translation-t5-small-standard-bahasa-cased-v2

33.1K
1
1 language
—
by
mesolitica
Language Model
OTHER
Fair
33K downloads
Community-tested
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Quick Summary

Trained on 1536 context length, able to translate malay, pasar malay (social media texts or local context), english, manglish, javanese, banjarese and indonesian to target language.

Training Data Analysis

🔵 Good (6.0/10)

Researched training datasets used by translation-t5-small-standard-bahasa-cased-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

how-topythontransformers
from transformers import T5ForConditionalGeneration, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
    'mesolitica/translation-t5-small-standard-bahasa-cased-v2',
    use_fast=False
)
model = T5ForConditionalGeneration.from_pretrained(
    'mesolitica/translation-t5-small-standard-bahasa-cased-v2'
)
s = 'Hai, ada yang bisa saya bantu?'
input_ids = tokenizer.encode(f'terjemah ke Melayu: {s}', return_tensors = 'pt')
outputs = model.generate(input_ids, max_length = 100)
all_special_ids = [0, 1, 2]
outputs = [i for i in outputs[0] if i not in all_special_ids]
print(tokenizer.decode(outputs, spaces_between_special_tokens = False))
how-topythontransformers
from transformers import T5ForConditionalGeneration, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
    'mesolitica/translation-t5-small-standard-bahasa-cased-v2',
    use_fast=False
)
model = T5ForConditionalGeneration.from_pretrained(
    'mesolitica/translation-t5-small-standard-bahasa-cased-v2'
)
s = 'Hai, ada yang bisa saya bantu?'
input_ids = tokenizer.encode(f'terjemah ke Melayu: {s}', return_tensors = 'pt')
outputs = model.generate(input_ids, max_length = 100)
all_special_ids = [0, 1, 2]
outputs = [i for i in outputs[0] if i not in all_special_ids]
print(tokenizer.decode(outputs, spaces_between_special_tokens = False))
how-topythontransformers
from transformers import T5ForConditionalGeneration, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
    'mesolitica/translation-t5-small-standard-bahasa-cased-v2',
    use_fast=False
)
model = T5ForConditionalGeneration.from_pretrained(
    'mesolitica/translation-t5-small-standard-bahasa-cased-v2'
)
s = 'Hai, ada yang bisa saya bantu?'
input_ids = tokenizer.encode(f'terjemah ke Melayu: {s}', return_tensors = 'pt')
outputs = model.generate(input_ids, max_length = 100)
all_special_ids = [0, 1, 2]
outputs = [i for i in outputs[0] if i not in all_special_ids]
print(tokenizer.decode(outputs, spaces_between_special_tokens = False))
how-topythontransformers
from transformers import T5ForConditionalGeneration, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
    'mesolitica/translation-t5-small-standard-bahasa-cased-v2',
    use_fast=False
)
model = T5ForConditionalGeneration.from_pretrained(
    'mesolitica/translation-t5-small-standard-bahasa-cased-v2'
)
s = 'Hai, ada yang bisa saya bantu?'
input_ids = tokenizer.encode(f'terjemah ke Melayu: {s}', return_tensors = 'pt')
outputs = model.generate(input_ids, max_length = 100)
all_special_ids = [0, 1, 2]
outputs = [i for i in outputs[0] if i not in all_special_ids]
print(tokenizer.decode(outputs, spaces_between_special_tokens = False))
how-topythontransformers
from transformers import T5ForConditionalGeneration, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
    'mesolitica/translation-t5-small-standard-bahasa-cased-v2',
    use_fast=False
)
model = T5ForConditionalGeneration.from_pretrained(
    'mesolitica/translation-t5-small-standard-bahasa-cased-v2'
)
s = 'Hai, ada yang bisa saya bantu?'
input_ids = tokenizer.encode(f'terjemah ke Melayu: {s}', return_tensors = 'pt')
outputs = model.generate(input_ids, max_length = 100)
all_special_ids = [0, 1, 2]
outputs = [i for i in outputs[0] if i not in all_special_ids]
print(tokenizer.decode(outputs, spaces_between_special_tokens = False))
how-topythontransformers
from transformers import T5ForConditionalGeneration, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
    'mesolitica/translation-t5-small-standard-bahasa-cased-v2',
    use_fast=False
)
model = T5ForConditionalGeneration.from_pretrained(
    'mesolitica/translation-t5-small-standard-bahasa-cased-v2'
)
s = 'Hai, ada yang bisa saya bantu?'
input_ids = tokenizer.encode(f'terjemah ke Melayu: {s}', return_tensors = 'pt')
outputs = model.generate(input_ids, max_length = 100)
all_special_ids = [0, 1, 2]
outputs = [i for i in outputs[0] if i not in all_special_ids]
print(tokenizer.decode(outputs, spaces_between_special_tokens = False))
how-topythontransformers
from transformers import T5ForConditionalGeneration, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
    'mesolitica/translation-t5-small-standard-bahasa-cased-v2',
    use_fast=False
)
model = T5ForConditionalGeneration.from_pretrained(
    'mesolitica/translation-t5-small-standard-bahasa-cased-v2'
)
s = 'Hai, ada yang bisa saya bantu?'
input_ids = tokenizer.encode(f'terjemah ke Melayu: {s}', return_tensors = 'pt')
outputs = model.generate(input_ids, max_length = 100)
all_special_ids = [0, 1, 2]
outputs = [i for i in outputs[0] if i not in all_special_ids]
print(tokenizer.decode(outputs, spaces_between_special_tokens = False))
how-topythontransformers
from transformers import T5ForConditionalGeneration, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
    'mesolitica/translation-t5-small-standard-bahasa-cased-v2',
    use_fast=False
)
model = T5ForConditionalGeneration.from_pretrained(
    'mesolitica/translation-t5-small-standard-bahasa-cased-v2'
)
s = 'Hai, ada yang bisa saya bantu?'
input_ids = tokenizer.encode(f'terjemah ke Melayu: {s}', return_tensors = 'pt')
outputs = model.generate(input_ids, max_length = 100)
all_special_ids = [0, 1, 2]
outputs = [i for i in outputs[0] if i not in all_special_ids]
print(tokenizer.decode(outputs, spaces_between_special_tokens = False))

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