>"                                               

inputs = tokenizer(input_string, return_tensors="pt").input_ids.to("cuda")

outputs = model.generate(inputs, max_length=200)

print(tokenizer.decode(outputs[0]))
S-Denoisingpythontransformers
from transformers import LongT5ForConditionalGeneration, T5Tokenizer
import torch

model = LongT5ForConditionalGeneration.from_pretrained("agemagician/mlong-t5-tglobal-large", low_cpu_mem_usage=True, torch_dtype=torch.bfloat16).to("cuda")                                                                                                   
tokenizer = T5Tokenizer.from_pretrained("agemagician/mlong-t5-tglobal-large")

input_string = "[S2S] Mr. Dursley was the director of a firm called Grunnings, which made drills. He was a big, solid man with a bald head. Mrs. Dursley was thin and blonde and more than the usual amount of neck, which came in very useful as she spent so much of her time craning over garden fences, spying on the neighbours. The Dursleys had a small son called Dudley and in their opinion there was no finer boy anywhere <extra_id_0>"                                               

inputs = tokenizer(input_string, return_tensors="pt").input_ids.to("cuda")

outputs = model.generate(inputs, max_length=200)

print(tokenizer.decode(outputs[0]))
S-Denoisingpythontransformers
from transformers import LongT5ForConditionalGeneration, T5Tokenizer
import torch

model = LongT5ForConditionalGeneration.from_pretrained("agemagician/mlong-t5-tglobal-large", low_cpu_mem_usage=True, torch_dtype=torch.bfloat16).to("cuda")                                                                                                   
tokenizer = T5Tokenizer.from_pretrained("agemagician/mlong-t5-tglobal-large")

input_string = "[S2S] Mr. Dursley was the director of a firm called Grunnings, which made drills. He was a big, solid man with a bald head. Mrs. Dursley was thin and blonde and more than the usual amount of neck, which came in very useful as she spent so much of her time craning over garden fences, spying on the neighbours. The Dursleys had a small son called Dudley and in their opinion there was no finer boy anywhere <extra_id_0>"                                               

inputs = tokenizer(input_string, return_tensors="pt").input_ids.to("cuda")

outputs = model.generate(inputs, max_length=200)

print(tokenizer.decode(outputs[0]))
S-Denoisingpythontransformers
from transformers import LongT5ForConditionalGeneration, T5Tokenizer
import torch

model = LongT5ForConditionalGeneration.from_pretrained("agemagician/mlong-t5-tglobal-large", low_cpu_mem_usage=True, torch_dtype=torch.bfloat16).to("cuda")                                                                                                   
tokenizer = T5Tokenizer.from_pretrained("agemagician/mlong-t5-tglobal-large")

input_string = "[S2S] Mr. Dursley was the director of a firm called Grunnings, which made drills. He was a big, solid man with a bald head. Mrs. Dursley was thin and blonde and more than the usual amount of neck, which came in very useful as she spent so much of her time craning over garden fences, spying on the neighbours. The Dursleys had a small son called Dudley and in their opinion there was no finer boy anywhere <extra_id_0>"                                               

inputs = tokenizer(input_string, return_tensors="pt").input_ids.to("cuda")

outputs = model.generate(inputs, max_length=200)

print(tokenizer.decode(outputs[0]))

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